CN107783125B - Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method - Google Patents

Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method Download PDF

Info

Publication number
CN107783125B
CN107783125B CN201610725771.6A CN201610725771A CN107783125B CN 107783125 B CN107783125 B CN 107783125B CN 201610725771 A CN201610725771 A CN 201610725771A CN 107783125 B CN107783125 B CN 107783125B
Authority
CN
China
Prior art keywords
frequency
channel
value
threshold
peak point
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201610725771.6A
Other languages
Chinese (zh)
Other versions
CN107783125A (en
Inventor
田雨农
王鑫照
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dalian Roiland Technology Co Ltd
Original Assignee
Dalian Roiland Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Dalian Roiland Technology Co Ltd filed Critical Dalian Roiland Technology Co Ltd
Priority to CN201610725771.6A priority Critical patent/CN107783125B/en
Publication of CN107783125A publication Critical patent/CN107783125A/en
Application granted granted Critical
Publication of CN107783125B publication Critical patent/CN107783125B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
    • G01S13/536—Discriminating between fixed and moving objects or between objects moving at different speeds using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

Rotor unmanned aerial vehicle anticollision millimeter wave radar system and signal processing method belong to the signal processing field for collision easily takes place and between the barrier when solving rotor unmanned aerial vehicle low-altitude flight, lead to the problem of rotor unmanned aerial vehicle's damage, the technical essential is: s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2; s2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data; and S3, carrying out CFAR threshold detection on the complex modulus value after FFT, outputting a first peak point of a threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies.

Description

Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method
Technical Field
The invention belongs to the field of signal processing, and relates to a radar signal processing method.
Background
In recent years, with the continuous development of technologies, the price of a civil small-sized rotor unmanned aerial vehicle is lower and lower, and the civil small-sized rotor unmanned aerial vehicle is widely applied to the fields of aerial photography, film shooting, pesticide spraying, on-site rescue, ground remote sensing surveying and mapping, high-voltage line power grid inspection and the like. But because the rotor unmanned aerial vehicle easily takes place when the low-altitude flight with the collision between the barrier, lead to rotor unmanned aerial vehicle's damage. At present, natural objects such as trees and artificial objects such as power lines, telegraph poles and buildings are mainly used as objects threatening the safety of outdoor low-altitude flight of the rotor unmanned aerial vehicle.
Because the working wavelength of the millimeter wave radar is between 1mm and 10mm, compared with other detection modes, the millimeter wave radar has the advantages of stable detection performance, good environmental adaptation, small size, low price, capability of being used in relatively severe rainy and snowy weather and the like. Therefore, the invention mainly introduces the realization of the unmanned aerial vehicle obstacle avoidance function method based on the millimeter wave radar.
Disclosure of Invention
In order to solve the problem that the rotor unmanned aerial vehicle is damaged due to collision with a barrier when the rotor unmanned aerial vehicle flies in a low altitude mode, the invention provides a method for processing an anti-collision millimeter wave radar signal of the rotor unmanned aerial vehicle, so that the speed, the distance and the angle of the barrier can be obtained through calculation, and therefore the barrier can be avoided.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the utility model provides a rotor unmanned aerial vehicle anticollision millimeter wave radar system, including ARM processing system, signal generator, voltage controlled oscillator, the transmitter, a receiver, the mixer, signal conditioning circuit, the AD converter, ARM processing system's one end is connected in signal generator, signal generator connects in voltage controlled oscillator, voltage controlled oscillator connects respectively in the first end of transmitter and mixer, the second end connection receiver of mixer, signal conditioning circuit is connected to the third end of mixer, signal conditioning circuit connects the AD converter, the other end of ARM processing system is connected to the AD converter.
Has the advantages that:
1. the invention provides a waveform design for realizing a rotor unmanned aerial vehicle anti-collision millimeter wave radar system based on linear frequency modulation triangular waves for the first time;
2. the invention provides a processing process of a rotor unmanned aerial vehicle anti-collision millimeter wave radar signal processing subsystem based on linear frequency modulation triangular waves, the subsystem can detect the relative distance and the relative speed of a front obstacle, and meanwhile, the detection function of a target direction angle can be realized.
Drawings
FIG. 1 is a graph of the frequency variation of a chirped triangular wave FMCW over a frequency sweep period;
fig. 2 is a signal processing flow diagram of a short-distance collision avoidance system of a rotor unmanned aerial vehicle according to the embodiment;
FIG. 3 is a working block diagram of an unmanned aerial vehicle collision avoidance millimeter wave radar system;
fig. 4 is a hardware block diagram of an ARM processing system of the unmanned aerial vehicle collision avoidance radar system;
fig. 5 schematic diagram of a measurement process of the unmanned aerial vehicle anti-collision radar system.
Detailed Description
Example 1: the utility model provides a rotor unmanned aerial vehicle short distance collision avoidance system based on combination waveform, including ARM processing system, signal generator, voltage controlled oscillator, the transmitter, the receiver, the mixer, signal conditioning circuit, the AD converter, the one end of ARM chip is connected in signal generator, signal generator connects in voltage controlled oscillator, voltage controlled oscillator connects respectively in the first end of transmitter and mixer, the receiver is connected to the second end of mixer, signal conditioning circuit is connected to the third end of mixer, signal conditioning circuit connects the AD converter, the other end of ARM chip is connected to the AD converter.
The working principle of the unmanned aerial vehicle anti-collision millimeter wave radar system is that the distance and the speed of a target to be detected are determined by utilizing the frequency difference between a transmitting signal and an echo signal, a linear frequency modulation triangular wave is transmitted by an ARM chip in a DA mode, namely a modulation signal with a certain amplitude and frequency is output, a Voltage Controlled Oscillator (VCO) generates a transmitting signal (linear frequency modulation continuous triangular wave) within a certain range under the action of the modulation signal, and the frequency of the transmitting signal is changed according to the rule of the modulation signal, so that the FMCW working mode is realized. One path of the emission signal is radiated to the space in front of the flight of the unmanned aerial vehicle through the signal generator, the other path of the emission signal is mixed with the echo signal reflected back, the frequency of the echo signal at the moment is changed compared with the frequency of the previous emission signal, and the signal obtained after the frequency mixer is a difference frequency signal.
Unmanned aerial vehicle flight the place ahead target information just contain in this difference frequency signal, through inputing the ARM chip with difference frequency signal through signal conditioning (after signal amplification filtering promptly) and carrying out AD sampling, carry out digital signal processing with the data after the sampling in the ARM chip, then obtain the distance of target through signal processing, speed, relevant information such as angle, insert into unmanned aerial vehicle main control unit through CAN or other communication methods or export and pass back to terminals such as host computer or cell-phone through wireless transmission mode and show in real time, thereby realize unmanned aerial vehicle anticollision function.
Example 2: as a supplementary technical solution of embodiment 1, the transmitter is a transmitting antenna, and the receiver is a three-row receiving antenna. The three rows of receiving antennas form two receiving antennas through a back feed network, and the array is formed in a micro-strip rectangular patch mode. The transmitting antenna and the receiving antenna are connected with the back microwave circuit through the via holes. The ARM processing system comprises an ARM processing module, a power supply module, a serial port module and a CAN module, wherein the AMR processing module enables four paths of I/Q intermediate frequency signals output by the signal conditioning circuit to enter four paths of AD acquisition channels of the ARM chip through the signal conditioning circuit, and the four paths of I/Q intermediate frequency signals are output through the serial port module or the CAN module.
In this embodiment, the transmitter and the receiver mainly include: 1. forming transmitting and receiving beams required for radar detection; 2. radiating the emission signal to a designated area; 3. and receiving a target scattered echo signal in the designated area.
In the embodiment, a 24GHz chip of the British flying is also selected to realize the transmission and receiving processing of signals, and the chip is mature in application and has the advantages of small size, low power consumption, light weight and the like. The signal conditioning circuit realizes the functions of filtering, amplitude amplification and the like of the intermediate-frequency analog signal and comprises a signal amplification part and a signal filtering part.
Referring to fig. 4, a block diagram of the overall design of the ARM processing system of the unmanned aerial vehicle anti-collision millimeter wave radar system is shown in fig. 4. The ARM processing system adopts a single ARM processing structure; the main circuit comprises an ARM processing module, a power supply module, a serial port module and a CAN module. The AMR processing module mainly enables four paths of I/Q intermediate frequency signal lines output by the signal conditioning circuit to enter four paths of AD acquisition channels of the ARM through the signal conditioning circuit. And outputting the result through a serial port or a CAN port after signal processing. The serial port and the CAN port CAN be selected according to different scenes.
The power supply module provides voltage for the whole ARM processing system. And 5V and 3.3V voltage is provided for the radio frequency front end and the signal conditioning circuit, the power supply input adopts wide-range input voltage, and 12V and 24V are compatible. The ARM processing system controls the radio frequency front end to transmit the waveform, receives and resolves the echo signal and outputs a measurement result, and after the ARM processing system is powered on, the system initialization, the ADC module initialization, the configuration of the radio frequency chip to transmit the waveform, the echo signal processing and the like are sequentially completed. The ARM processing module controls an ARM processing system (VCO) to emit linear frequency modulation triangular waves in a DA mode, the ADC in the chip collects echo data to process the echo data, and the measured distance, speed and azimuth angle are output and sent to an upper computer to be displayed. The above process is repeated to realize continuous output and display of the measured value, as shown in fig. 5.
Example 3: to embodiment 1 or 2, each rotor unmanned aerial vehicle anticollision millimeter wave radar system, this embodiment provides its corresponding signal processing method, includes the following step:
s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2; the dc removing method in step S1 includes: calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; calculating the mean value of the upper sweep IQ data collected by AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ data; the step mainly plays a role in removing direct current.
S2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data; in step S2, a windowing step is further included, which is located after the dc removing step. I, Q data after direct current removal are merged into an I + jQ data form, then windowing processing is carried out, and windowing processing is carried out on the data of the up-scanning frequency band and the down-scanning frequency band in the channel 1 and the data of the up-scanning frequency band in the channel 2. A Hanning window or a Hamming window and the like can be selected to reduce side lobes, so that the detection performance of the target is improved; the hanning window will cause the main lobe to widen and decrease, but the side lobes will decrease significantly.
The Hanning window has the calculation formula of
Figure GDA0002956842910000041
S3, performing CFAR threshold detection on the complex modulus value after FFT, outputting a first peak point of a threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies;
as an embodiment, in step S3, the object closest to the drone is mainly considered as the object with the greatest risk to the drone aircraft, so that the maximum value of all the threshold values is not found, but the peak value of the first threshold value is selected. If the peak coordinate of the first threshold point of the up-scan band in the channel 1 is p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT-transformed data is a _ p1_ up +1j b _ p1_ up, and the phase is
Figure GDA0002956842910000042
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT-transformed data is a _ p2_ up +1j × b _ p2_ up, and the phase is
Figure GDA0002956842910000043
Setting the peak value coordinate of the first threshold passing point of the lower sweep frequency segment in the channel 1 as p1_ down, and setting the frequency value corresponding to the point as f1_ down; wherein: a represents the data value of the I path, b represents the data value of the Q path, a _ p1 represents that in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, a _ p2 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p2, b _ p1 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, b _ p2 represents in the array formed by a + j × b, and the corresponding coordinate of the peak point of the threshold is p 2.
S4, calculating to obtain the distance of the unmanned aerial vehicle to the front obstacle target by using the frequency value of the upper sweep frequency and the frequency value corresponding to the lower sweep frequency in the channel 1 obtained in the step S3;
as an example: in the step S4, the frequency value f1_ up of the upper sweep frequency and the frequency value f1_ down corresponding to the lower sweep frequency in the channel 1 obtained in the step S3 are calculated according to the formula
Figure GDA0002956842910000051
Calculating to obtain unmanned aerial vehicle forward obstacle targetDistance, where T is a triangular wave period, T is 20ms, B is a bandwidth, B is 200MHz, c is the speed of light, and c is 3.0 × 108；
According to the formula
Figure GDA0002956842910000052
Calculating to obtain the speed of the unmanned aerial vehicle forward obstacle target, wherein f0Is the center frequency, f0＝24.125GHz；
And S5, respectively calculating the phase positions of the channel 1 and the channel 2 obtained in the step S3 according to the respective upper frequency sweep to obtain an azimuth angle.
As an example: in step S5, the phases calculated from the respective upper frequency sweeps in channel 1 and channel 2 obtained in step S3 are respectively used
Figure GDA0002956842910000053
And
Figure GDA0002956842910000054
according to a calculation formula
Figure GDA0002956842910000055
Obtaining the phase difference delta psi; according to the formula
Figure GDA0002956842910000056
And calculating the azimuth angle, wherein d is the antenna spacing.
As an embodiment, further comprising the steps of: and S6, filtering and tracking, and predicting the distance and the speed value at the next measurement moment.
Further, the signal processing of the unmanned aerial vehicle short-distance anti-collision millimeter wave radar system based on the combined waveform of the sawtooth wave and the constant frequency wave is completed through the steps, and the resolving process of the relative speed, the relative distance and the corresponding azimuth angle of the single target is completed.
After the unmanned aerial vehicle short-distance anti-collision millimeter wave radar system finishes the resolving process of the relative speed, the relative distance and the corresponding azimuth angle of the target, a filtering and tracking module is needed. Because the system output data has a high refresh rate and the variation of distance, speed and the like is small in a short time, the system can be approximately regarded as uniform motion, the variation rate of the height can be estimated through a certain algorithm, and the distance, the speed value and the like at the next measurement moment can be predicted. The tracking and prediction method is the premise and basis of adaptive tracking filtering. The main methods currently include linear autoregressive filtering, wiener filtering, weighted least squares filtering, alpha-beta and alpha-beta-gamma filtering, kalman filtering, simplified kalman filtering, and the like.
The present invention recommends the use of an alpha-beta filter. The alpha-beta filter is suitable for the condition that the change rate of the tracking error is relatively uniform, so that the alpha-beta filter is basically suitable for the flight scene of the unmanned aerial vehicle.
In the α - β filter, the prediction equation of the constant gain filter is X (K +1/K) ═ Φ X (K/K), and the filter equation is X (K +1/K +1) ═ X (K +1/K) + K [ Z (K +1) -H (K +1/K) ], where X (K/K) is a filtered value at time K, X (K +1/K) is a predicted value at time K to the next time, and Z (K) is an observed value at time K.
When the target motion equation adopts a constant velocity model, the constant gain matrix K is [ alpha, beta/T ]]TIts state transition matrix
Figure GDA0002956842910000061
The measurement matrix of the model is H ═ 1, 0]. The α - β filter is a constant gain filter satisfying the expression K, the state transition matrix Φ and the measurement matrix H, i.e. the constant gain filter
Figure GDA0002956842910000062
Figure GDA0002956842910000063
The selection of the parameters a and β in the a- β filter is relevant for the response of the tracking, the convergence speed and the tracking stability. Generally, 0 < alpha < 1, 0 < beta < 1 are required. In engineering, the values of alpha and beta can be calculated according to a formula, namely
Figure GDA0002956842910000064
And
Figure GDA0002956842910000065
where k is the number of times, and α and β take different values as k changes, in practice, these two parameters tend to be constant.
The target speed and distance of single settlement can be filtered, tracked and predicted through the alpha-beta filter. The target tracking can be better realized, the output data is smoother, the appearance of abnormal values is reduced, and the stability of the system is effectively improved.
Example 4: in addition to embodiment 3, this embodiment mainly accomplishes the measurement of the distance, speed and orientation of the environmental obstacle ahead of the unmanned rotorcraft in flight. The front obstacles are mainly aimed at people, trees, walls, nets, high-voltage lines and other targets.
The millimeter wave radar designed by the embodiment has the working frequency of 24GHz or 77GHz, adopts an FMCW continuous wave system, and adopts linear frequency modulation, so that the distance resolution is high. The waveform adopts a chirp triangular wave FMCW, mainly because the calculation of the target distance and speed is realized by the present embodiment. Target distance and speed calculation can be achieved through the upper frequency sweep and the lower frequency sweep of the triangular wave. The rotor unmanned aerial vehicle's of this embodiment design maximum airspeed is 40km/h, and the biggest range finding of unmanned aerial vehicle anticollision is 50m, is higher than the unmanned aerial vehicle anticollision distance on the present market more than 3 times.
The embodiment mainly provides the design of the signal processing part of the anti-collision millimeter wave radar of the unmanned aerial vehicle and a signal processing method.
The radar center frequency f designed by the embodiment is 24.125 GHz. Triangular waves are selected as the emission waveforms, the period is 20ms, and the bandwidth is 200 MHz. The transmit waveform is shown in fig. 1.
In the embodiment, the resolving of the target distance and speed is realized through single-path IQ data, and because the calculation of the target azimuth angle is realized in the embodiment, the embodiment adopts a double-receiving antenna mode, namely, two-path IQ data, and the angle measurement function of the target is realized through the calculation of respective up-scanning frequency bands of two paths.
Rotor unmanned aerial vehicle anticollision millimeter wave radar signal processing flow chart, as shown in fig. 2, concrete realization step is as follows:
1. calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; and calculating the mean value of the upper frequency sweep IQ data collected by the AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ. The step mainly plays a role in removing direct current.
2. And carrying out FFT (fast Fourier transform) on the IQ data which are subjected to direct current removal and are collected by the A/D in the channel 1 and the channel 2, and converting time domain data into frequency data.
3. The embodiment carries out CFAR threshold detection on the complex modulus value after FFT conversion, outputs the first peak value point of the threshold, mainly considers that the object which has the largest risk degree to the unmanned plane and is closest to the unmanned plane, and therefore the maximum value of all the threshold is not found, and the peak value of the first threshold is selected.
Setting the peak coordinate of the first threshold point of the up-scan frequency band in the channel 1 as p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT data is a _ p1_ up +1j b _ p1_ up, and the phase is
Figure GDA0002956842910000081
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT data is a _ p2_ up +1j × b _ p2_ up, and the phase position is
Figure GDA0002956842910000082
And if the peak value coordinate of the first threshold point of the lower sweep frequency segment in the channel 1 is p1_ down, the frequency value corresponding to the point is f1_ down.
4. The frequency value f1_ up of the upper sweep frequency in the channel I and the frequency value f1_ down corresponding to the lower sweep frequency obtained in the step three are processed according to a formula
Figure GDA0002956842910000083
Wherein T is a triangular wavePeriod, T20 ms, B bandwidth, B200 MHz, c speed, c 3.0 × 108(ii) a According to the formula
Figure GDA0002956842910000084
Wherein f is0Is the center frequency, f024.125 GHz. According to the two formulas, the distance and the speed of the unmanned aerial vehicle to the obstacle target are obtained.
5. The phases respectively calculated from the respective upper frequency sweeps in the channel 1 and the channel 2 obtained in the step S3
Figure GDA0002956842910000085
And
Figure GDA0002956842910000086
the calculation is according to a calculation formula
Figure GDA0002956842910000087
The phase difference is obtained as Δ ψ.
According to the formula
Figure GDA0002956842910000088
And calculating the azimuth angle, wherein d is the antenna spacing.
The function of resolving information such as the distance, the speed and the azimuth angle of the unmanned aerial vehicle to the obstacle in front of the unmanned aerial vehicle in operation by the rotor unmanned aerial vehicle anti-collision millimeter wave radar is completed by the steps.
Example 5: for the peak processing in the above solutions, this embodiment provides a peak processing method applied to the signal of the unmanned aerial vehicle:
setting a peak point threshold factor α for limiting the absolute value of the difference between the detected threshold-crossing maximum peak point and the maximum peak point appearing in the previous cycle, so that the absolute value of the difference is not greater than the peak point threshold factor α:
the expression is as follows:
|L_max(k)-L_max(k-1)|≤α；
Figure GDA0002956842910000091
wherein: l _ max (k) is the maximum peak point coordinate of the threshold passing of the k period, L _ max (k-1) is the maximum peak point coordinate of the last period, and k represents the kth moment; v. ofmaxThe maximum flight speed of the unmanned aerial vehicle is shown, lambda is the wavelength of the millimeter wave radar, fs is the sampling rate, and N is the number of points of FFT;
if the absolute value difference value of the threshold-crossing maximum peak point at the moment k and the threshold-crossing maximum peak point at the moment k-1 is within the set range of the threshold factor alpha of the peak point, the peak point of the kth period is considered to be effective; and if the threshold-crossing maximum peak point exceeds the set peak point threshold factor alpha at the moment k, replacing the peak point output at the moment k with the peak point at the moment k-1.
As explained in the above technical means, in a time unit of an adjacent period, the peak point calculated in the current period and the peak point in the previous period, if the speed does not change in the adjacent period, the peak point will remain unchanged in the adjacent period, however, if the horizontal flying speed of the unmanned aerial vehicle changes in the adjacent period time, the peak point of the current period will change to some extent in the previous period, if the unmanned aerial vehicle is close to the target, the number of points in the current period is smaller than the number of points in the previous period, if the unmanned aerial vehicle is far away from the target, the number of points in the current period is larger than the number of points in the previous period, the variation range of the peak point is the designed threshold factor alpha of the peak point, and the value range selected by the factor mainly depends on the maximum flight speed of the unmanned aerial vehicle in the adjacent period, namely a formula.
Figure GDA0002956842910000092
Wherein v ismaxThe maximum flight speed of the unmanned aerial vehicle is shown, lambda is the millimeter wave radar wavelength, fs is the sampling rate, and N is the number of points of FFT.
However, if the flight environment of the unmanned rotorcraft changes suddenly, the number of peak points corresponding to the threshold may also continuously exceed the designed threshold factor. If the correction is not carried out, after mutation occurs, the threshold-crossing maximum peak point detected in each period exceeds the set threshold factor, and the threshold-crossing maximum peak point coordinate is corrected to the peak point coordinate at the last moment every time, namely, the value before mutation is also kept by the same value, and the value after mutation cannot be adapted. In order to improve the adaptability of the unmanned aerial vehicle to various environments, a peak point mutation accumulation factor phi is introduced for the unmanned aerial vehicle.
And setting a peak point sudden change accumulation factor phi, wherein the peak point sudden change accumulation factor phi is defined as that if b periods are continuously carried out from the moment k, the value range of b is 5-10, and the threshold-crossing maximum peak point is compared with the threshold-crossing maximum peak point of the previous period and exceeds a threshold factor a, the threshold-crossing maximum peak point calculated at the moment k + b is taken as the threshold-crossing maximum peak point at the moment. In order to ensure the real-time performance of tracking, the value of b is 5-10.
And after the threshold-crossing maximum peak point is obtained in the last step, in order to improve the precision of the measurement of the table system value, a spectrum maximum estimation algorithm for improving the distance measurement precision is provided.
Ideally, the frequency spectrum of the echo difference frequency signal has only one spectral line, but actually, in the using process, due to the barrier effect existing in sampling, the spectral line with the maximum amplitude of the discrete frequency spectrum inevitably shifts the position of a spectral peak, so that a certain error exists between the distance value calculated by the peak point and the actual distance. When a spectral peak is shifted, the central spectral line corresponding to the main lobe peak will be shifted to the left or to the right. If the left peak value is larger than the right peak value in the left and right peak values of the threshold-crossing maximum value peak value point, the position of the central spectral line is between the maximum peak value point and the left peak value point, otherwise, the position is between the maximum peak value point and the right peak value point.
Because the spectrum obtained by FFT calculation samples continuous distance spectrum at equal intervals, the maximum point of the spectrum amplitude is necessarily positioned in the main lobe of the curve, and the main lobe has two sampling points. Setting the coordinates of the threshold-crossing maximum peak point A1 as (a1, k1), wherein a1 represents the value of the threshold-crossing maximum peak point, and k1 represents the amplitude value corresponding to the threshold-crossing peak point; the coordinates of the secondary peak points are A3(A3, k3), the coordinate of the central peak point A is (amax, kmax), and e is amax-a1, the point A1, the coordinate of the point A2 symmetric to the point A is (a2, k1) is (a1+2e, k1), and the zero point A4 of the complex envelope is (a4, k1) is (A3+ e, 0);
wherein: a2, a3 and a4 are the values of the over-threshold maximum peak point of the corresponding point, and k3 and k4 are the amplitude values corresponding to the over-threshold peak point of the corresponding point;
a2, A3 and A4 are approximately a straight line, and the linear relationship is as follows:
Figure GDA0002956842910000101
order to
Figure GDA0002956842910000102
Then
Figure GDA0002956842910000103
Setting error E and deviation E to compare, if | E tint<E, the value of the over-threshold peak point at the moment is the value of the required central peak point, if the deviation E is greater than the set error E,
Figure GDA0002956842910000104
beta is a correction factor, the value range is 1.5-1.9, and the correction factor is selected from the following reasons: due to the initial time
Figure GDA0002956842910000105
The coordinate of the point a symmetric point a2 is (a2, k1) — (a1+2E, k1), the abscissa of the point a is symmetric to the abscissa of the point a2 about the maximum peak point under the initial condition, that is, the coordinate of the point a2 is a1+2E, if the deviation E is greater than the set error E, it means that the coordinate of the point a2 is selected too large, that is, the maximum peak point is between a1+2E, and the 2-fold deviation E needs to be reduced. The value-taking principle of the correction factor beta can be selected according to the required E valueIf the required precision of E is not high, the correction factor beta can be selected to be 1.9 for correction, if the required precision of E is high, multiple iterations are possibly required to meet the requirement, the correction factor beta is selected to be a little as possible, and 1.5 can be selected for correction. The value of e calculated by the correction factor is changed to calculate the value amax of the central peak point as a1+ e.
As another embodiment, the method further comprises the steps of: distance tracking: setting a threshold factor epsilon, which is used for limiting the absolute value of the difference between the current distance data H (k) and the distance data H (k-1) appearing in the previous period, so that the absolute value of the difference is not larger than the threshold factor epsilon;
the expression is as follows:
the value of | H (k) | -H (k-1) | is less than or equal to epsilon, and the value range of epsilon is 0.8-1.3;
if the absolute value difference value of the data at the k moment and the absolute value difference value at the k-1 moment are within the range of the set threshold factor epsilon, the peak point of the k-th period is considered to be effective; if the data at time k exceeds the set threshold factor epsilon, the data output at time k is replaced with the data at time k-1.
And setting a sudden change accumulation factor theta, wherein the sudden change accumulation factor theta is defined in that if b periods are continued from the time k, and the data are compared with the data of the previous period and exceed a threshold factor theta, the data obtained by resolving the current time are taken as the data of the current time at the time k + b.
As an embodiment, specifically, in the embodiment, for the distance data which is not subjected to the distance tracking or is subjected to the distance tracking, when outputting, the distance value is output by using a sliding window algorithm for the distance data which is output once;
the data at time k is equal to N in the sliding windowcThe average value of the values after the maximum value and the minimum value are removed is used as the final data output, and the calculation formula is
Figure GDA0002956842910000111
Wherein N iscRepresenting the number of data points employed by the sliding window.
By adopting the peak value tracking algorithm and the tracking algorithm, the abnormal phenomenon of one or more times of data calculation caused by single or multiple times of peak value searching errors can be effectively avoided, for example, in the single peak value searching process, peak value jumping occurs, the peak value difference value between adjacent periods is large, and simultaneously, the large jumping occurs caused by the jumping with the peak value, namely, the jumping range caused by the peak value jumping in the period is far larger than the distance change range caused by one period caused by the speed of the unmanned aerial vehicle. Therefore, the peak tracking and tracking can effectively avoid abnormal values caused by the abnormal peaks, and the stability of the tracked data is effectively improved.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.

Claims (9)

1. A rotor unmanned aerial vehicle anti-collision millimeter wave radar system is characterized by comprising an ARM processing system, a signal generator, a voltage-controlled oscillator, a transmitter, a receiver, a frequency mixer, a signal conditioning circuit and an A/D converter, wherein one end of the ARM processing system is connected with the signal generator, the signal generator is connected with the voltage-controlled oscillator, the voltage-controlled oscillator is respectively connected with the transmitter and the first end of the frequency mixer, the second end of the frequency mixer is connected with the receiver, the third end of the frequency mixer is connected with the signal conditioning circuit, the signal conditioning circuit is connected with the A/D converter, and the A/D converter is connected with the other end of the ARM processing system;
rotor unmanned aerial vehicle anticollision millimeter wave radar system includes following step:
s1, carrying out direct current removal on IQ data acquired by A/D in a channel 1 and a channel 2;
s2, carrying out FFT (fast Fourier transform) on IQ data acquired by A/D (analog to digital) in the channel 1 and the channel 2 after direct current removal, and converting time domain data into frequency data;
s3, performing CFAR threshold detection on the complex modulus value after FFT, outputting a first peak point of a threshold, obtaining frequency values corresponding to an upper sweep frequency and a lower sweep frequency in the channel 1 and an upper sweep frequency value in the channel 2, calculating the frequency values in the channel 1 and the channel 2, and respectively calculating to obtain phases according to the respective upper sweep frequencies;
s4, calculating to obtain the distance of the unmanned aerial vehicle to the front obstacle target by using the frequency value of the upper sweep frequency and the frequency value corresponding to the lower sweep frequency in the channel 1 obtained in the step S3;
s5, respectively calculating the phase positions of the channel 1 and the channel 2 obtained in the step S3 according to the respective upper sweep frequencies to obtain azimuth angles;
the peak processing method of CFAR threshold detection in step S3 includes:
setting a peak point threshold factor α for limiting the absolute value of the difference between the detected threshold-crossing maximum peak point and the maximum peak point appearing in the previous cycle, so that the absolute value of the difference is not greater than the peak point threshold factor α:
the expression is as follows:
|L_max(k)-L_max(k-1)|≤α；
Figure FDA0002955333050000011
wherein: l _ max (k) is the maximum peak point coordinate of the threshold passing of the k period, L _ max (k-1) is the maximum peak point coordinate of the last period, and k represents the kth moment; v. ofmaxThe maximum flight speed of the unmanned aerial vehicle is shown, lambda is the wavelength of the millimeter wave radar, fs is the sampling rate, and N is the number of points of FFT;
if the absolute value difference value of the threshold-crossing maximum peak point at the moment k and the threshold-crossing maximum peak point at the moment k-1 is within the set range of the threshold factor alpha of the peak point, the peak point of the kth period is considered to be effective; and if the threshold-crossing maximum peak point exceeds the set peak point threshold factor alpha at the moment k, replacing the peak point output at the moment k with the peak point at the moment k-1.
2. The anti-collision millimeter wave radar system for the unmanned rotorcraft according to claim 1, wherein the ARM processing system comprises an ARM processing module, a power supply module, a serial port module and a CAN module, and the AMR processing module enables four paths of I/Q intermediate frequency signals output by the signal conditioning circuit to enter four paths of AD acquisition channels carried by the ARM chip through the signal conditioning circuit and to be output through the serial port module or the CAN module.
3. The method for signal processing for a mm-wave radar system for unmanned rotorcraft collision avoidance according to claim 1, wherein step S3 is performed in step S3
If the peak coordinate of the first threshold point of the up-scan band in the channel 1 is p1_ up, the frequency value corresponding to the point is f1_ up, the corresponding FFT-transformed data is a _ p1_ up +1j b _ p1_ up, and the phase is
Figure FDA0002955333050000021
The peak coordinate of the first threshold point of the upper scanning frequency band in the channel 2 is p2_ up, the frequency value corresponding to the point is f2_ up, the corresponding FFT-transformed data is a _ p2_ up +1j × b _ p2_ up, and the phase is
Figure FDA0002955333050000022
Setting the peak value coordinate of the first threshold passing point of the lower sweep frequency segment in the channel 1 as p1_ down, and setting the frequency value corresponding to the point as f1_ down;
wherein: a represents the data value of the I path, b represents the data value of the Q path, a _ p1 represents that in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, a _ p2 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p2, b _ p1 represents in the array formed by a + j × b, the corresponding coordinate of the peak point of the threshold is p1, b _ p2 represents in the array formed by a + j × b, and the corresponding coordinate of the peak point of the threshold is p 2.
4. Signal processing of unmanned gyroplane millimeter wave radar system according to claim 1The method is characterized in that, in the step S4, the frequency value f1_ up of the upper sweep frequency and the frequency value f1_ down corresponding to the lower sweep frequency in the channel 1 obtained in the step S3 are calculated according to a formula
Figure FDA0002955333050000031
Calculating to obtain the distance of the unmanned aerial vehicle to the obstacle target, wherein T is a triangular wave period, B is a frequency modulation bandwidth, and c is the speed of light;
according to the formula
Figure FDA0002955333050000032
Calculating to obtain the speed of the unmanned aerial vehicle forward obstacle target, wherein f0Is the center frequency.
5. The signal processing method for the mm-wave radar system for unmanned gyroplane collision avoidance according to claim 1, wherein in step S5, the phases calculated from the respective up-swept frequencies in channel 1 and channel 2 obtained in step S3 are respectively calculated
Figure FDA0002955333050000033
And
Figure FDA0002955333050000034
according to a calculation formula
Figure FDA0002955333050000035
Obtaining the phase difference delta psi; according to the formula
Figure FDA0002955333050000036
And calculating the azimuth angle, wherein d is the antenna spacing and lambda is the radar wavelength.
6. The method for processing signals of the millimeter wave radar system for preventing collision of the unmanned gyroplane according to claim 1, wherein the step S1 is to remove direct current by: calculating the mean value of upper and lower frequency sweep IQ data collected by AD in the channel 1, and subtracting the calculated mean value from each data point in IQ; and calculating the mean value of the upper frequency sweep IQ data collected by the AD in the channel 2, and subtracting the calculated mean value from each data point of the IQ.
7. The method of signal processing for a rotary wing drone anti-collision millimeter wave radar system of claim 1, further comprising a step of windowing after the step of removing direct current in step S2.
8. The signal processing method for the unmanned gyroplane millimeter wave radar system for collision avoidance according to claim 1, further comprising step s6. filtering and tracking, and predicting the distance and velocity values at the next measurement time.
9. The method of signal processing for a mm-wave radar system for unmanned gyroplane collision avoidance according to claim 8, wherein the filtering is performed using an α - β filter having a constant gain filter with a prediction equation of
X(k+1/k)＝ΦX(k/k)；
The filter equation is:
X(k+1/k+1)＝X(k+1/k)+K[Z(k+1)-H(k+1/k)]；
wherein X (k/k) is a filter value at the time k, X (k +1/k) is a predicted value of the time k to the next time, and Z (k) is an observed value at the time k;
when the target motion equation adopts a constant velocity model, the constant gain matrix K is [ alpha, beta/T ]]TIts state transition matrix
Figure FDA0002955333050000041
The measurement matrix of the model is H ═ 1, 0]；
Figure FDA0002955333050000042
Figure FDA0002955333050000043
Wherein: alpha is more than 0 and less than 1, beta is more than 0 and less than 1.
CN201610725771.6A 2016-08-25 2016-08-25 Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method Active CN107783125B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610725771.6A CN107783125B (en) 2016-08-25 2016-08-25 Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610725771.6A CN107783125B (en) 2016-08-25 2016-08-25 Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method

Publications (2)

Publication Number Publication Date
CN107783125A CN107783125A (en) 2018-03-09
CN107783125B true CN107783125B (en) 2021-04-20

Family

ID=61438747

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610725771.6A Active CN107783125B (en) 2016-08-25 2016-08-25 Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method

Country Status (1)

Country Link
CN (1) CN107783125B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110220853B (en) * 2019-04-29 2022-04-12 西安科技大学 Laser spectrum telemetering and early warning unmanned aerial vehicle system for comprehensive pipe gallery and positioning method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102486537A (en) * 2010-12-05 2012-06-06 中国科学院沈阳自动化研究所 Millimeter wave radar anticollision detection apparatus
CN102798863A (en) * 2012-07-04 2012-11-28 西安电子科技大学 Road central isolation belt detection method based on automobile anti-collision radar
CN102890272A (en) * 2012-11-05 2013-01-23 中国航天科工集团第二研究院二十三所 Method for processing millimeter wave cloud radar signal
CN103630888A (en) * 2013-02-27 2014-03-12 中国科学院电子学研究所 High-precision real-time microwave velocity and distance measuring device based on symmetrical triangle LFMCW (Linear Frequency Modulation Continuous Wave) radar
CN103913742A (en) * 2014-04-25 2014-07-09 桂林电子科技大学 Automotive anti-collision radar system with two receiving antennas and operating method
CN105445714A (en) * 2015-11-24 2016-03-30 大连楼兰科技股份有限公司 Automobile forward direction anticollision system signal processing method
DE102015210676A1 (en) * 2014-11-19 2016-05-19 Mitsubishi Electric Corporation FMCW RADAR DEVICE AND FMCW RADAR SIGNAL PROCESSING METHOD

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102486537A (en) * 2010-12-05 2012-06-06 中国科学院沈阳自动化研究所 Millimeter wave radar anticollision detection apparatus
CN102798863A (en) * 2012-07-04 2012-11-28 西安电子科技大学 Road central isolation belt detection method based on automobile anti-collision radar
CN102890272A (en) * 2012-11-05 2013-01-23 中国航天科工集团第二研究院二十三所 Method for processing millimeter wave cloud radar signal
CN103630888A (en) * 2013-02-27 2014-03-12 中国科学院电子学研究所 High-precision real-time microwave velocity and distance measuring device based on symmetrical triangle LFMCW (Linear Frequency Modulation Continuous Wave) radar
CN103913742A (en) * 2014-04-25 2014-07-09 桂林电子科技大学 Automotive anti-collision radar system with two receiving antennas and operating method
DE102015210676A1 (en) * 2014-11-19 2016-05-19 Mitsubishi Electric Corporation FMCW RADAR DEVICE AND FMCW RADAR SIGNAL PROCESSING METHOD
CN105445714A (en) * 2015-11-24 2016-03-30 大连楼兰科技股份有限公司 Automobile forward direction anticollision system signal processing method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
基于FPGA和DSP的频域恒虚警设计;葛尧等;《计算机工程与应用》;20071231;第163-164、187页 *
基于双通道射频前端的汽车变道辅助雷达设计与实现;吴鹏飞;《中国优秀硕士学位论文全文数据库信息科技辑》;20150215;第46-50页 *
基于梯形波FMCW雷达的多目标探测技术的研究;薛效龙;《中国优秀硕士学位论文全文数据库 信息科技辑》;20140915;第6页 *

Also Published As

Publication number Publication date
CN107783125A (en) 2018-03-09

Similar Documents

Publication Publication Date Title
CN107783133B (en) Anti-collision system and anti-collision method for fixed-wing unmanned aerial vehicle of millimeter wave radar
CN107783107A (en) The millimetre-wave radar altimeter of plant protection rotor wing unmanned aerial vehicle
WO2018195876A1 (en) Distance determination method for microwave radar, microwave radar, computer storage medium, unmanned aerial vehicle and control method thereof
CN110187332B (en) Low altitude defense radar system and method based on digital beam forming technology
CN107783121B (en) Unmanned automobile anti-collision radar system signal processing system and method based on combined waveform
CN107783123B (en) System and method for processing collision-proof millimeter wave radar signal in complex environment of unmanned vehicle
CN106019285B (en) Millimeter wave radar of micro unmanned aerial vehicle
CN107783128B (en) Multi-target anti-collision system of fixed-wing unmanned aerial vehicle based on millimeter wave radar
US20140253370A1 (en) Weather radar apparatus, observation sequence generation method, and observation sequence generation program
CN107783132B (en) Anti-collision millimeter wave radar system for automatic driving automobile and signal processing method
CN106019280B (en) FMCW SAR imaging methods and device based on range Doppler correction
CN107783114A (en) The remote complex environment anticollision MMW RADAR SIGNAL USING processing system of rotor wing unmanned aerial vehicle and method
CN107783099A (en) Rotor wing unmanned aerial vehicle short distance CAS signal processing system and method based on combined waveform
CN107783125B (en) Rotor unmanned aerial vehicle anti-collision millimeter wave radar system and signal processing method
CN107783090B (en) Millimeter wave radar-based radar signal processing method for collision avoidance system of fixed-wing unmanned aerial vehicle
CN107783124B (en) Rotor unmanned aerial vehicle complex environment anti-collision radar system based on combined waveform and signal processing method
CN107783129B (en) Anti-collision millimeter wave radar signal processing method for rotor unmanned aerial vehicle
CN110927724B (en) Intelligent monitoring system and method for millimeter wave radar debris flow
WO2021087706A1 (en) Radar system, movable platform and radar system control method
CN112816957A (en) High every single move angle scattering test system based on unmanned aerial vehicle
CN107783077B (en) Method for processing threshold-passing peak point
CN107783102B (en) Peak tracking method for height signal of unmanned aerial vehicle
CN107783100B (en) Rotor unmanned aerial vehicle short-distance anti-collision system signal processing method based on combined waveform
CN115436940A (en) Sparse sliding spotlight SAR imaging mode realization method and device
CN107783127B (en) Rotor unmanned aerial vehicle anti-collision millimeter wave radar signal processing method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant