CN117348045A - Optimization method and device for selecting reflected signals of multimode GNSS-R receiver - Google Patents
Optimization method and device for selecting reflected signals of multimode GNSS-R receiver Download PDFInfo
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- G—PHYSICS
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G—PHYSICS
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
- G01S19/25—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS
- G01S19/258—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS relating to the satellite constellation, e.g. almanac, ephemeris data, lists of satellites in view
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The invention discloses an optimization method and device for selecting reflected signals of a multimode GNSS-R receiver. And the data of the reflected signals are screened and fused, so that the revisit period can be greatly shortened, and the data rate of the load is reduced. The invention can process the signals of different satellites and obtain the fused DDM data product, thereby greatly reducing the storage load of the multimode GNSS-R receiver and facilitating the data downloading.
Description
Technical Field
The invention relates to the technical field of reflected signal selection, in particular to a method and a device for optimizing reflected signal selection of a multimode GNSS-R receiver.
Background
The GNSS-R technology is a novel technology in the current remote sensing detection field, and receives reflected signals from GNSS satellites on a scattering surface in a mode of being carried on small satellites or effective loads, an airborne mode, a shore mode and the like to acquire physical information such as ground roughness, reflectivity and the like, so that detection of the earth, the land and the sea is realized. GNSS satellites are of a wide variety including GPS in the united states, beidou in china, galileo in europe, GLONASS in russia, and quasi-zenith satellite systems in japan. The GNSS signals can be used for researching climate change, ocean roughness and salinity, soil humidity, ice, wind speed, disaster monitoring, atmospheric and ionosphere measurement and the like, and have a certain application prospect.
In recent 20 years, multiple satellites with GNSS-R loading have been launched in multiple countries, such as foreign TDS-1, UK-DMC and CYGNSS constellations, and domestic wind first and wind cloud third E stars. The CYGNSS constellation has 8 observation satellites and measurements are made based on all-weather GPS scattering. And the national Fengyun No. E, F star is a reflected signal for receiving Beidou, GPS and Galileo.
The current foreign GNSS-R receivers mostly adopt single-mode receivers, and all the receivers process the same GNSS satellite signals though having a certain number of reflection channels, so that data information from other satellites is lost. The domestic detection load of the third wind cloud is multimode, and mainly aims at Beidou and GPS signals. But the data products of the two are separated, which requires a large amount of independent storage space. And when the mirror reflection points of different satellites are received in the same area, a larger data storage space is required if the generated data products are stored, and overload of small satellites or payloads and incompatibility of data rates are caused.
Disclosure of Invention
In order to solve at least one of the above problems in the prior art, the present invention provides a method and apparatus for optimizing reflected signal selection of a multimode GNSS-R receiver.
According to an aspect of the present invention, there is provided a method for optimizing selection of reflected signals of a multimode GNSS-R receiver, comprising:
step one: receiving direct reflection signals from a plurality of GNSS-R satellites at the current moment from a plurality of channels of the multimode GNSS-R receiver, and determining the signal type of the direct reflection signals received by each channel;
step two: performing ephemeris decoding on the direct reflection signals received by each channel, and determining the position information of each GNSS-R satellite at the current time; determining the position information of the multimode GNSS-R receiver at the current moment according to the navigation information of the multimode GNSS-R receiver;
step three: determining the positions of specular reflection points of a plurality of channels based on the position information of each GNSS-R satellite and the position information of the multimode GNSS-R receiver at the current moment;
step four: judging whether the specular reflection points of the channels are overlapped according to the specular reflection point positions of the channels, screening signals of the direct reflection signals of the channels corresponding to the overlapped specular reflection points, and selecting the direct reflection signals with optimal performance for subsequent processing;
step five: judging and classifying the specular reflection points of all channels in a polling mode after no coincident specular reflection points exist or signal screening is carried out in the fourth step, and separating the specular reflection point channels observed in effective time from the specular reflection point channels not observed;
step six: performing secondary signal screening on the observed data stored in the specular reflection point channel observed in the effective time in the fifth step, and selecting the final observed data with optimal performance as the current specular reflection point channel;
step seven: for the specular reflection point channels which are not observed in the effective time in the step five and the specular reflection point channels which are subjected to secondary signal screening in the step six and replace observed data, checking the signal types of the direct reflection signals received by each specular reflection point channel, and inputting the direct reflection signals of different signal types into corresponding processing channels for processing;
step eight: and processing the reflected signals output by the processing channels into DDM data products and storing related information.
Optionally, the decoding the ephemeris of the direct reflection signal received by each channel to determine the position information of each GNSS-R satellite at the current time includes:
carrying out carrier synchronization, bit synchronization and frame synchronization operation on the direct-reflection signals received by each channel, and decoding the signals by using a decoding algorithm built in a multimode GNSS-R receiver to obtain ephemeris information of each GNSS-R satellite;
and calculating the position information of each GNSS-R satellite at the current moment according to the ephemeris information of each GNSS-R satellite.
Optionally, the signal screening is performed on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and the subsequent processing is performed on the direct reflection signals with optimal performance, including:
performing signal quality evaluation on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and determining signal quality evaluation values of the coincident specular reflection points;
determining signal scores of the coincident specular reflection points according to the signal quality evaluation values and preset evaluation weights;
sorting the direct reflection signals of the channels corresponding to the overlapped specular reflection points according to the signal scores;
the direct-reflected signal with the highest signal score is taken as the direct-reflected signal with the best performance.
Optionally, the evaluation weight is set by:
through analysis of inversion accuracy of the reflected signals and the generated DDM data products, an evaluation strategy of the direct reflected signals adopts 5 physical quantities of elevation angle theta, polarization matching degree sigma, reflected signal frequency f, reflected signal intensity beta, distance from a specular reflection point to a multimode GNSS-R receiver and the GNSS-R satellite and delta of the GNSS-R satellite as signal influence factors;
weights of 5 physical quantities θ, σ, f, β, Δδ are set to (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 )，For the evaluation value, (y) 1 ,y 2 ,y 3 ,y 4 ,y 5 ) Constructing a deep learning model for the quantizers corresponding to the 5 physical quantities, wherein the expression of the deep learning model is as follows:
performing deep learning model fitting through multiple groups of physical quantity and weight data, and outputting evaluation values of the deep learning modelAnd feeding back to the weight input, and finally obtaining the optimal parameter value of the estimated weight.
According to still another aspect of the present invention, there is provided an optimizing apparatus for selecting reflected signals of a multimode GNSS-R receiver, comprising:
the signal acquisition module is used for receiving the direct reflection signals from the GNSS-R satellites at the current moment from the channels of the multimode GNSS-R receiver and determining the signal type of the direct reflection signals received by each channel;
the first position determining module is used for performing ephemeris decoding on the direct reflection signals received by each channel and determining the position information of each GNSS-R satellite at the current time; determining the position information of the multimode GNSS-R receiver at the current moment according to the navigation information of the multimode GNSS-R receiver;
the second position determining module is used for determining the positions of the specular reflection points of the multiple channels based on the position information of each GNSS-R satellite at the current moment and the position information of the multimode GNSS-R receiver;
the signal screening module is used for judging whether the specular reflection points of the channels are overlapped according to the specular reflection point positions of the channels, screening the signals of the direct reflection signals of the channels corresponding to the overlapped specular reflection points, and selecting the direct reflection signals with optimal performance for subsequent processing;
the signal judging module is used for judging and classifying the specular reflection points of all channels in a polling mode after no coincident specular reflection points exist or the signal screening is carried out in the fourth step, and separating the specular reflection point channels observed in the effective time from the specular reflection point channels not observed;
the signal selection module is used for carrying out secondary signal screening on the observed data stored in the specular reflection point channel observed in the effective time in the step five, and selecting the final observed data with optimal performance as the current specular reflection point channel;
the signal input module is used for checking the signal types of the direct reflection signals received by each specular reflection point channel, and inputting the direct reflection signals of different signal types into the corresponding processing channels for processing, wherein the specular reflection point channels are not observed in the effective time in the step five, and the specular reflection point channels of the observed data are replaced after secondary signal screening in the step six;
and the signal processing module is used for processing the reflected signals output by the processing channels into DDM data products and storing related information.
Optionally, the first position determining module is specifically configured to:
carrying out carrier synchronization, bit synchronization and frame synchronization operation on the direct-reflection signals received by each channel, and decoding the signals by using a decoding algorithm built in a multimode GNSS-R receiver to obtain ephemeris information of each GNSS-R satellite;
and calculating the position information of each GNSS-R satellite at the current moment according to the ephemeris information of each GNSS-R satellite.
Optionally, the signal screening module is specifically configured to:
performing signal quality evaluation on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and determining signal quality evaluation values of the coincident specular reflection points;
determining signal scores of the coincident specular reflection points according to the signal quality evaluation values and preset evaluation weights;
sorting the direct reflection signals of the channels corresponding to the overlapped specular reflection points according to the signal scores;
the direct-reflected signal with the highest signal score is taken as the direct-reflected signal with the best performance.
Optionally, the apparatus further comprises an evaluation weight setting module for setting the evaluation weight by:
through analysis of inversion accuracy of the reflected signals and the generated DDM data products, an evaluation strategy of the direct reflected signals adopts 5 physical quantities of elevation angle theta, polarization matching degree sigma, reflected signal frequency f, reflected signal intensity beta, distance from a specular reflection point to a multimode GNSS-R receiver and the GNSS-R satellite and delta of the GNSS-R satellite as signal influence factors;
weights of 5 physical quantities θ, σ, f, β, Δδ are set to (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 )，For the evaluation value, (y) 1 ,y 2 ,y 3 ,y 4 ,y 5 ) Constructing a deep learning model for the quantizers corresponding to the 5 physical quantities, wherein the expression of the deep learning model is as follows:
performing deep learning model fitting through multiple groups of physical quantity and weight data, and outputting evaluation values of the deep learning modelAnd feeding back to the weight input, and finally obtaining the optimal parameter value of the estimated weight.
According to a further aspect of the present invention there is provided a computer readable storage medium storing a computer program for performing the method according to any one of the above aspects of the present invention.
According to still another aspect of the present invention, there is provided an electronic device including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any of the above aspects of the present invention.
According to the invention, on the basis of the multimode GNSS-R receiver, screening and optimizing are carried out on different satellite signals received by a plurality of channels, the performance of the obtained reflected signals is better in effective time, and the generated DDM data product is more accurate. And the data of the reflected signals are screened and fused, so that the revisit period can be greatly shortened, and the data rate of the load is reduced. The invention can process the signals of different satellites and obtain the fused DDM data product, thereby greatly reducing the storage load of the multimode GNSS-R receiver and facilitating the data downloading.
Drawings
Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of GNSS-R receiver signal processing provided by an exemplary embodiment of the present invention;
FIG. 2 is a flowchart of a signal screening algorithm provided in an exemplary embodiment of the present invention;
FIG. 3 is a reference diagram of signal scoring and weight setting provided by an exemplary embodiment of the present invention;
FIG. 4 is a schematic diagram of an optimization apparatus for selecting reflected signals of a multimode GNSS-R receiver according to an exemplary embodiment of the present invention;
fig. 5 is a structure of an electronic device provided in an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
Fig. 1 is a schematic flow chart of an optimization method for selecting reflected signals of a multimode GNSS-R receiver according to the present invention. As shown in fig. 1, the optimization method for selecting reflected signals of the multimode GNSS-R receiver includes:
fig. 1 is a main flow chart of the technical scheme of the present invention. As shown in FIG. 1, the preferred method for reflecting signals by the multimode GNSS-R receiver according to the present invention comprises the following steps:
step one: when the multimode antenna receives direct reflection signals from a plurality of satellites at a certain moment, the type and the number of the signals are firstly determined. The multimode antenna can acquire information about the received signals by having a plurality of independent reception channels, each of which can simultaneously receive signals of different satellites, and analyzing the signals received by each of the channels. Meanwhile, for satellite navigation systems (Beidou, GPS and the like), the code phase of the signals can also be used for identifying the signals of different satellites, and the code phase information of the signals is obtained by performing correlation analysis (usually adopting a parallel code phase acquisition method) with known satellite codes.
Step two: the direct signal is subjected to operations such as carrier synchronization, bit synchronization, frame synchronization and the like, and the signal is decoded by using a decoding algorithm built in the GNSS-R receiver, wherein the decoding algorithm comprises information such as navigation message decoding, ephemeris, clock error decoding and the like.
Step three: ephemeris includes orbital parameters, position information, clock bias, etc. of the satellites. And calculating the accurate position of the GNSS satellite at the current time by using a data model according to the ephemeris information.
Navigation information is used for navigation solution, and the three-dimensional position (latitude, longitude, altitude) and speed of the receiver itself are calculated.
GNSS signals may be subject to various errors during propagation, such as atmospheric delays, multipath interference, clock skew, and the like. The receiver needs to take these errors into account and correct them to improve the accuracy of the position.
And (3) under the WGS-84 coordinate system, inputting the three-dimensional position of the receiver and the accurate position of the GNSS satellite obtained in the step (2) into a specular reflection point prediction algorithm for position calculation, and obtaining the position of the specular reflection point. The specular reflection point calculation method is as follows:
first assume that the receiver is located at (X r ,Y r ,Z r ) The position of the GNSS satellite is (X g ,Y g ,Z g ) At this time, by constructing the reflection geometry, the geometrical position (X k ,Y k ,Z k )。
Converting this position into a longitude and latitude representation
A and b in the formula are a long half shaft and a short half shaft of an ellipsoid of earth respectively and are constants; lambda (lambda) k 、φ k The longitude and latitude of the corresponding location, respectively.
When a signal is reflected off the ground, the path delay of the reflected signal can be expressed as:
in order to solve the specular reflection point, S needs to be minimized, so the corresponding specular reflection point position can be obtained by solving the extremum of the above. There are many methods for extremum determination, such as newton's method, gradient descent method, simulated annealing algorithm, genetic algorithm, etc.
Step four: and obtaining the positions of the specular reflection points of all the channels at the moment, and judging whether the positions of the specular reflection points have the same area or not, namely judging whether the specular reflection points of the channels are overlapped or not.
This step is to solve one theoretical situation, that is, at a certain moment, the GNSS-R receiver receives the reflected signals of multiple satellites at the same time, and the positions of the specular reflection points formed by the signals after processing on the ground coincide, that is, on the same point.
This may occur under certain geographical conditions and circumstances or the window of the area where the reflected signal blinks may be large, with specular reflection points at two different locations within this range.
If specular reflection points in the same area (i.e. overlapping) exist, signals of the corresponding channels need to be subjected to signal screening, signals with optimal performance are selected for subsequent processing, other signals are discarded, and only one signal with the same specular reflection point position is ensured.
The signal filtering algorithm is a core algorithm of the present invention, and a specific flow will be described later.
Step five: when the specular reflection points of the same area do not exist or after signal screening is carried out in the fourth step, the specular reflection points of all channels are judged and classified in a polling mode, and the specular reflection point channels observed in the effective time and the specular reflection point channels not observed are separated for subsequent processing.
Step six: and step five, for the channel of the specular reflection point observed in the effective time, secondary signal screening is required to be carried out on the channel of the specular reflection point and the observed data stored at the specular reflection point, and the final observed data with the optimal performance at the specular reflection point is selected.
Step seven: and (3) checking the signal types of the specular reflection point channels which are not observed in the effective time in the step five and the signal channels which are subjected to secondary signal screening and replace the observed data in the step (6), and inputting different types of reflected signals into corresponding processing channels for processing.
Step eight: and the reflected signals are processed into DDM data products, so that the data can be conveniently compressed and downloaded for the inversion of the follow-up meteorological data.
The generated data product needs to store certain signal characteristics and related information so as to carry out secondary data screening in the step six.
Wherein, the relevant information stored includes: elevation angle of the GNSS satellite, polarization matching of the reflected signal, wavelength, signal frequency, signal strength, and distance sum of specular reflection point to the receiver and GNSS satellite.
The above steps are implemented on the premise of ensuring the timeliness of the product, namely that the geographic state of the specular reflection point is not changed in a certain time period. At present, the time resolution of the sea surface wind field inversion based on the GNSS-R technology is about 1 hour, and the inversion of soil humidity, sea ice and the like is about 24 hours, so that different time slices are required to be set for different applications, and the reflection signal optimization step can be performed in the same time slice.
Fig. 2 is a flow chart of a signal screening algorithm. As shown in fig. 2, the signal screening algorithm according to the present invention includes the following steps:
1. and a data acquisition stage: reflected signal data from a variety of satellites is received, including parameters such as signal strength, doppler shift, signal to noise ratio, elevation angle of the satellite, etc.
2. Signal quality evaluation: the signal-to-noise ratio (SNR) of each signal, doppler shift stability, reflected signal frequency, degree of polarization matching, etc. are calculated. Signal characteristics such as phase stability, coherence time, etc. can also be calculated.
3. Parameter setting: threshold values and weights of evaluation criteria are set, and trade-off relationships of different signal characteristics.
4. Signal scoring: each signal is assigned a composite score that can be calculated based on the evaluation of the different signal characteristics and the weights.
5. Signal sequencing: all signals were ranked according to score from high to low according to signal score.
6. Optimal signal selection: the signal with the highest score is selected as the best signal.
7. Updating and adjusting in real time: in operation, the observation position is continuously monitored, and the threshold value and the weight of the evaluation standard are dynamically adjusted for different observation points (such as ocean, land and the like).
This algorithm flow can be refined and customized according to the application requirements. Each step may require some specific calculation method and algorithm to implement. In practical applications, factors such as processing real-time data, algorithm efficiency and the like need to be considered.
In order to better implement the algorithm scheme described above, the setting of the evaluation weights in step 3 will be described in detail herein.
By analyzing the reflected signals and generating the inversion accuracy of the DDM diagram, the evaluation strategy of the signals adopts 5 physical quantities of the elevation angle theta, the polarization matching degree sigma, the reflected signal frequency f, the reflected signal intensity beta, the distance between a specular reflection point and a receiver and a GNSS satellite and delta as signal influence factors.
1) The satellite elevation angle theta can influence the resolution and sensitivity of the DDM, and a larger incident angle can make a received reflected signal more sensitive to small changes of the surface characteristics, thereby being beneficial to capturing finer surface motion changes and enlarging the Fresnel reflection coefficient and the radar scattering cross section. The range of elevation angle θ should be greater than 50 degrees based on empirical values.
2) The polarization matching degree σ refers to the degree of matching between the polarization direction of the received reflected signal and the polarization direction of the receiver. Better polarization matching can improve the strength and quality of the signal, thereby reducing the impact of noise on DDM. The polarization matching degree (0 < sigma < 1) can be calculated by judging the polarization direction of the reflected signal through a polarization filter.
3) Higher reflected signal frequencies f may provide higher doppler shifts, thereby increasing the resolution and sensitivity of the DDM.
4) The reflected signal strength beta directly affects the quality of the generated DDM product, and the higher the signal strength is, the stronger the noise resistance is.
5) The greater the specular reflection point distance to the receiver and GNSS satellites and the better the delta.
The weight of each physical quantity is set to (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 )
The above formula is a comprehensive scoring formula of the signal,to evaluate the factors, the signals are ranked by comparing their sizes. (y) 1 ,y 2 ,y 3 ,y 4 ,y 5 ) For the quantizer corresponding to each physical quantity, specific quantization needs to be discussed according to practical application.
Regarding the weight (x 1 ,x 2 ,x 3 ,x 4 ,x 5 ) The setting of (2) can use a deep learning method to perform model fitting through multiple groups of data. And feeding back the output of the model to the input of the weight, and finally obtaining the optimal weight setting parameter value. The reference diagram is shown in fig. 3.
The invention has the advantages that when the multimode GNSS-R receiver receives signals of various GNSS satellites simultaneously, the receiver needs to process direct signals in each receiving channel, acquire ephemeris, perform position calculation, construct reflection geometry and determine the position of a specular reflection point. After the position of the specular reflection point is obtained, the method is one of the innovative key points of the invention, and the signal receiving flow is designed. For the specular reflection point positions of each channel, two judging and classifying modes are set, one is to judge whether the specular reflection point positions are overlapped or not. The other is to determine whether the specular reflection point is observed. By these two determinations, the signal is classified into three types, one is a signal constituting a specular reflection point which is not overlapped and is not observed, and one is a signal constituting a specular reflection point which is not overlapped and is observed. And a signal in which the specular reflection points coincide. Corresponding processing modes are adopted for all three signals, so that the three signals are fused into a data product.
Furthermore, the invention refers to the signal physical quantity affecting the DDM precision of the zero-order data product in the signal screening algorithm, carries out quantization weighting on each physical quantity, finally obtains the comprehensive score of each signal, and can obtain the optimal processing signal through sequencing. The design of the evaluation index considers various factors, and combines anti-noise performance, spatial resolution and precision, fresnel reflection coefficient and the like. Finally, the setting of the weighted value adopts a deep learning method, and different settings of the weighted value are required for different reflecting surface media.
In summary, the invention discloses an optimization method for selecting reflected signals of a multimode GNSS-R receiver, which is based on the multimode receiver, performs screening optimization on different satellite signals received by a receiving channel, obtains better reflected signal performance in effective time, and generates more accurate DDM data products. The screening algorithm is a core algorithm of the invention, adopts a weighted quantization mode, performs normalization processing on the physical quantity of the signal affecting the DDM precision, distributes a comprehensive score for the signal of each channel through transverse comparison, and selects the signal with the best score for subsequent processing. The invention can process the signals of different satellites and obtain the fused DDM data product, thereby greatly reducing the storage load of the multimode GNSS-R receiver and facilitating the data downloading.
Exemplary apparatus
FIG. 4 is a schematic diagram of an optimization apparatus for selecting reflected signals of a multimode GNSS-R receiver according to an exemplary embodiment of the invention. As shown in fig. 4, the apparatus 400 includes:
a signal acquisition module 410, configured to receive, from a plurality of channels of the multimode GNSS-R receiver, direct reflected signals from a plurality of GNSS-R satellites at a current moment, and determine a signal type of the direct reflected signal received by each channel;
a first position determining module 420, configured to perform ephemeris decoding on the direct-reflection signal received by each channel, and determine position information of each GNSS-R satellite at the current time; determining the position information of the multimode GNSS-R receiver at the current moment according to the navigation information of the multimode GNSS-R receiver;
a second position determining module 430, configured to determine positions of specular reflection points of the multiple channels based on the position information of each GNSS-R satellite at the current time and the position information of the multimode GNSS-R receiver;
the signal screening module 440 is configured to determine whether the specular reflection points of the multiple channels overlap according to the specular reflection point positions of the multiple channels, and perform signal screening on the direct reflection signals of the channels corresponding to the overlapped specular reflection points, and select the direct reflection signal with the optimal performance for subsequent processing;
the signal judging module 450 is configured to judge and classify the specular reflection points of all channels by means of polling after no coincident specular reflection points exist or signal screening is performed in the fourth step, so as to separate the specular reflection point channels observed in the effective time from the specular reflection point channels not observed;
the signal selection module 460 is configured to perform secondary signal screening on the observed data stored in the specular reflection point channel observed in the effective time in the fifth step, and select the final observed data with the optimal performance as the current specular reflection point channel;
the signal input module 470 is configured to check the signal types of the direct reflection signals received by each specular reflection point channel, and input the direct reflection signals of different signal types into corresponding processing channels for processing, for the specular reflection point channel that is not observed in the effective time in the fifth step and the specular reflection point channel that is subjected to secondary signal screening and replaces the observation data in the sixth step;
the signal processing module 480 is configured to process the reflected signal output by the processing channel into a DDM data product and store related information.
Optionally, the first location determining module 420 is specifically configured to:
carrying out carrier synchronization, bit synchronization and frame synchronization operation on the direct-reflection signals received by each channel, and decoding the signals by using a decoding algorithm built in a multimode GNSS-R receiver to obtain ephemeris information of each GNSS-R satellite;
and calculating the position information of each GNSS-R satellite at the current moment according to the ephemeris information of each GNSS-R satellite.
Optionally, the signal filtering module 440 is specifically configured to:
performing signal quality evaluation on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and determining signal quality evaluation values of the coincident specular reflection points;
determining signal scores of the coincident specular reflection points according to the signal quality evaluation values and preset evaluation weights;
sorting the direct reflection signals of the channels corresponding to the overlapped specular reflection points according to the signal scores;
the direct-reflected signal with the highest signal score is taken as the direct-reflected signal with the best performance.
Optionally, the apparatus 400 further comprises an evaluation weight setting module for setting the evaluation weight by:
through analysis of inversion accuracy of the reflected signals and the generated DDM data products, an evaluation strategy of the direct reflected signals adopts 5 physical quantities of elevation angle theta, polarization matching degree sigma, reflected signal frequency f, reflected signal intensity beta, distance from a specular reflection point to a multimode GNSS-R receiver and the GNSS-R satellite and delta of the GNSS-R satellite as signal influence factors;
weights of 5 physical quantities θ, σ, f, β, Δδ are set to (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 )，For the evaluation value, (y) 1 ,y 2 ,y 3 ,y 4 ,y 5 ) Constructing a deep learning model for the quantizers corresponding to the 5 physical quantities, wherein the expression of the deep learning model is as follows:
performing deep learning model fitting through multiple groups of physical quantity and weight data, and outputting evaluation values of the deep learning modelFeedback to the weight input to finally obtainThe parameter value of the best evaluation weight.
The optimizing device for selecting the reflected signals of the multimode GNSS-R receiver according to the embodiment of the present invention corresponds to the optimizing method for selecting the reflected signals of the multimode GNSS-R receiver according to another embodiment of the present invention, and is not described herein.
Exemplary electronic device
Fig. 5 is a structure of an electronic device provided in an exemplary embodiment of the present invention. As shown in fig. 5, the electronic device 50 includes one or more processors 51 and memory 52.
The processor 51 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
Memory 52 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 51 to implement the method of information mining historical change records and/or other desired functions of the software program of the various embodiments of the present invention described above. In one example, the electronic device may further include: an input device 53 and an output device 54, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 53 may also include, for example, a keyboard, a mouse, and the like.
The output device 54 can output various information to the outside. The output device 54 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device that are relevant to the present invention are shown in fig. 5 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium, having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in a method of mining history change records according to various embodiments of the present invention described in the "exemplary methods" section above in this specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention is not necessarily limited to practice with the above described specific details.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, systems, apparatuses, systems according to the present invention are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, systems, apparatuses, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It is also noted that in the systems, devices and methods of the present invention, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. An optimization method for selecting reflected signals of a multimode GNSS-R receiver, comprising:
step one: receiving direct reflection signals from a plurality of GNSS-R satellites at the current moment from a plurality of channels of the multimode GNSS-R receiver, and determining the signal type of the direct reflection signals received by each channel;
step two: performing ephemeris decoding on the direct reflection signals received by each channel, and determining the position information of each GNSS-R satellite at the current time; determining the position information of the multimode GNSS-R receiver at the current moment according to the navigation information of the multimode GNSS-R receiver;
step three: determining the positions of specular reflection points of a plurality of channels based on the position information of each GNSS-R satellite and the position information of the multimode GNSS-R receiver at the current moment;
step four: judging whether the specular reflection points of the channels are overlapped according to the specular reflection point positions of the channels, screening signals of the direct reflection signals of the channels corresponding to the overlapped specular reflection points, and selecting the direct reflection signals with optimal performance for subsequent processing;
step five: judging and classifying the specular reflection points of all channels in a polling mode after no coincident specular reflection points exist or signal screening is carried out in the fourth step, and separating the specular reflection point channels observed in effective time from the specular reflection point channels not observed;
step six: performing secondary signal screening on the observed data stored in the specular reflection point channel observed in the effective time in the fifth step, and selecting the final observed data with optimal performance as the current specular reflection point channel;
step seven: for the specular reflection point channels which are not observed in the effective time in the step five and the specular reflection point channels which are subjected to secondary signal screening in the step six and replace observed data, checking the signal types of the direct reflection signals received by each specular reflection point channel, and inputting the direct reflection signals of different signal types into corresponding processing channels for processing;
step eight: and processing the reflected signals output by the processing channels into DDM data products and storing related information.
2. The method of claim 1, wherein the performing ephemeris decoding on the received direct reflection signals of each channel to determine the position information of each GNSS-R satellite at the current time comprises:
carrying out carrier synchronization, bit synchronization and frame synchronization operation on the direct-reflection signals received by each channel, and decoding the signals by using a decoding algorithm built in a multimode GNSS-R receiver to obtain ephemeris information of each GNSS-R satellite;
and calculating the position information of each GNSS-R satellite at the current moment according to the ephemeris information of each GNSS-R satellite.
3. The method according to claim 1, wherein the step of performing signal screening on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and selecting the direct reflection signal with the optimal performance for subsequent processing includes:
performing signal quality evaluation on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and determining signal quality evaluation values of the coincident specular reflection points;
determining signal scores of the coincident specular reflection points according to the signal quality evaluation values and preset evaluation weights;
sorting the direct reflection signals of the channels corresponding to the overlapped specular reflection points according to the signal scores;
the direct-reflected signal with the highest signal score is taken as the direct-reflected signal with the best performance.
4. A method according to claim 3, characterized in that the evaluation weight is set by:
through analysis of inversion accuracy of the reflected signals and the generated DDM data products, an evaluation strategy of the direct reflected signals adopts 5 physical quantities of elevation angle theta, polarization matching degree sigma, reflected signal frequency f, reflected signal intensity beta, distance from a specular reflection point to a multimode GNSS-R receiver and the GNSS-R satellite and delta of the GNSS-R satellite as signal influence factors;
weights of 5 physical quantities θ, σ, f, β, Δδ are set to (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 )，For the evaluation value, (y) 1 ,y 2 ,y 3 ,y 4 ,y 5 ) Constructing a deep learning model for the quantizers corresponding to the 5 physical quantities, wherein the expression of the deep learning model is as follows:
performing deep learning model fitting through multiple groups of physical quantity and weight data, and outputting evaluation values of the deep learning modelAnd feeding back to the weight input, and finally obtaining the optimal parameter value of the estimated weight.
5. An optimizing device for selecting reflected signals of a multimode GNSS-R receiver, comprising:
the signal acquisition module is used for receiving the direct reflection signals from the GNSS-R satellites at the current moment from the channels of the multimode GNSS-R receiver and determining the signal type of the direct reflection signals received by each channel;
the first position determining module is used for performing ephemeris decoding on the direct reflection signals received by each channel and determining the position information of each GNSS-R satellite at the current time; determining the position information of the multimode GNSS-R receiver at the current moment according to the navigation information of the multimode GNSS-R receiver;
the second position determining module is used for determining the positions of the specular reflection points of the multiple channels based on the position information of each GNSS-R satellite at the current moment and the position information of the multimode GNSS-R receiver;
the signal screening module is used for judging whether the specular reflection points of the channels are overlapped according to the specular reflection point positions of the channels, screening the signals of the direct reflection signals of the channels corresponding to the overlapped specular reflection points, and selecting the direct reflection signals with optimal performance for subsequent processing;
the signal judging module is used for judging and classifying the specular reflection points of all channels in a polling mode after no coincident specular reflection points exist or the signal screening is carried out in the fourth step, and separating the specular reflection point channels observed in the effective time from the specular reflection point channels not observed;
the signal selection module is used for carrying out secondary signal screening on the observed data stored in the specular reflection point channel observed in the effective time in the step five, and selecting the final observed data with optimal performance as the current specular reflection point channel;
the signal input module is used for checking the signal types of the direct reflection signals received by each specular reflection point channel, and inputting the direct reflection signals of different signal types into the corresponding processing channels for processing, wherein the specular reflection point channels are not observed in the effective time in the step five, and the specular reflection point channels of the observed data are replaced after secondary signal screening in the step six;
and the signal processing module is used for processing the reflected signals output by the processing channels into DDM data products and storing related information.
6. The apparatus of claim 5, wherein the first location determination module is specifically configured to:
carrying out carrier synchronization, bit synchronization and frame synchronization operation on the direct-reflection signals received by each channel, and decoding the signals by using a decoding algorithm built in a multimode GNSS-R receiver to obtain ephemeris information of each GNSS-R satellite;
and calculating the position information of each GNSS-R satellite at the current moment according to the ephemeris information of each GNSS-R satellite.
7. The apparatus of claim 5, wherein the signal screening module is specifically configured to:
performing signal quality evaluation on the direct reflection signals of the channels corresponding to the coincident specular reflection points, and determining signal quality evaluation values of the coincident specular reflection points;
determining signal scores of the coincident specular reflection points according to the signal quality evaluation values and preset evaluation weights;
sorting the direct reflection signals of the channels corresponding to the overlapped specular reflection points according to the signal scores;
the direct-reflected signal with the highest signal score is taken as the direct-reflected signal with the best performance.
8. The apparatus of claim 7, further comprising an evaluation weight setting module to set the evaluation weight by:
through analysis of inversion accuracy of the reflected signals and the generated DDM data products, an evaluation strategy of the direct reflected signals adopts 5 physical quantities of elevation angle theta, polarization matching degree sigma, reflected signal frequency f, reflected signal intensity beta, distance from a specular reflection point to a multimode GNSS-R receiver and the GNSS-R satellite and delta of the GNSS-R satellite as signal influence factors;
weights of 5 physical quantities θ, σ, f, β, Δδ are set to (x) 1 ,x 2 ,x 3 ,x 4 ,x 5 )，For the evaluation value, (y) 1 ,y 2 ,y 3 ,y 4 ,y 5 ) Constructing a deep learning model for the quantizers corresponding to the 5 physical quantities, wherein the expression of the deep learning model is as follows:
performing deep learning model fitting through multiple groups of physical quantity and weight data, and outputting evaluation values of the deep learning modelAnd feeding back to the weight input, and finally obtaining the optimal parameter value of the estimated weight.
9. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any of the preceding claims 1-4.
10. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-4.
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