WO2019119223A1 - 基于雷达的测距处理方法、装置及无人飞行器 - Google Patents
基于雷达的测距处理方法、装置及无人飞行器 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—Extracting wanted echo-signals
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
- G01S13/08—Systems for measuring distance only
- G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
- G01S13/08—Systems for measuring distance only
- G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
- G01S13/34—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/933—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
- G01S13/935—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft for terrain-avoidance
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/356—Receivers involving particularities of FFT processing
Definitions
- the invention relates to signal processing technology, in particular to a radar-based ranging processing method and device and an unmanned aerial vehicle.
- the continuous wave radar ranging algorithm is usually implemented in a software manner by using a central processing unit (CPU). Since the CPU can only process data in a serial manner, that is, only one can be processed at a time. Data, which makes the algorithm's processing delay longer when using the CPU to implement the radar ranging algorithm, far exceeding the delay of the radar backhaul data. Therefore, when the radar is used to implement the radar ranging algorithm, the radar can not be processed in real time. Passing data, resulting in low accuracy of radar ranging values. Therefore, how to effectively improve the real-time performance of the radar ranging algorithm has become an urgent technical problem.
- CPU central processing unit
- a first aspect of the present invention provides a radar-based ranging processing method, including:
- the method for obtaining the detection value of each constant false alarm is:
- a radar-based ranging processing apparatus including: a memory and a processor;
- memory is used to store program instructions
- the processor is configured to invoke the program instructions stored in the memory to implement:
- the method for obtaining the detection value of each constant false alarm is:
- Still another aspect of the present invention provides an unmanned aerial vehicle comprising: a fuselage, an arm extending from the fuselage, and a power assembly mounted on the arm, wherein the unmanned aerial vehicle Also included is a radar, and a device as described above, the radar and the device being disposed on the fuselage.
- the radar-based ranging processing method and device and the unmanned aerial vehicle provided by the embodiments of the present invention acquire the input spectral amplitude data according to the difference frequency signal by acquiring the difference frequency signal of the radar; and acquire each of the input spectral amplitude data according to the parallel processing manner.
- the method for obtaining the detection value of each constant false alarm is: acquiring a sequence of adjacent values corresponding to the spectrum amplitude, and sorting the adjacent value sequences by using a pre-configured parallel pipeline sorting algorithm, and acquiring the spectrum according to the sorted adjacent value sequence. Width The corresponding false alarm detection value.
- the processing efficiency is improved, the processing delay is reduced, and the accuracy of radar ranging is improved.
- FIG. 1 is a schematic flow chart of a radar-based ranging processing method according to an embodiment of the present invention
- 2A is a schematic diagram of an even number of neighboring value sorting algorithms according to an embodiment of the present invention.
- 2B is a schematic diagram of an odd number of neighboring value sorting algorithms according to an embodiment of the present invention.
- FIG. 3 is a schematic diagram of acquiring a sequence of neighboring values according to an embodiment of the present invention.
- FIG. 4 is a schematic diagram of an output data packet according to an embodiment of the present invention.
- FIG. 5 is a schematic flowchart of a spectrum refinement process according to the embodiment.
- FIG. 6 is a schematic structural diagram of a radar-based ranging processing apparatus according to an embodiment of the present invention.
- FIG. 7 is a schematic structural diagram of a radar-based ranging processing apparatus according to another embodiment of the present invention.
- FIG. 8 is a schematic structural diagram of an unmanned aerial vehicle according to an embodiment of the present invention.
- Embodiments of the present invention provide a radar-based ranging processing method, apparatus, and unmanned aerial vehicle.
- the basic principle of radar ranging is: using a certain modulation method to modulate the carrier frequency signal transmitted by the radar, and using the modulated carrier frequency signal as the transmitting signal and the local oscillator signal. After receiving the target echo, the radar firstly echoes the echo. The signal and the local oscillator signal are mixed, filtered and amplified to obtain the difference frequency signal, and then the frequency domain analysis is performed. The distance between the radar and the obstacle can be obtained by using the corresponding relationship between the frequency shift of the echo signal and the delay. Among them, the radar can be a continuous wave radar. Embodiments of the invention are not limited thereto.
- FIG. 1 is a schematic flowchart of a radar-based ranging processing method according to an embodiment of the present invention.
- the embodiment provides a radar-based ranging processing method for determining a distance between a radar and an obstacle, and is a subsequent operation. Provide evidence.
- the method in this embodiment may include:
- Step 101 Acquire a difference frequency signal of the radar.
- the object to be processed in this embodiment is a difference frequency signal obtained by mixing the transmitted signal of the radar front end of the radar and the received signal (that is, the echo signal).
- the difference frequency signal may be a difference frequency signal obtained after a certain filtering and amplification after mixing.
- Step 102 Acquire input spectral amplitude data according to the difference frequency signal.
- the input spectrum amplitude data can be acquired according to the difference frequency signal.
- the input spectral amplitude data includes a plurality of spectral amplitudes (also referred to as spectral amplitude values), and specifically includes the number of spectral amplitudes, which can be set according to actual requirements in the process of acquiring input spectral amplitude data according to the difference frequency signals.
- the specific process of acquiring the input spectrum amplitude data according to the difference frequency signal may be a mode in the prior art, which is not limited herein.
- spectrum extraction of the difference frequency signal is performed to obtain input spectral amplitude data.
- Step 103 Acquire, according to the parallel processing manner, a constant false alarm detection value corresponding to each spectral amplitude in the input spectral amplitude data.
- the method for obtaining the detection value of each constant false alarm is:
- the parallel false processing method may be used to acquire the constant false alarm detection corresponding to each spectral amplitude in parallel. value.
- the constant false alarm detection value corresponding to each spectral amplitude can be obtained in parallel based on a processor having parallel processing characteristics, such as FPGA-based parallel characteristics, combined with FPGA hardware implementation.
- a processor having parallel processing characteristics such as FPGA-based parallel characteristics, combined with FPGA hardware implementation.
- the embodiment is not limited thereto, and may be another processor having a parallel processing function.
- the constant false alarm detection value corresponding to each spectral amplitude in the input spectral amplitude data is obtained based on the parallel processing manner, which can effectively improve the data processing efficiency, reduce the delay of the radar ranging, and thereby improve the accuracy of the radar ranging. .
- the method for obtaining the detection value of each constant false alarm may be: acquiring a sequence of adjacent values corresponding to the spectrum amplitude, and simultaneously arranging N adjacent values in the sequence of adjacent values. In sequence, the sequence of adjacent values is sorted by N times, and the constant false alarm detection value corresponding to the spectrum amplitude is obtained according to the sequence of adjacent values after sorting.
- the sequence of adjacent values corresponding to the spectrum amplitude may be obtained, and a pre-configured parallel pipeline sorting algorithm is used to sort the adjacent value sequences, and the constant false alarm detection values corresponding to the spectral amplitudes are obtained according to the sequenced adjacent value sequences.
- the sequence of adjacent values corresponding to the spectrum amplitude is: a sequence consisting of N spectral amplitudes adjacent to the spectrum amplitude in the input spectral amplitude data.
- Each spectral amplitude corresponds to a sequence of adjacent values. After acquiring the sequence of adjacent values corresponding to each spectrum amplitude, it is necessary to sort the N spectral amplitudes in each adjacent value sequence.
- a pre-configured parallel pipeline sorting algorithm may be adopted, that is, N neighboring values in the adjacent value sequence are simultaneously sorted, and the sorting of adjacent value sequences is completed at most N times.
- the pipeline means that the sorting process can be divided into multiple stages. Parallel means that for each stage, adjacent two adjacent values in the adjacent value sequence can be compared and sorted in parallel.
- the ordering process of the adjacent value sequence [A, B, C, D] composed of four adjacent values can be divided into four stages.
- A is performed in parallel. Compare and sort with B, C and D, such as ascending order, assuming that A is greater than B, C is greater than D, then the first stage sorting result is [B, A, D, C], with the sequence [B, A, D, C] Sorts the input into the next stage.
- N adjacent values in the sequence of adjacent values are simultaneously sorted, and the sequence of adjacent values is sorted at most N times.
- the processing delay is effectively reduced, and the accuracy of the radar ranging is further improved.
- each adjacent value sequence may be processed in parallel, that is, each adjacent value sequence may be at the same time. Sort processing. It can effectively reduce the processing delay.
- the constant false alarm detection value corresponding to the corresponding spectral amplitude may be obtained according to the sorted adjacent value sequence.
- the specific process of obtaining the constant false alarm detection value corresponding to the corresponding spectral amplitude according to the sequence of the adjacent values may be the prior art, which is not limited herein.
- the Pth critical value is selected from the sequence of adjacent values, and the constant false alarm detection value corresponding to the spectrum amplitude is calculated according to a specific calculation formula.
- Step 104 Search for the target frequency point according to each spectral amplitude and the constant false alarm detection value corresponding to each spectral amplitude.
- the target frequency point may be searched for.
- the specific method can be obtained according to the existing method, and is not limited herein.
- the spectrum amplitude peak search method is used, and the frequency point number corresponding to the maximum spectrum amplitude is obtained as the target frequency point. This embodiment is not limited to this.
- Step 105 Acquire a distance value between the radar and the obstacle according to the target frequency point.
- the distance value between the radar and the obstacle can be obtained according to the target frequency point.
- the specific method can be obtained according to the existing method, and is not limited herein.
- a constant false alarm detection value corresponding to each spectral amplitude in the input spectral amplitude data is acquired, and the acquisition target is searched according to each spectral amplitude and a constant false alarm detection value corresponding to each spectral amplitude.
- the pre-configured parallel pipeline sorting algorithm is used to sort the adjacent value sequences, and according to the sorted adjacent value sequence, the constant false alarm detection value corresponding to the spectrum amplitude is obtained, thereby further reducing the processing delay and improving the radar ranging. The accuracy.
- the N adjacent values in the sequence of adjacent values are simultaneously sorted by two or two, and may specifically include:
- first neighboring value in the adjacent value sequence [A, B, C, D] with the second neighboring value, the third neighboring value, and the fourth neighboring value, that is, A and B, C and D, assuming that A is greater than B, C is greater than D, sorted in ascending order, then after comparison, A and B exchange positions, C and D exchange positions, and obtain the first adjacent value sequence [B, A, D, C].
- the ordering of the sequence of adjacent values is performed at most N times, which may specifically include:
- the first adjacent value sequence is sent to the next stage for sorting, and the N in the first adjacent value sequence is The adjacent values are simultaneously sorted to obtain the second adjacent value sequence; this step is repeated until the Nth neighboring value sequence is obtained.
- the N adjacent values in the sequence of adjacent values are simultaneously sorted, and the ordering of the adjacent value sequences is performed at most N times.
- the N adjacent values in the adjacent value sequence are simultaneously compared at the same time; for each of the two adjacent values to be compared, if two adjacent values are determined according to the comparison result and the pre-configured sorting manner, the exchange position is required. Then, the two adjacent values are exchanged for the first adjacent value sequence, and the first adjacent value sequence is sent to the second phase;
- the N adjacent values in the sequence of j-1 neighboring values are simultaneously compared two by two; for each two adjacent values to be compared, two adjacent values are determined according to the comparison result and the pre-configured sorting manner. If the location needs to be exchanged, the two adjacent values are exchanged to obtain the sequence of the jth neighboring value, and the sequence of the jth adjacent value is sent to the j+1th phase; the j is incremented by 1, and the step is repeated until the Nth neighbor is obtained.
- a sequence of values wherein N, j are integers, and j is greater than or equal to 2, and j is less than or equal to N.
- the sorting process of the adjacent value sequence is divided into N stages, and each time a stage is sorted, The results obtained are sent to the next stage.
- the above N adjacent values are compared at the same time as follows:
- the i-th neighboring value X(i) and the i+1th neighboring value X(i+1), i of the N adjacent values are equal to 1, 3, ..., N-1, respectively.
- the i-th neighboring value X(i) and the i+1th neighboring value X(i+1), i of the N neighboring values are equal to 2, 4, ..., N-2, respectively.
- the i-th adjacent value X(i) and the i+1th adjacent value X(i+1), i of the N adjacent values are equal to 1, 3, ..., N-2, respectively.
- the i-th neighboring value X(i) and the i+1th neighboring value X(i+1), i of the N adjacent values are equal to 2, 4, ..., N-1, respectively.
- t is an integer and t is less than or equal to N.
- FIG. 2A is a schematic diagram of an even number of neighboring value sorting algorithms according to an embodiment of the present invention.
- FIG. 2B is a schematic diagram of an odd number of neighboring value sorting algorithms according to an embodiment of the present invention.
- the sorting process is four stages, as follows:
- the first adjacent value in the adjacent value sequence [A, B, C, D] is compared with the second adjacent value, the third adjacent value and the fourth adjacent value, that is, A and B, C and D, if A is greater than B, C is greater than D, and the pre-configured sorting mode is ascending order, then after comparison, A and B exchange positions, C and D exchange positions, and obtain the first adjacent value sequence [B, A, D, C]; the first adjacent value sequence [B, A, D, C] is sent to the second stage
- the second adjacent value in the second adjacent value sequence is compared with the third adjacent value, that is, A and D, if A is greater than D, then compare the position of A and D, and obtain the second adjacent value sequence [B, D, A, C];
- the first adjacent value in the third adjacent value sequence is compared with the second adjacent value, the third adjacent value, and the fourth neighboring value.
- the values are compared, that is, B and D, A and C. If B is greater than D and A is greater than C, the position is exchanged after comparison, and the third adjacent value sequence [D, B, C, A] is obtained.
- the second adjacent value in the fourth adjacent value sequence is compared with the third adjacent value, that is, B and C, if B If it is greater than C, the position of B and C is exchanged, and the fourth adjacent value sequence [D, C, B, A] is obtained. Then, the fourth adjacent value sequence [D, C, B, A] is the sequence of adjacent values after sorting.
- the number of adjacent value sequences is an odd number
- the last adjacent value does not participate in the comparison, and directly enters the next stage.
- the first adjacent value does not participate in the comparison and directly enters the next stage.
- the specific process is similar to the number of adjacent values, and will not be described here.
- the sorting process of the adjacent value sequence is a part with a large amount of computation and a long time; in order to reduce the delay, the parallel pipeline structure-based sorting algorithm structure using the above process, for example, can be parallelized by FPGA.
- Processing characteristics and pipeline structure make the sorting calculation time in the constant false alarm detection process greatly reduced, and the sorting algorithm of each set of N data is divided into N stages, each stage At the same time, the N/2 or N/2-1 data size comparison is completed and the data is exchanged according to the size of the data (in ascending or descending order), and then the processed data is sent to the next pipeline through the structure of the pipeline.
- the processing of the stage which makes the sorting algorithm of each set of N data only need N cycles to complete.
- the obtaining the constant false alarm detection value corresponding to the spectrum amplitude according to the sequence of the adjacent values in the sequence may include:
- the constant false alarm detection value corresponding to the spectral amplitude is obtained.
- the first preset threshold P can be set according to actual experience.
- the pre-configured constant false alarm probability value Pf is set according to the actual situation of the radar system.
- the obtaining the sequence of the adjacent values corresponding to the spectrum amplitude may include: selecting, from the spectrum amplitude data, N spectral amplitudes adjacent to the spectral amplitude, and forming the spectral amplitudes corresponding to the N spectral amplitudes. Sequence of values.
- the acquisition rule of the adjacent value sequence may be: selecting N/2 spectral amplitudes in front of the current spectral amplitude, and then selecting N/2 spectral amplitudes after the current spectral amplitude (the closest to the current spectral amplitude is needed to be selected when selecting Before and after each U data), if there is no N/2 spectral amplitudes before or after the current spectrum amplitude, then take some neighboring values from the other side to ensure that the total number of neighboring values is N.
- the N data form a sequence of neighboring values.
- FIG. 3 is a schematic diagram of acquiring a sequence of neighboring values according to an embodiment of the present invention.
- the number of spectral amplitudes in the input spectral amplitude data is 16, U is 1, The number of adjacent values included in each adjacent value sequence is 6.
- 16 spectral amplitudes 16 corresponding sequences of neighboring values are obtained.
- the above step 102 may specifically include step 1021 and step 1022.
- Step 1021 Acquire windowed data according to the difference frequency signal.
- Step 1022 Acquire input spectral amplitude data according to the windowed data.
- the foregoing step 1022 may specifically include:
- Step 10221 Perform Fourier transform on the windowed data to obtain transformed data.
- Step 10222 Acquire input spectral amplitude data according to the transformed data.
- the input spectral amplitude squared value data may also be used as input spectral amplitude data for subsequent processing. Specifically, according to the transformed data, the squared value data of the input spectral amplitude is calculated, and the squared value data of the input spectral amplitude is used as the input spectral amplitude data. That is, after obtaining the transformed data, the input spectral amplitude squared value data can be calculated without starting the operation.
- the spectrum extraction in this embodiment only needs to obtain the squared value of the spectrum amplitude of the complex spectrum, and does not need to perform the square operation.
- the squared value of the spectrum amplitude needs to be calculated first, and then The open operation, and the open operation requires more computing resources and time. Therefore, this embodiment does not need to perform the open operation, which effectively improves the efficiency of spectrum extraction, thereby improving the real-time and accuracy of radar ranging. .
- the squared value of the spectral amplitude as the basis for further extracting the frequency information, it is possible to achieve a very close effect with the existing square root operation, and at the same time reduce the amount of calculation.
- the foregoing step 1021 may further include:
- Step 10211 Acquire an extracted output data packet according to the difference frequency signal.
- the implementation can be implemented by using the prior art, which is not limited herein.
- Step 10212 Perform window processing on the output data packet to obtain windowed data.
- the windowing process is performed on the output data packet to obtain the specific operation of the windowed data, which may be an operation mode in the prior art, which is not limited herein.
- the Hanning window may be used to window the output data packet to obtain the windowed data.
- step 10211 may further include: using a predetermined format, the difference frequency The signal is processed to obtain a corresponding output data packet; wherein, the output data packet includes: a synchronization flag signal, Y data points, and a number of cycles of the data points.
- the output data includes the following: the synchronization identification signal, the Y data points, and each The number of cycles the data point lasts.
- Each output packet is a set of data.
- the number of spectral amplitudes included in the spectral amplitude data obtained above is the number Y of data points in a set of data.
- FIG. 4 is a schematic diagram of an output data packet according to an embodiment of the present invention.
- head_sync represents a synchronization flag signal
- data represents a beat signal
- M represents the number of cycles in which a data point continues
- Y represents the number of data points of a group of data.
- the specific set value of the number of consecutive periods of the data point and the number of data points of a set of data can be specifically adjusted according to the user's request; the synchronous flag signal is valid in advance (ie, Y data points) for one clock cycle, and the duration is one clock cycle. Used to mark the starting position of a new set of data so that subsequent processing knows when to start processing a new set of data.
- the difference frequency signal is processed in a predetermined format, so that the processor can support the data processing function of the random burst, and can process not only the radar ranging data of the continuous regular period but also the irregular period of the random burst. Radar ranging data.
- step 10212 specifically, the method may include:
- Step 102121 traversing the Y data points to obtain the maximum value and the minimum value.
- Step 102122 determining a fluctuation range R1 corresponding to the Y data points according to the maximum value and the minimum value.
- the difference between the maximum value and the minimum value is the fluctuation range R1 corresponding to the Y data points.
- Step 102123 determining the dynamic adjustment factor R2/R1 according to the fluctuation range R1 and the pre-configured fluctuation range R2.
- the pre-configured fluctuation range R2 can be configured according to the actual situation of the radar system.
- Step 102124 Determine a final window function value according to the initially configured window function and the dynamic adjustment factor, and perform windowing processing on the Y data points according to the window function value to obtain the windowed data.
- the start of the new set of data is identified according to the synchronization flag signal in the output data packet, the Y data points after the synchronization flag signal are acquired, and the Y data points are windowed.
- Windowing is to multiply the input data (that is, Y data points) by the window function, so the windowing operation will make the number
- the embodiment provides a mechanism for dynamically adjusting the window function ratio according to the fluctuation range of the signal, so that the windowed signal does not overflow and the dynamic range of the signal is ensured.
- the product of the initially configured window function and the dynamic adjustment factor is used as the final window function value, and the input Y data points are multiplied by the window function value to implement windowing processing to obtain windowed data.
- the target frequency point obtained in step 104 may be subjected to spectrum refinement processing, specifically: performing spectrum refinement processing on the target frequency point, and acquiring the first target frequency point according to the refinement processing result, and The first target frequency point is used as the target frequency point.
- the method may include: acquiring the windowed data; performing spectral thinning processing on the target frequency point according to the windowed data, and acquiring the first target frequency point according to the refined processing result.
- FIG. 5 is a schematic flowchart of a spectrum refinement process provided by this embodiment. As shown in FIG. 5, the foregoing step 105 may further include:
- Step 1051 Perform frequency shift processing on the target frequency point to shift to zero frequency, and obtain the frequency shifted data.
- the digital control oscillator can be used to generate a complex signal with a frequency of the target frequency, and the orthogonal complex signal is multiplied by the windowed data to move the target frequency in the windowed data to Zero frequency, get the data after frequency shift. It can be seen that this step needs to obtain the above windowed data as an object to be processed.
- Step 1052 Perform low-pass filtering processing on the frequency-shifted data according to the pre-configured scaling factor to obtain the filtered data.
- the Cache Register is cleared to eliminate the effects between the two sets of data.
- Step 1053 Perform data extraction processing on the filtered data according to the pre-configured scaling factor to obtain the extracted data.
- Step 1054 Perform spectrum extraction processing on the extracted data to obtain first spectrum amplitude data.
- Step 1055 Perform peak search processing on the first spectrum amplitude data to obtain a first target frequency point.
- the first target frequency point with the largest amplitude of the first spectrum is extracted from the refined frequency points.
- Step 1056 Acquire a distance value between the radar and the obstacle according to the first target frequency point.
- the first target frequency point F 1 may be obtained according to the refinement processing result, and the first target frequency point is used as the final target frequency point, according to the first target frequency point F 1 , the pre-configured light speed C, and the pre-configuration
- the period T of the radar modulated signal and the bandwidth of the pre-configured radar modulated signal are calculated using the formula:
- the obtained first target frequency point is used as the final target frequency point for calculating the distance between the radar and the obstacle, thereby further improving the accuracy of the radar ranging.
- the data extraction processing is performed on the filtered data according to the pre-configured scaling factor to obtain the extracted data, including:
- a data point is extracted every D data points, and zero is added after the extracted data points, so that the number of frequency points in the extracted data is the same as the number of frequency points in the filtered data.
- the foregoing step 104 may specifically include:
- Step 1041 Acquire, according to each spectral amplitude and a constant false alarm detection value corresponding to each spectral amplitude, obtain a target spectral amplitude that satisfies a pre-configured condition.
- the pre-configuration condition is that the target spectrum amplitude is greater than the previous spectrum amplitude, greater than the latter one of the spectrum amplitudes, and greater than its corresponding constant false alarm detection value.
- Step 1042 Obtain a maximum target spectral amplitude from each target spectral amplitude.
- step 1043 the frequency point corresponding to the largest target spectrum amplitude is used as the target frequency point.
- the step 105 may specifically include: according to the target frequency point F, the pre-configured speed of light C, the period T of the pre-configured radar modulation signal, and the bandwidth of the pre-configured radar modulation signal, using the formula:
- the radar modulated signal can be a triangular wave, a sawtooth wave, a sine wave. Different modulation signals can use their corresponding distance formula to obtain the distance between the radar and the obstacle. The value is not limited here.
- the radar and the obstacle may be relatively static or relatively movable, which is not limited herein.
- the radar ranging is realized based on the parallel processing and the pipeline structure as a whole, which greatly improves the efficiency of signal processing and reduces the processing delay, thereby improving the accuracy of the radar ranging.
- FIG. 6 is a schematic structural diagram of a radar-based ranging processing apparatus according to an embodiment of the present invention.
- the radar-based ranging processing apparatus 60 of the present embodiment may include a memory 61 and a processor 62.
- the processor 62 may be a Field-Programmable Gate Array (FPGA).
- the processor 62 may also be another processor having parallel processing characteristics and pipeline characteristics. For example, it may be a central processing unit (Central Processing Unit). , CPU) and a digital signal processor (DSP) combined into a processor.
- CPU Central Processing Unit
- DSP digital signal processor
- the memory 61 is configured to store program instructions
- the processor 62 is configured to call program instructions stored in the memory to implement:
- the method for obtaining the detection value of each constant false alarm is:
- the processor 62 is specifically configured to:
- the processor 62 is specifically configured to:
- the first adjacent value sequence is sent to the next stage for sorting, and the N adjacent values in the first adjacent value sequence are simultaneously sorted to obtain the second adjacent value sequence; the step is repeated until the Nth neighboring value sequence is obtained. .
- the processor 62 is specifically configured to:
- the N adjacent values in the adjacent value sequence are simultaneously compared at the same time; for each of the two adjacent values to be compared, if two adjacent values are determined according to the comparison result and the pre-configured sorting manner, the exchange position is required. Then, the two adjacent values are exchanged for the first adjacent value sequence, and the first adjacent value sequence is sent to the second phase;
- the N adjacent values in the sequence of j-1 neighboring values are simultaneously compared two by two; for each two adjacent values to be compared, two adjacent values are determined according to the comparison result and the pre-configured sorting manner. If the location needs to be exchanged, the two adjacent values are exchanged to obtain the sequence of the jth neighboring value, and the sequence of the jth adjacent value is sent to the j+1th phase; the j is incremented by 1, and the step is repeated until the Nth neighbor is obtained. Sequence of values;
- N j is an integer, and j is greater than or equal to 2, and j is less than or equal to N.
- the processor 62 is specifically configured to:
- the i-th neighboring value X(i) and the i+1th neighboring value X(i+1), i of the N adjacent values are equal to 1, 3, ..., N-1, respectively.
- the i-th neighboring value X(i) and the i+1th neighboring value X(i+1), i of the N neighboring values are equal to 2, 4, ..., N-2, respectively.
- the i-th adjacent value X(i) and the i+1th adjacent value X(i+1), i of the N adjacent values are equal to 1, 3, ..., N-2, respectively.
- the i-th neighboring value X(i) and the i+1th neighboring value X(i+1), i of the N adjacent values are equal to 2, 4, ..., N-1, respectively.
- t is an integer and t is less than or equal to N.
- the processor 62 is specifically configured to:
- the Pth threshold D(P) the number of spectral amplitudes in the input spectral amplitude data, NF, And the pre-configured constant false alarm probability value, and the constant false alarm detection value corresponding to the spectrum amplitude is obtained.
- the processor 62 is specifically configured to:
- N spectral amplitudes adjacent to the spectral amplitude are selected, and the N spectral amplitudes form a sequence of adjacent values corresponding to the spectral amplitude.
- the processor 62 is specifically configured to:
- the processor 62 is specifically configured to:
- the input spectrum amplitude data is obtained.
- the processor 62 is specifically configured to:
- the input spectrum amplitude data is obtained.
- the processor 62 is specifically configured to:
- the input spectral amplitude squared value data is calculated, and the input spectral amplitude squared value data is used as the input spectral amplitude data.
- the processor 62 is specifically configured to:
- the output data packet is windowed to obtain the windowed data.
- the processor 62 is specifically configured to:
- the difference frequency signal is processed by using a predetermined format to obtain a corresponding output data packet.
- the output data packet includes: a synchronization flag signal, Y data points, and a number of cycles in which the data points last.
- the processor 62 is specifically configured to:
- the final window function value is determined, and according to the window function value, the Y data points are windowed to obtain the windowed data.
- the processor 62 is specifically configured to:
- the target frequency point is subjected to spectrum refinement processing, and the first target frequency point is obtained according to the refinement processing result, and the first target frequency point is used as the target frequency point.
- the processor 62 is specifically configured to:
- the target frequency point is subjected to spectrum refinement processing, and the first target frequency point is obtained according to the refinement processing result.
- the processor 62 is specifically configured to:
- the distance value between the radar and the obstacle is obtained.
- the processor 62 is specifically configured to:
- a data point is extracted every D data points, and zero is added after the extracted data points, so that the number of frequency points in the extracted data is the same as the number of frequency points in the filtered data.
- the processor 62 is specifically configured to:
- the pre-configuration condition is: the target spectral amplitude is greater than a previous spectral amplitude, which is greater than a subsequent one The amplitude of the spectrum is greater than its corresponding constant false alarm detection value;
- the frequency point corresponding to the largest target spectrum amplitude is taken as the target frequency point.
- the processor 62 is specifically configured to:
- the distance between the radar and the obstacle is obtained according to the target frequency F, the pre-configured speed of light C, the period T of the pre-configured radar modulation signal, and the bandwidth of the pre-configured radar modulation signal.
- the processor can be a processor of a programmable logic gate array FPGA.
- the radar-based ranging processing device 60 may further include: a main controller 63.
- FIG. 7 is a schematic structural diagram of a radar-based ranging processing apparatus according to another embodiment of the present invention.
- Processor 62 can also include a buffer.
- the buffer is used for interaction between the processor and the main controller for data and control information
- the buffer includes a control logic module, a first cache module, and a second cache module;
- control logic module configured to control a read operation of the first cache module and a write operation of the second cache module
- the main controller 63 is configured to control a write operation of the first cache module and a read operation of the second cache module.
- the radar-based ranging processing device implements radar ranging based on parallel processing and pipeline structure, obtains a distance value between the radar and the obstacle, effectively improves processing efficiency, reduces processing delay, and thereby improves radar.
- the accuracy of ranging And setting the buffer to the structure of the double buffer module, the control logic module in the buffer controls the read operation of the first cache module and the write operation of the second cache module; and the first cache is controlled by the main controller outside the processor The write operation of the module and the read operation of the second cache module. Read and write conflicts between the control logic portion of the buffer and the external host controller are avoided. This makes the communication between the processor and the external main controller smoother.
- the device in this embodiment may be used to implement the technical solutions of the foregoing method embodiments of the present invention, and the implementation principles and technical effects thereof are similar, and details are not described herein again.
- FIG. 8 is a schematic structural diagram of an unmanned aerial vehicle according to an embodiment of the present invention.
- the unmanned aerial vehicle 80 provided in this embodiment includes a body 81, a robot arm 82 extending from the airframe, a power assembly 83 mounted on the arm, a radar 84, and any of the foregoing embodiments.
- a radar based ranging processing device 60 is shown in FIG. 8, the unmanned aerial vehicle 80 provided in this embodiment includes a body 81, a robot arm 82 extending from the airframe, a power assembly 83 mounted on the arm, a radar 84, and any of the foregoing embodiments.
- a radar based ranging processing device 60 is a radar based ranging processing device 60.
- the radar and the device are all arranged on the fuselage.
- the foregoing program may be stored in a computer readable storage medium, and the program is executed when executed.
- the foregoing steps include the steps of the foregoing method embodiments; and the foregoing storage medium includes: a medium that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.
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Abstract
本发明实施例提供一种基于雷达的测距处理方法、装置及无人飞行器,此方法包括:获取雷达的差频信号;根据差频信号,获取输入频谱幅度数据;基于并行处理方式,获取输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;根据目标频点,获取雷达与障碍物之间的距离值;其中,对于每个恒虚警检测值的获取方式为:获取频谱幅度对应的临近值序列,并将临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对临近值序列的排序,并根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值。通过并行处理提高了处理效率,降低了处理延时,从而提高了雷达测距的准确性。
Description
本发明涉及信号处理技术,尤其涉及一种基于雷达的测距处理方法、装置及无人飞行器。
随着科学技术的飞速发展,雷达技术进入现代人类生活的各个领域,拥有着不可或缺的重要地位。其中,连续波雷达凭借其距离分辨率较高,不存在探测盲区,抗干扰能力较强等优点,在各个领域中占据着越来越重要的位置。为了实时测量雷达与障碍物之间的距离,需要依据雷达回传的数据,并进行相应的处理,因此,基于连续波雷达的测距算法应运而生。
现有技术中,连续波雷达测距算法通常是利用中央处理单元(Central Processing Unit,CPU)以软件的方式来实现,由于CPU只能以串行的方式处理数据,即每次只能处理一个数据,这使得利用CPU来实现雷达测距算法时,算法的处理延时比较长,远远超过了雷达回传数据的延时,因此利用CPU来实现雷达测距算法时不能实时的处理雷达回传数据,导致雷达测距值准确度不高。因此,如何有效提高雷达测距算法的实时性成为亟需解决的技术问题。
发明内容
本发明的第一个方面是提供一种基于雷达的测距处理方法,包括:
获取所述雷达的差频信号;
根据所述差频信号,获取输入频谱幅度数据;
基于并行处理方式,获取所述输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;
根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;
根据所述目标频点,获取所述雷达与障碍物之间的距离值;
其中,对于每个恒虚警检测值的获取方式为:
获取所述频谱幅度对应的临近值序列,并将所述临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对所述临近值序列的排序,并根据排序后的临近值序列,获取所述频谱幅度对应的恒虚警检测值。
本发明的另一个方面是提供一种基于雷达的测距处理装置,包括:存储器和处理器;
其中,所述存储器,用于存储程序指令;
所述处理器,用于调用所述存储器中存储的所述程序指令以实现:
获取所述雷达的差频信号;
根据所述差频信号,获取输入频谱幅度数据;
基于并行处理方式,获取所述输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;
根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;
根据所述目标频点,获取所述雷达与障碍物之间的距离值;
其中,对于每个恒虚警检测值的获取方式为:
获取所述频谱幅度对应的临近值序列,并将所述临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对所述临近值序列的排序,并根据排序后的临近值序列,获取所述频谱幅度对应的恒虚警检测值。
本发明的再一个方面是提供一种无人飞行器,包括:机身、自所述机身延伸的机臂及装设于所述机臂上的动力组件,其特征在于,所述无人飞行器还包括雷达,以及如上所述的装置,所述雷达和所述装置均设置于所述机身上。
本发明实施例提供的基于雷达的测距处理方法、装置及无人飞行器,通过获取雷达的差频信号,根据差频信号获取输入频谱幅度数据;基于并行处理方式获取输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;根据目标频点,获取雷达与障碍物之间的距离值;其中,对于每个恒虚警检测值的获取方式为:获取频谱幅度对应的临近值序列,并采用预配置的并行流水线排序算法对临近值序列进行排序,根据排序后的临近值序列,获取频谱幅
度对应的恒虚警检测值。提高了处理效率,降低了处理延时,从而提高了雷达测距的准确性。
图1为本发明一实施例提供的基于雷达的测距处理方法的流程示意图;
图2A为本发明一实施例提供的偶数个临近值排序算法的示意图;
图2B为本发明一实施例提供的奇数个临近值排序算法的示意图
图3为本发明一实施例提供的获取临近值序列的示意图;
图4为本发明一实施例提供的输出数据包的示意图;
图5为本实施例提供的频谱细化处理的流程示意图;
图6为本发明一实施例提供的基于雷达的测距处理装置的结构示意图;
图7为本发明另一实施例提供的基于雷达的测距处理装置的结构示意图;
图8为本发明一实施例提供的无人飞行器的结构示意图。
为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
本发明的实施例提供了基于雷达的测距处理方法、装置及无人飞行器。雷达测距的基本原理为:采用一定的调制方式对雷达发射的载频信号进行调制,并将调制的载频信号作为发射信号和本振信号,雷达接收到目标回波后,首先将回波信号和本振信号进行混频、滤波及放大,获得差频信号,然后对其作频域分析,利用回波信号的频移与时延的对应关系可得到雷达与障碍物的距离。其中,雷达可以为连续波雷达。本发明的实施例并不限于此。
图1为本发明一实施例提供的基于雷达的测距处理方法的流程示意图,本实施例提供一种基于雷达的测距处理方法,用于测定雷达与障碍物之间的距离,以为后续操作提供依据。如图1所示,本实施例的方法可以包括:
步骤101,获取雷达的差频信号。
具体的,本实施例中待处理的对象是雷达的射频前端回传的发射信号与接收信号(也即回波信号)混频后得到的差频信号。可以理解地,该差频信号可以是混频后经过一定的滤波放大后得到的差频信号。
步骤102,根据差频信号,获取输入频谱幅度数据。
在获取到差频信号后,可以根据差频信号获取输入频谱幅度数据。
输入频谱幅度数据包括了多个频谱幅度(也可称为频谱幅度值),具体包括的频谱幅度的个数,可以在根据差频信号获取输入频谱幅度数据的过程中,根据实际需求设置。
本实施例中,根据差频信号获取输入频谱幅度数据的具体过程可以为现有技术中的方式,在此不做限定。
进一步举例说明,对差频信号进行频谱提取获得输入频谱幅度数据。
步骤103,基于并行处理方式,获取输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值。
其中,对于每个恒虚警检测值的获取方式为:
获取频谱幅度对应的临近值序列,并将临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对临近值序列的排序,并根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值。
具体的,在获得输入频谱幅度数据后,需要确定输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值,可以采用基于并行处理方式,并行地获取每个频谱幅度对应的恒虚警检测值。
进一步举例说明,可以基于具有并行处理特性的处理器实现并行地获取每个频谱幅度对应的恒虚警检测值,比如基于FPGA的并行特性,结合FPGA硬件实现。本实施例并不限于此,也可以是其他具备并行处理功能的处理器。
本实施例基于并行处理方式获取输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值,可以有效提高数据处理效率,减小雷达测距的时延,从而提高了雷达测距的准确性。
进一步地,对于每个恒虚警检测值的获取方式具体可以为:获取频谱幅度对应的临近值序列,并将临近值序列中的N个临近值两两同时进行排
序,至多通过N次完成对临近值序列的排序,并根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值。
具体可以为获取频谱幅度对应的临近值序列,并采用预配置的并行流水线排序算法,对临近值序列进行排序,并根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值。
具体的,频谱幅度对应的临近值序列为:输入频谱幅度数据中与该频谱幅度前后临近的N个频谱幅度组成的序列。每个频谱幅度对应一个临近值序列。在获取到各频谱幅度对应的临近值序列后,需要对每个临近值序列中的N个频谱幅度进行排序。
本实施例可以采用预配置的并行流水线排序算法,即将临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对临近值序列的排序。流水线是指,可以将排序过程分为多个阶段,并行是指对于每个阶段,可以并行地对临近值序列中临近的两两临近值进行比较排序。
进一步举例说明,以一个临近值序列为例,可以将4个临近值组成的临近值序列[A,B,C,D]的排序过程分为4个阶段,在第1阶段,并行地对A与B、C与D进行比较并排序,比如升序,假设A大于B,C大于D,则第1阶段排序结果为[B,A,D,C],以该序列[B,A,D,C]为输入进入下一个阶段的排序。
本实施例中,采用将临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对临近值序列的排序。有效降低了处理延时,进一步提高了雷达测距的准确性。
需要说明的是,本实施例中,不仅对于每一个临近值序列的排序过程采用并行流水线方式,且各临近值序列的排序过程也可以并行处理,即在同一时间可能每一个临近值序列都在进行排序处理。更可以有效降低处理延时。
在获取到各排序后的临近值序列后,则可以根据排序后的临近值序列获取其对应的频谱幅度所对应的恒虚警检测值。根据排序后的临近值序列获取其对应的频谱幅度所对应的恒虚警检测值的具体过程可以为现有技术中的方式,在此不做限定。
举例说明,对于每个频谱幅度,可以根据第一预设阈值P,从对应的排
序后的临近值序列中选取第P个临界值,并根据具体的计算公式计算获得该频谱幅度对应的恒虚警检测值。
步骤104,根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,搜索获取目标频点。
具体的,在获取到每个频谱幅度即对应的恒虚警检测值后,可以搜索获取目标频点。具体可以根据现有的方式来获取,在此不做限定。
进一步举例说明,采用频谱幅度峰值搜索方式,获得频谱幅度最大值对应的频点序号即为目标频点。本实施例不限于此。
步骤105,根据目标频点,获取雷达与障碍物之间的距离值。
具体的,在获取到目标频点后,即可以根据目标频点获取雷达与障碍物之间的距离值。具体可以根据现有的方式来获取,在此不做限定。
本实施例中,通过基于并行处理方式,获取输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值,根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,搜索获取目标频点,根据目标频点,获取雷达与障碍物之间的距离值,可以有效提高数据处理效率,减小雷达测距的时延,从而提高了雷达测距的准确性。并进一步采用预配置的并行流水线排序算法,对临近值序列进行排序,并根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值,进一步降低了处理延时,提高了雷达测距的准确性。
在一些实施例中,上述将临近值序列中的N个临近值两两同时进行排序,具体可以包括:
将临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列。
进一步举例说明,将临近值序列[A,B,C,D]中的第1个临近值与第2个临近值、第3个临近值与第4个临近值进行比较,即A与B、C与D,假设A大于B,C大于D,按升序排序,则比较后,A与B交换位置,C与D交换位置,获得第1临近值序列[B,A,D,C]。
在一些实施例中,上述至多通过N次完成对临近值序列的排序,具体可以包括:
将第1临近值序列送入下一个阶段进行排序,将第1临近值序列中的N
个临近值两两同时进行排序,获得第2临近值序列;重复此步骤,直至获得第N临近值序列。
在一些实施例中,上述将临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对临近值序列的排序,具体可以包括:
根据临近值序列,获取N个阶段;
在第1阶段,将临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列,并将第1临近值序列送入第2阶段;
在第j阶段,将第j-1临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第j临近值序列,并将第j临近值序列送入第j+1阶段;将j加1,重复该步骤,直至获取到第N临近值序列;其中,N,j为整数,且j大于或等于2,j小于或等于N。
具体的,以一个频谱幅度对应的临近值序列为例,若该临近值序列中包括N个临近值,则将该临近值序列的排序过程分为N个阶段,每完成一个阶段的排序,将获得的结果送入下一个阶段。其中,上述N个临近值两两同时进行比较具体过程如下:
若N为偶数,在第t阶段:
若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-1,同时进行比较;
若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-2,同时进行比较;
若N为奇数,在第t阶段:
若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-2,同时进行比较;
若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-1,同时进行比较;
其中,t为整数,且t小于或等于N。
循环执行各阶段,直至第N阶段,获取到第N临近值序列即为排序后
的临近值序列。
进一步举例说明,图2A为本发明一实施例提供的偶数个临近值排序算法的示意图。图2B为本发明一实施例提供的奇数个临近值排序算法的示意图。
如图2A所示,假设某一频谱幅度对应的序列为[A,B,C,D],即包括了4个临近值,并按升序排序,则排序过程为4个阶段,如下:
第1阶段,将临近值序列[A,B,C,D]中的第1个临近值与第2个临近值、第3个临近值与第4个临近值进行比较,即A与B、C与D,若A大于B,C大于D,预配置的排序方式为升序,则比较后,A与B交换位置,C与D交换位置,获得第1临近值序列[B,A,D,C];将第1临近值序列[B,A,D,C]送入第2阶段
第2阶段,针对第1临近值序列[B,A,D,C],将第2临近值序列中的第2个临近值与第3个临近值进行比较,即A与D,若A大于D,则比较后A与D交换位置,获得第2临近值序列[B,D,A,C];
第3阶段,针对第2临近值序列[B,D,A,C],将第3临近值序列中的第1个临近值与第2个临近值、第3个临近值与第4个临近值进行比较,即B与D、A与C,若B大于D,A大于C,则比较后交换位置,获得第3临近值序列[D,B,C,A]。
第4阶段,针对第3临近值序列[D,B,C,A],将第4临近值序列中的第2个临近值与第3个临近值进行比较和,即B与C,若B大于C,则比较后B与C交换位置,获得第4临近值序列[D,C,B,A]。则第4临近值序列[D,C,B,A]即为排序后的临近值序列。
如图2B所示,当临近值序列个数为奇数时,在奇数阶段,最后一个临近值不参与比较,直接进入下一个阶段,在偶数阶段,第1个临近值不参与比较直接进入下一个阶段,具体过程与临近值个数为偶数个相似,在此不再赘述。
本实施例中临近值序列的排序处理过程为运算量较大,耗时较长的部分;为了减小延时,采用了上述过程的基于并行流水线结构的排序算法结构,比如可以采用FPGA的并行处理特性和流水线结构,使得恒虚警检测过程中的排序计算耗时大大减小,将每组N个数据的排序算法分为N个阶段,每个阶段
同时完成N/2或者N/2-1对数据的大小值比较并根据数据的大小交换数据的位置(使其为升序或降序排列),然后将处理后的数据通过流水线的结构送到下一阶段的处理,这使得每组N个数据的排序算法只需要N个周期即可完成。
在一些实施例中,上述根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值,具体可以包括:
1)据第一预设阈值P,获取排序后的临近值序列中的第P个临界值;
2)根据第P个临界值D(P)、输入频谱幅度数据中频谱幅度的个数NF、以及预配置的恒虚警概率值,获取频谱幅度对应的恒虚警检测值。
具体可以根据第P个临界值D(P)、输入频谱幅度数据中频谱幅度的个数NF、以及预配置的恒虚警概率值Pf,采用公式:
Dcfar=D(P)×2NF×(Pfexp((-1/2NF)-1)
获取频谱幅度对应的恒虚警检测值Dcfar。本实施例不限于此。
具体的,第一预设阈值P可以根据实际经验进行设置。预配置的恒虚警概率值Pf是根据雷达***的实际情况进行设置。
在一些实施例中,上述获取频谱幅度对应的临近值序列,具体可以包括:从频谱幅度数据中,选取与频谱幅度前后临近的N个频谱幅度,并将N个频谱幅度形成频谱幅度对应的临近值序列。
上述获取频谱幅度对应的临近值序列具体的可以包括,从频谱幅度数据中,选取与频谱幅度前临近的T个频谱幅度,以及与频谱幅度后临近的S个频谱幅度,并去除与频谱幅度前临近的U个频谱幅度以及与频谱幅度后临近的U个,并将剩余的T+S-2U=N个频谱幅度形成频谱幅度对应的临近值序列。
具体的,临近值序列的获取规则可以为在当前频谱幅度的前面选择N/2个频谱幅度,再在当前频谱幅度的后面选择N/2个频谱幅度(选择时需要去除掉最靠近当前频谱幅度的前后各U个数据),如果当前频谱幅度的前面或后面没有N/2个频谱幅度时,则从另外一边多取一些临近值,保证取到的总临近值的个数为N,将这N个数据组成一个临近值序列。
进一步举例说明,图3为本发明一实施例提供的获取临近值序列的示意图。在该举例中,输入频谱幅度数据中频谱幅度的个数为16,U为1,
每个临近值序列中包括的临近值个数为6。对于16个频谱幅度,需获得16个对应的临近值序列。Di(i=1,2,…,16)表示第i个频谱幅度。
在一些实施例中,上述步骤102具体可以包括步骤1021、步骤1022。
步骤1021,根据差频信号,获取加窗后的数据。
步骤1022,根据加窗后的数据,获取输入频谱幅度数据。
本实施例中,可以采用现有技术,在此不做限定。
在一些实施例中,上述步骤1022具体还可以包括:
步骤10221,对加窗后的数据进行傅立叶变换,获得变换后的数据;
步骤10222,根据变换后的数据,获取输入频谱幅度数据。
具体过程可以采用现有技术实现,在此不做限定。
在一些实施例中,还可以采用输入频谱幅度平方值数据作为输入频谱幅度数据,进行后续处理。具体为:根据变换后的数据,计算获得输入频谱幅度平方值数据,将输入频谱幅度平方值数据作为输入频谱幅度数据。即,在获得变换后的数据后,计算输入频谱幅度平方值数据即可,而无需开方操作。
具体的,本实施例的频谱提取只需获得复数频谱的频谱幅度平方值,而无需开方操作,解决了现有技术中计算复数频谱的幅度值时需要先计算频谱幅度平方值,然后再进行开方操作,而开方操作需要较多的运算资源和时间的问题,因此,本实施例无需进行开方操作,有效提高了频谱提取的效率,从而提高了雷达测距的实时性及准确性。利用频谱幅度平方值作为后续进一步提取频率信息的依据,可以达到与现有做开方操作非常近似的效果,同时减小了运算量。
在一些实施例中,上述步骤1021具体还可以包括:
步骤10211,根据差频信号,获取提取的输出数据包。
具体可以采用现有技术实现,在此不做限定。
步骤10212,对输出数据包进行加窗处理,获得加窗后的数据。
本实施例中,对输出数据包进行加窗处理,获得加窗后的数据的具体操作,可以为现有技术中的操作方式,在此不做限定。
进一步举例说明,可以采用汉宁窗对输出数据包进行加窗处理,获得加窗后的数据。
在一些实施例中,步骤10211具体还可以包括:采用预定格式,对差频
信号进行处理,获取对应的输出数据包;其中,输出数据包包括:同步标志信号、Y个数据点以及数据点持续的周期数。
具体的,在获取到雷达的差频信号后,采用预定格式,对差频信号进行处理,获取对应的输出数据包,该输出数据包括具体可以包括:同步标识信号、Y个数据点以及每个数据点持续的周期数。每个输出数据包为一组数据。则上述获得的频谱幅度数据中包括的频谱幅度的个数即为一组数据中的数据点的个数Y。
进一步举例说明,图4为本发明一实施例提供的输出数据包的示意图。如图4所示,head_sync表示同步标志信号,data表示差频信号,M表示一个数据点持续的周期数,Y表示一组数据的数据点数。数据点的持续周期数和一组数据的数据点数的具体设定值可根据用户要求具体调整;同步标志信号提前有效数据(即Y个数据点)一个时钟周期输出,持续时间为一个时钟周期,用于标志一组新数据的起始位置,使得后续处理过程知道从何时开始处理一组新数据。
本实施例中,将差频信号采用预定格式进行处理,使得处理器可以支持随机突发的数据处理功能,不仅可以处理连续规则周期的雷达测距数据,还可以处理随机突发的不规则周期雷达测距数据。
在一些实施例中,步骤10212,具体可以包括:
步骤102121,遍历Y个数据点,获取最大值和最小值。
步骤102122,根据最大值和最小值,确定Y个数据点对应的波动范围R1。
具体的,最大值与最小值的差即为Y个数据点对应的波动范围R1。
步骤102123,根据波动范围R1以及预配置的波动范围R2,确定动态调整因子R2/R1。
具体的,预配置的波动范围R2可以根据雷达***实际情况进行配置。
步骤102124,根据初始配置的窗函数以及动态调整因子,确定最终的窗函数值,并根据窗函数值,对Y个数据点进行加窗处理,获取加窗后的数据。
具体的,根据输出数据包中的同步标志信号识别一组新的数据的开始,获取同步标志信号后的Y个数据点,对Y个数据点进行加窗处理。加窗处理即是将输入数据(即Y个数据点)与窗函数相乘,因此加窗操作会使得数
据幅度发生变化,有可能会使特别大的信号数据溢出,从而会降低信号的动态范围。因此,本实施例提供了可以根据信号的波动范围动态调整窗函数比例的机制,使得加窗后的信号不会溢出,保证信号的动态范围。
将初始配置的窗函数与动态调整因子的乘积作为最终的窗函数值,并将输入的Y个数据点与该窗函数值相乘,实现加窗处理,获得加窗后的数据。
在一些实施例中,还可以对步骤104获得的目标频点进行频谱细化处理,具体为:对目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点,并将第一目标频点作为目标频点。具体可以包括:获取加窗后的数据;根据加窗后的数据,对目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点。
在一些实施例中,图5为本实施例提供的频谱细化处理的流程示意图。如图5所示,上述步骤105,具体还可以包括:
步骤1051,对目标频点进行移频处理,使移至零频,获得移频后的数据。
具体的,可以利用数字控制振荡器产生一个频率为目标频点的复信号,将该正交的复信号与加窗后的数据相乘,从而将加窗后的数据中的目标频点移至零频,获得移频后的数据。可知此步骤需要获取上述加窗后的数据作为待处理的对象。
步骤1052,根据预配置的缩放倍数,对移频后的数据进行低通滤波处理,获得滤波后的数据。
具体的,根据频谱的预配置的缩放倍数滤除原始频谱(即移频后的数据,假如为B1)中目标频谱(假设为B2)以外的信号,预配置的缩放倍数(假设为D)可以由用户根据测距精度自行设定,它们之间的关系为D=B1/B2。由于低通滤波操作会产生群延时影响,为了避免前后两组数据之间的相互影响,本实施例中,每组有效滤波数据输出后,都会利用每组数据的同步标志信号对模块中的缓存寄存器进行清零,从而消除前后两组数据之间的影响。
步骤1053,根据预配置的缩放倍数对滤波后的数据进行数据抽取处理,获得抽取的数据。
具体的,对滤波后的数据,每间隔D(即是缩放倍数)个数据点只取一个数据,然后在所取所有数据的后面补零,使每组数据的总点数保持不变。
步骤1054,对抽取的数据进行频谱提取处理,以获取第一频谱幅度数据。
具体的频谱提取处理过程与上述频谱提取处理过程一致,在此不再赘述。
步骤1055,对第一频谱幅度数据进行峰值搜索处理,获得第一目标频点。
具体的,从细化后的频点中提取出第一频谱幅度最大的第一目标频点。
步骤1056,根据第一目标频点,获取雷达与障碍物之间的距离值。
具体的,可以根据细化处理结果获取第一目标频点F1,并将该第一目标频点作为最终的目标频点,根据第一目标频点F1、预配置的光速C、预配置的雷达调制信号的周期T以及预配置的雷达调制信号的带宽,采用公式:
获取雷达与障碍物之间的距离值。本实施例不限于此。
本实施例中,采用频谱细化处理后,获得的第一目标频点作为最终的目标频点用于计算雷达与障碍物之间的距离,进一步提高了雷达测距的准确性。
在一些实施例中,上述根据预配置的缩放倍数对滤波后的数据进行数据抽取处理,获得抽取的数据,包括:
每间隔D个数据点抽取一个数据点,并在抽取的数据点后补零,使抽取的数据中频点的个数与滤波后的数据中频点的个数相同。
在一些实施例中,上述步骤104具体可以包括:
步骤1041,根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,获取满足预配置条件的各目标频谱幅度。
其中,预配置条件为:目标频谱幅度大于其前面一个频谱幅度,大于其后面一个频谱幅度,且大于其对应的恒虚警检测值。
步骤1042,从各目标频谱幅度中获取最大的目标频谱幅度。
步骤1043,将最大的目标频谱幅度对应的频点作为目标频点。
在一些实施例中,上述步骤105具体可以包括:根据目标频点F、预配置的光速C、预配置的雷达调制信号的周期T以及预配置的雷达调制信号的带宽,采用公式:
获取雷达与障碍物之间的距离值。本实施例不限于此。
在一些实施例中,雷达调制信号可以为三角波、锯齿波、正弦波。不同的调制信号可以采用其对应的距离公式获得雷达与障碍物之间的距离
值,在此不做限定。
在一些实施例中,雷达与障碍物既可以相对静止,也可以是相对运动的,在此不做限定。
本实施例整体上基于并行处理及流水线结构实现雷达测距,大大提高了信号处理的效率,降低了处理延时,从而提高了雷达测距的准确性。
图6为本发明一实施例提供的基于雷达的测距处理装置的结构示意图,如图6所示,本实施例的基于雷达的测距处理装置60可以包括:存储器61和处理器62。处理器62可以是现场可编程门阵列(Field-Programmable Gate Array,FPGA),该处理器62还可以是其他具有并行处理特性及流水线特性的处理器,比如,可以是中央处理单元(Central Processing Unit,CPU)与数字信号处理器(Digital Signal Processor,DSP)结合成的处理器等。
存储器61用于存储程序指令;
处理器62用于调用存储器中存储的程序指令以实现:
获取雷达的差频信号;
根据差频信号,获取输入频谱幅度数据;
基于并行处理方式,获取输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;
根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;
根据目标频点,获取雷达与障碍物之间的距离值;
其中,对于每个恒虚警检测值的获取方式为:
获取频谱幅度对应的临近值序列,并将临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对临近值序列的排序,并根据排序后的临近值序列,获取频谱幅度对应的恒虚警检测值。
在一些实施例中,处理器62,具体用于:
将临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列。
在一些实施例中,处理器62,具体用于:
将第1临近值序列送入下一个阶段进行排序,将第1临近值序列中的N个临近值两两同时进行排序,获得第2临近值序列;重复此步骤,直至获得第N临近值序列。
在一些实施例中,处理器62,具体用于:
根据临近值序列,获取N个阶段;
在第1阶段,将临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列,并将第1临近值序列送入第2阶段;
在第j阶段,将第j-1临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第j临近值序列,并将第j临近值序列送入第j+1阶段;将j加1,重复该步骤,直至获取到第N临近值序列;
其中,N,j为整数,且j大于或等于2,j小于或等于N。
在一些实施例中,处理器62,具体用于:
若N为偶数,在第t阶段:
若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-1,同时进行比较;
若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-2,同时进行比较;
若N为奇数,在第t阶段:
若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-2,同时进行比较;
若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-1,同时进行比较;
其中,t为整数,且t小于或等于N。
在一些实施例中,处理器62,具体用于:
根据第一预设阈值P,获取排序后的临近值序列中的第P个临界值;
根据第P个临界值D(P)、输入频谱幅度数据中频谱幅度的个数NF、以
及预配置的恒虚警概率值,获取频谱幅度对应的恒虚警检测值。
在一些实施例中,处理器62,具体用于:
从频谱幅度数据中,选取与频谱幅度前后临近的N个频谱幅度,并将N个频谱幅度形成频谱幅度对应的临近值序列。
在一些实施例中,处理器62,具体用于:
从频谱幅度数据中,选取与频谱幅度前临近的T个频谱幅度,以及与频谱幅度后临近的S个频谱幅度,并去除与频谱幅度前临近的U个频谱幅度以及与频谱幅度后临近的U值频谱幅度,并将剩余的T+S-2U=N个频谱幅度形成频谱幅度对应的临近值序列。
在一些实施例中,处理器62,具体用于:
根据差频信号,获取加窗后的数据;
根据加窗后的数据,获取输入频谱幅度数据。
在一些实施例中,处理器62,具体用于:
对加窗后的数据进行傅立叶变换,获得变换后的数据;
根据变换后的数据,获取输入频谱幅度数据。
在一些实施例中,处理器62,具体用于:
根据变换后的数据,计算获得输入频谱幅度平方值数据,将输入频谱幅度平方值数据作为输入频谱幅度数据。
在一些实施例中,处理器62,具体用于:
根据差频信号,获取提取的输出数据包;
对输出数据包进行加窗处理,获得加窗后的数据。
在一些实施例中,处理器62,具体用于:
采用预定格式,对差频信号进行处理,获取对应的输出数据包;其中,输出数据包包括:同步标志信号、Y个数据点以及数据点持续的周期数。
在一些实施例中,处理器62,具体用于:
遍历Y个数据点,获取最大值和最小值;
根据最大值和最小值,确定Y个数据点对应的波动范围R1;
根据波动范围R1以及预配置的波动范围R2,确定动态调整因子R2/R1;
根据初始配置的窗函数以及动态调整因子,确定最终的窗函数值,并根据窗函数值,对Y个数据点进行加窗处理,获取加窗后的数据。
在一些实施例中,处理器62,具体用于:
对目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点,并将第一目标频点作为目标频点。
在一些实施例中,处理器62,具体用于:
获取加窗后的数据;
根据加窗后的数据,对目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点。
在一些实施例中,处理器62,具体用于:
对目标频点进行移频处理,使移至零频,获得移频后的数据;
根据预配置的缩放倍数,对移频后的数据进行低通滤波处理,获得滤波后的数据;
根据预配置的缩放倍数对滤波后的数据进行数据抽取处理,获得抽取的数据;
对抽取的数据进行频谱提取处理,以获取第一频谱幅度数据;
对第一频谱幅度数据进行峰值搜索处理,获得第一目标频点;
根据第一目标频点,获取雷达与障碍物之间的距离值。
在一些实施例中,处理器62,具体用于:
每间隔D个数据点抽取一个数据点,并在抽取的数据点后补零,使抽取的数据中频点的个数与滤波后的数据中频点的个数相同。
在一些实施例中,处理器62,具体用于:
根据每个频谱幅度及每个频谱幅度对应的恒虚警检测值,获取满足预配置条件的各目标频谱幅度;其中,预配置条件为:目标频谱幅度大于其前面一个频谱幅度,大于其后面一个频谱幅度,且大于其对应的恒虚警检测值;
从各目标频谱幅度中获取最大的目标频谱幅度;
将最大的目标频谱幅度对应的频点作为目标频点。
在一些实施例中,处理器62,具体用于:
根据目标频点F、预配置的光速C、预配置的雷达调制信号的周期T以及预配置的雷达调制信号的带宽,获取雷达与障碍物之间的距离值。
在一些实施例中,处理器可以为可编程逻辑门阵列FPGA构成的处理器。
在一些实施例中,该基于雷达的测距处理装置60,还可以包括:主控制器63。图7为本发明另一实施例提供的基于雷达的测距处理装置的结构示意图。
处理器62还可以包括缓存器。
缓存器用于处理器与主控制器之间进行数据与控制信息的交互;
缓存器包括控制逻辑模块、第一缓存模块和第二缓存模块;
控制逻辑模块,用于控制第一缓存模块的读操作和第二缓存模块的写操作;
主控制器63,用于控制第一缓存模块的写操作和第二缓存模块的读操作。
本实施例提供的基于雷达的测距处理装置,基于并行处理及流水线结构实现雷达测距,获得雷达与障碍物之间的距离值,有效提高了处理效率,降低了处理延时,从而提高雷达测距的准确性。并且将缓存器设置成双缓存模块的结构,由缓存器中的控制逻辑模块控制第一缓存模块的读操作和第二缓存模块的写操作;由该处理器外的主控制器控制第一缓存模块的写操作和第二缓存模块的读操作。避免了缓存器的控制逻辑部分与外部主控制器之间出现读写冲突。使得该处理器与外部主控制器之间的沟通更通畅。
本实施例的装置,可以用于执行本发明上述各方法实施例的技术方案,其实现原理和技术效果类似,此处不再赘述。
图8为本发明一实施例提供的无人飞行器的结构示意图。如图8所示,本实施例提供的无人飞行器80包括机身81、自机身延伸的机臂82、装设于机臂上的动力组件83、雷达84和上述任一实施例提供的基于雷达的测距处理装置60。
其中,雷达和装置均设置于机身上。
本领域普通技术人员可以理解:实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成,前述的程序可以存储于一计算机可读取存储介质中,该程序在执行时,执行包括上述方法实施例的步骤;而前述的存储介质包括:ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是:以上各实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述各实施例对本发明进行了详细的说明,本领域的普通
技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分或者全部技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。
Claims (43)
- 一种基于雷达的测距处理方法,其特征在于,包括:获取所述雷达的差频信号;根据所述差频信号,获取输入频谱幅度数据;基于并行处理方式,获取所述输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;根据所述目标频点,获取所述雷达与障碍物之间的距离值;其中,对于每个恒虚警检测值的获取方式为:获取所述频谱幅度对应的临近值序列,并将所述临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对所述临近值序列的排序,并根据排序后的临近值序列,获取所述频谱幅度对应的恒虚警检测值。
- 根据权利要求1所述的方法,其特征在于,所述将所述临近值序列中的N个临近值两两同时进行排序,包括:将所述临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列。
- 根据权利要求2所述的方法,其特征在于,所述至多通过N次完成对所述临近值序列的排序,包括:将所述第1临近值序列送入下一个阶段进行排序,将所述第1临近值序列中的N个临近值两两同时进行排序,获得第2临近值序列;重复此步骤,直至获得第N临近值序列。
- 根据权利要求3所述的方法,其特征在于,所述将所述临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对所述临近值序列的排序,包括:根据所述临近值序列,获取N个阶段;在第1阶段,将所述临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列,并 将所述第1临近值序列送入第2阶段;在第j阶段,将第j-1临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第j临近值序列,并将所述第j临近值序列送入第j+1阶段;将j加1,重复该步骤,直至获取到第N临近值序列;其中,N,j为整数,且j大于或等于2,j小于或等于N。
- 根据权利要求4所述的方法,其特征在于,所述N个临近值两两同时进行比较,包括:若N为偶数,在第t阶段:若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-1,同时进行比较;若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-2,同时进行比较;若N为奇数,在第t阶段:若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-2,同时进行比较;若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-1,同时进行比较;其中,t为整数,且t小于或等于N。
- 根据权利要求1所述的方法,其特征在于,所述根据排序后的临近值序列,获取所述频谱幅度对应的恒虚警检测值,包括:根据第一预设阈值P,获取所述排序后的临近值序列中的第P个临界值;根据所述第P个临界值D(P)、所述输入频谱幅度数据中频谱幅度的个数NF、以及预配置的恒虚警概率值,获取所述频谱幅度对应的恒虚警检测值。
- 根据权利要求1所述的方法,其特征在于,所述获取所述频谱幅度对应的临近值序列,包括:从所述频谱幅度数据中,选取与所述频谱幅度前后临近的N个频谱幅度,并将所述N个频谱幅度形成所述频谱幅度对应的临近值序列。
- 根据权利要求7所述的方法,其特征在于,从所述频谱幅度数据 中,选取与所述频谱幅度前临近的T个频谱幅度,以及与所述频谱幅度后临近的S个频谱幅度,并去除与所述频谱幅度前临近的U个频谱幅度以及与所述频谱幅度后临近的U个频谱幅度,并将剩余的T+S-2U=N个频谱幅度形成所述频谱幅度对应的临近值序列。
- 根据权利要求1所述的方法,其特征在于,所述根据所述差频信号,获取输入频谱幅度数据,包括:根据所述差频信号,获取加窗后的数据;根据所述加窗后的数据,获取所述输入频谱幅度数据。
- 根据权利要求9所述的方法,其特征在于,所述根据所述加窗后的数据,获取所述输入频谱幅度数据,包括:对所述加窗后的数据进行傅立叶变换,获得变换后的数据;根据所述变换后的数据,获取所述输入频谱幅度数据。
- 根据权利要求10所述的方法,其特征在于,所述根据所述变换后的数据,获取输入频谱幅度数据,包括:根据所述变换后的数据,计算获得输入频谱幅度平方值数据,将所述输入频谱幅度平方值数据作为所述输入频谱幅度数据。
- 根据权利要求9所述的方法,其特征在于,所述根据所述差频信号,获取加窗后的数据,包括:根据所述差频信号,获取提取的输出数据包;对所述输出数据包进行加窗处理,获得所述加窗后的数据。
- 根据权利要求12所述的方法,其特征在于,所述根据所述差频信号,获取提取的输出数据包,包括:采用预定格式,对所述差频信号进行处理,获取对应的输出数据包;其中,所述输出数据包包括:同步标志信号、Y个数据点以及所述数据点持续的周期数。
- 根据权利要求13所述的方法,其特征在于,所述对所述输出数据包进行加窗处理,获得所述加窗后的数据,包括:遍历所述Y个数据点,获取最大值和最小值;根据所述最大值和所述最小值,确定所述Y个数据点对应的波动范围R1;根据所述波动范围R1以及预配置的波动范围R2,确定动态调整因子 R2/R1;根据初始配置的窗函数以及所述动态调整因子,确定最终的窗函数值,并根据所述窗函数值,对所述Y个数据点进行加窗处理,获取加窗后的数据。
- 根据权利要求9所述的方法,其特征在于,还包括:对所述目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点,并将所述第一目标频点作为所述目标频点。
- 根据权利要求15所述的方法,其特征在于,所述对所述目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点,包括:获取所述加窗后的数据;根据所述加窗后的数据,对所述目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点。
- 根据权利要求1所述的方法,其特征在于,所述根据所述目标频点,获取所述雷达与障碍物之间的距离值,包括:对所述目标频点进行移频处理,使移至零频,获得移频后的数据;根据预配置的缩放倍数,对所述移频后的数据进行低通滤波处理,获得滤波后的数据;根据所述预配置的缩放倍数对所述滤波后的数据进行数据抽取处理,获得抽取的数据;对所述抽取的数据进行频谱提取处理,以获取第一频谱幅度数据;对所述第一频谱幅度数据进行峰值搜索处理,获得第一目标频点;根据所述第一目标频点,获取所述雷达与障碍物之间的距离值。
- 根据权利要求17所述的方法,其特征在于,所述根据所述预配置的缩放倍数对所述滤波后的数据进行数据抽取处理,获得抽取的数据,包括:每间隔D个数据点抽取一个数据点,并在抽取的数据点后补零,使抽取的数据中频点的个数与所述滤波后的数据中频点的个数相同。
- 根据权利要求1-14任一项所述的方法,其特征在于,所述根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,搜索获取目标频点,包括:根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,获取满足预配置条件的各目标频谱幅度;其中,所述预配置条件为:目标频 谱幅度大于其前面一个频谱幅度,大于其后面一个频谱幅度,且大于其对应的恒虚警检测值;从所述各目标频谱幅度中获取最大的目标频谱幅度;将所述最大的目标频谱幅度对应的频点作为所述目标频点。
- 根据权利要求1-18任一项所述的方法,其特征在于,所述根据所述目标频点,获取所述雷达与障碍物之间的距离值,包括:根据所述目标频点F、预配置的光速C、预配置的雷达调制信号的周期T以及预配置的雷达调制信号的带宽,获取所述雷达与障碍物之间的距离值。
- 一种基于雷达的测距处理装置,其特征在于,包括:存储器和处理器;其中,所述存储器,用于存储程序指令;所述处理器,用于调用所述存储器中存储的所述程序指令以实现:获取所述雷达的差频信号;根据所述差频信号,获取输入频谱幅度数据;基于并行处理方式,获取所述输入频谱幅度数据中每个频谱幅度对应的恒虚警检测值;根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,搜索获取目标频点;根据所述目标频点,获取所述雷达与障碍物之间的距离值;其中,对于每个恒虚警检测值的获取方式为:获取所述频谱幅度对应的临近值序列,并将所述临近值序列中的N个临近值两两同时进行排序,至多通过N次完成对所述临近值序列的排序,并根据排序后的临近值序列,获取所述频谱幅度对应的恒虚警检测值。
- 根据权利要求21所述的装置,其特征在于,所述处理器,具体用于:将所述临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列。
- 根据权利要求22所述的装置,其特征在于,所述处理器,具体用于:将所述第1临近值序列送入下一个阶段进行排序,将所述第1临近值序 列中的N个临近值两两同时进行排序,获得第2临近值序列;重复此步骤,直至获得第N临近值序列。
- 根据权利要求23所述的装置,其特征在于,所述处理器,具体用于:根据所述临近值序列,获取N个阶段;在第1阶段,将所述临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第1临近值序列,并将所述第1临近值序列送入第2阶段;在第j阶段,将第j-1临近值序列中的N个临近值两两同时进行比较;对于进行比较的每两个临近值,若根据比较结果及预配置的排序方式确定两个临近值需要交换位置,则将两个临近值交换位置,获得第j临近值序列,并将所述第j临近值序列送入第j+1阶段;将j加1,重复该步骤,直至获取到第N临近值序列;其中,N,j为整数,且j大于或等于2,j小于或等于N。
- 根据权利要求24所述的装置,其特征在于,所述处理器,具体用于:若N为偶数,在第t阶段:若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-1,同时进行比较;若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-2,同时进行比较;若N为奇数,在第t阶段:若t为奇数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于1,3,…,N-2,同时进行比较;若t为偶数,将N个临近值中第i个临近值X(i)与第i+1个临近值X(i+1),i分别等于2,4,…,N-1,同时进行比较;其中,t为整数,且t小于或等于N。
- 根据权利要求21所述的装置,其特征在于,所述处理器,具体用于:根据第一预设阈值P,获取所述排序后的临近值序列中的第P个临界值;根据所述第P个临界值D(P)、所述输入频谱幅度数据中频谱幅度的个数NF、以及预配置的恒虚警概率值,获取所述频谱幅度对应的恒虚警检测值。
- 根据权利要求21所述的装置,其特征在于,所述处理器,具体用于:从所述频谱幅度数据中,选取与所述频谱幅度前后临近的N个频谱幅度,并将所述N个频谱幅度形成所述频谱幅度对应的临近值序列。
- 根据权利要求27所述的装置,其特征在于,所述处理器,具体用于:从所述频谱幅度数据中,选取与所述频谱幅度前临近的T个频谱幅度,以及与所述频谱幅度后临近的S个频谱幅度,并去除与所述频谱幅度前临近的U个频谱幅度以及与所述频谱幅度后临近的U值频谱幅度,并将剩余的T+S-2U=N个频谱幅度形成所述频谱幅度对应的临近值序列。
- 根据权利要求21所述的装置,其特征在于,所述处理器,具体用于:根据所述差频信号,获取加窗后的数据;根据所述加窗后的数据,获取所述输入频谱幅度数据。
- 根据权利要求29所述的装置,其特征在于,所述处理器,具体用于:对所述加窗后的数据进行傅立叶变换,获得变换后的数据;根据所述变换后的数据,获取所述输入频谱幅度数据。
- 根据权利要求30所述的装置,其特征在于,所述处理器,具体用于:根据所述变换后的数据,计算获得输入频谱幅度平方值数据,将所述输入频谱幅度平方值数据作为所述输入频谱幅度数据。
- 根据权利要求29所述的装置,其特征在于,所述处理器,具体用于:根据所述差频信号,获取提取的输出数据包;对所述输出数据包进行加窗处理,获得所述加窗后的数据。
- 根据权利要求32所述的装置,其特征在于,所述处理器,具体用于:采用预定格式,对所述差频信号进行处理,获取对应的输出数据包;其中,所述输出数据包包括:同步标志信号、Y个数据点以及所述数据点持续的周期数。
- 根据权利要求33所述的装置,其特征在于,所述处理器,具体用于:遍历所述Y个数据点,获取最大值和最小值;根据所述最大值和所述最小值,确定所述Y个数据点对应的波动范围R1;根据所述波动范围R1以及预配置的波动范围R2,确定动态调整因子R2/R1;根据初始配置的窗函数以及所述动态调整因子,确定最终的窗函数值, 并根据所述窗函数值,对所述Y个数据点进行加窗处理,获取加窗后的数据。
- 根据权利要求29所述的装置,其特征在于,所述处理器,还用于:对所述目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点,并将所述第一目标频点作为所述目标频点。
- 根据权利要求35所述的装置,其特征在于,所述处理器,具体用于:获取所述加窗后的数据;根据所述加窗后的数据,对所述目标频点进行频谱细化处理,并根据细化处理结果获取第一目标频点。
- 根据权利要求21所述的装置,其特征在于,所述处理器,具体用于:对所述目标频点进行移频处理,使移至零频,获得移频后的数据;根据预配置的缩放倍数,对所述移频后的数据进行低通滤波处理,获得滤波后的数据;根据所述预配置的缩放倍数对所述滤波后的数据进行数据抽取处理,获得抽取的数据;对所述抽取的数据进行频谱提取处理,以获取第一频谱幅度数据;对所述第一频谱幅度数据进行峰值搜索处理,获得第一目标频点;根据所述第一目标频点,获取所述雷达与障碍物之间的距离值。
- 根据权利要求37所述的装置,其特征在于,所述处理器,具体用于:每间隔D个数据点抽取一个数据点,并在抽取的数据点后补零,使抽取的数据中频点的个数与所述滤波后的数据中频点的个数相同。
- 根据权利要求21-34任一项所述的装置,其特征在于,所述处理器,具体用于:根据所述每个频谱幅度及所述每个频谱幅度对应的恒虚警检测值,获取满足预配置条件的各目标频谱幅度;其中,所述预配置条件为:目标频谱幅度大于其前面一个频谱幅度,大于其后面一个频谱幅度,且大于其对应的恒虚警检测值;从所述各目标频谱幅度中获取最大的目标频谱幅度;将所述最大的目标频谱幅度对应的频点作为所述目标频点。
- 根据权利要求21-38任一项所述的装置,其特征在于,所述处理器,具体用于:根据所述目标频点F、预配置的光速C、预配置的雷达调制信号的周期T以及预配置的雷达调制信号的带宽,获取所述雷达与障碍物之间的距离值。
- 根据权利要求21-38任一项所述的装置,其特征在于,所述处理器为可编程逻辑门阵列FPGA构成的处理器。
- 根据权利要求41所述的装置,其特征在于,还包括:主控制器;所述处理器还包括:缓存器;所述缓存器用于所述处理器与所述主控制器之间进行数据与控制信息的交互;所述缓存器包括控制逻辑模块、第一缓存模块和第二缓存模块;所述控制逻辑模块,用于控制所述第一缓存模块的读操作和所述第二缓存模块的写操作;所述主控制器,用于控制所述第一缓存模块的写操作和所述第二缓存模块的读操作。
- 一种无人飞行器,包括机身、自所述机身延伸的机臂及装设于所述机臂上的动力组件,其特征在于,所述无人飞行器还包括雷达,以及如权利要求21-42任一项所述的装置,所述雷达和所述装置均设置于所述机身上。
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CN112986973A (zh) * | 2019-12-18 | 2021-06-18 | 华为技术有限公司 | 距离测量方法和距离测量装置 |
US20210326581A1 (en) * | 2020-04-14 | 2021-10-21 | Bitsensing Inc. | DNN-Based Human Face Classification |
CN112686222B (zh) * | 2021-03-12 | 2021-06-29 | 耕宇牧星(北京)空间科技有限公司 | 星载可见光探测器对船舶目标检测的方法和*** |
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Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105487060A (zh) * | 2015-11-25 | 2016-04-13 | 上海无线电设备研究所 | 一种双通道四斜率调制的多目标提取方法 |
CN106054193A (zh) * | 2016-05-24 | 2016-10-26 | 深圳市雷博泰克科技有限公司 | 一种车辆周围多目标检测方法、处理器及毫米波雷达*** |
CN106443671A (zh) * | 2016-08-30 | 2017-02-22 | 西安电子科技大学 | 基于调频连续波的sar雷达动目标检测与成像方法 |
Family Cites Families (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102353939B (zh) * | 2011-06-28 | 2013-05-22 | 清华大学 | 一种改进的恒虚警方法 |
CN103257346B (zh) * | 2013-05-15 | 2014-12-10 | 桂林电子科技大学 | 一种汽车防撞雷达多目标探测方法与*** |
KR101896725B1 (ko) * | 2013-12-02 | 2018-09-07 | 주식회사 만도 | Fmcw 레이더 기반의 주변 환경 감지 방법 및 장치 |
CN103823215B (zh) * | 2014-03-03 | 2016-03-02 | 中国科学院电子学研究所 | 线性调频连续波雷达测距方法 |
CN104569961B (zh) * | 2015-01-22 | 2017-04-26 | 中国科学院电子学研究所 | 基于频谱细化的雷达测距方法 |
CN105116380A (zh) * | 2015-08-13 | 2015-12-02 | 电子科技大学 | 一种排序式恒虚警门限的计算方法 |
CN105204023A (zh) * | 2015-09-11 | 2015-12-30 | 安徽四创电子股份有限公司 | 基于连续波体制气象雷达***的回波信号处理方法及装置 |
CN107255814B (zh) * | 2017-07-31 | 2020-04-10 | 西安电子科技大学 | 一种基于lfmsk波形的雷达目标检测方法 |
-
2017
- 2017-12-18 CN CN201780026275.4A patent/CN109154651A/zh active Pending
- 2017-12-18 WO PCT/CN2017/116989 patent/WO2019119223A1/zh active Application Filing
-
2020
- 2020-06-09 US US16/897,003 patent/US20200301007A1/en not_active Abandoned
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105487060A (zh) * | 2015-11-25 | 2016-04-13 | 上海无线电设备研究所 | 一种双通道四斜率调制的多目标提取方法 |
CN106054193A (zh) * | 2016-05-24 | 2016-10-26 | 深圳市雷博泰克科技有限公司 | 一种车辆周围多目标检测方法、处理器及毫米波雷达*** |
CN106443671A (zh) * | 2016-08-30 | 2017-02-22 | 西安电子科技大学 | 基于调频连续波的sar雷达动目标检测与成像方法 |
Non-Patent Citations (2)
Title |
---|
MAGAZ, B.: "An efficient FPGA implementation of the OS-CFAR processo", 2008 INTERNATIONAL RADAR SYMPOSIUM, 1 May 2008 (2008-05-01), pages 1 - 4, XP055621478 * |
MELEBARI, ASEM: "The effect of windowing on the performance of the CA - CFAR and OS-CFAR algorithms", 2015 IEEE RADAR CONFERENCE, 27 February 2016 (2016-02-27), pages 249 - 254, XP032865741 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
RU2730182C1 (ru) * | 2019-08-19 | 2020-08-19 | Иван Васильевич Колбаско | Способ многообзорного накопления сигнала в рлс при обнаружении воздушных целей в импульсно-доплеровском режиме |
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