CN111551902A - Method for recovering acquired signals when FMCW radar antenna is defective based on compressed sensing technology - Google Patents

Method for recovering acquired signals when FMCW radar antenna is defective based on compressed sensing technology Download PDF

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CN111551902A
CN111551902A CN202010489124.6A CN202010489124A CN111551902A CN 111551902 A CN111551902 A CN 111551902A CN 202010489124 A CN202010489124 A CN 202010489124A CN 111551902 A CN111551902 A CN 111551902A
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antenna
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CN111551902B (en
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张涛
檀天涵
陈道林
庄杰
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/354—Extracting wanted echo-signals

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

The invention discloses a recovery method of collected signals when an FMCW radar antenna is defective based on a compressed sensing technology, which comprises training a dictionary matrix by using complete receiving and transmitting pairs of data; storing the dictionary matrix, and judging whether hardware storage limitation or antenna failure condition exists; generating an analog sampling matrix according to the position of the defective antenna, and multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same specification to obtain an observation matrix; computing a sparse representation using the valid data; the sparse representation restores the signal by dictionary projection. By utilizing the sparsity of the received signals on the projection domain of the dictionary matrix, the original data can be accurately recovered when the array lacks partial sensor data, and the storage requirement of hardware is reduced.

Description

Method for recovering acquired signals when FMCW radar antenna is defective based on compressed sensing technology
Technical Field
The invention relates to the technical field of array signal acquisition, in particular to a recovery method of acquired signals when an FMCW radar antenna is defective based on a compressed sensing technology.
Background
Array signal processing is widely used in many fields of daily life and scientific research as an important branch of signal processing. The acquisition of signals is a key factor influencing the design and the realization effect of related signal processing algorithms, and the acquisition period of the signals influences the accuracy of the calculation results of the algorithms. In the processing of array signals, because the number and the positions of the array sensors directly determine the scale of matrix calculation in algorithm design, the failure of the whole array system can be caused by the wrong signal acquisition caused by the missing or damaged antenna.
The service life of the hardware of the FMCW (frequency modulated continuous wave) radar antenna system has a plurality of influence factors. On one hand, the antenna in practical use is exposed in the air for a long time, even in an outdoor environment, and the probability of accidental damage exists; on the other hand, when the FMCW array signal acquisition period is faster, the number of sampling points is larger, and the requirement of data storage of the array antenna on hardware is higher. And the system can be ensured to run by replacing hardware in time, and the cost is higher. And the inaccurate gain, phase or frequency of the replacement sensor causes the array system to have electronic uncertainty and need to be recalibrated.
Under the condition that replacement hardware is not prepared in advance, research significance and practical value are provided for ensuring the effective operation of the array system, so that the whole FMCW system can still normally operate under the condition that a few antennas and other hardware can work.
Disclosure of Invention
The invention aims to provide a method for recovering collected signals when an FMCW radar antenna is defective based on a compressed sensing technology, and aims to ensure the effective operation of a system when part of the FMCW radar array antenna is damaged and reduce the hardware replacement cost; and secondly, under the condition of limited hardware storage capacity, the data of fewer antennas can be used to meet the precision requirement.
In order to achieve the above object, the present invention provides a method for recovering an acquired signal when an FMCW radar antenna is defective based on a compressed sensing technology, which comprises:
training a dictionary matrix by using complete receiving and transmitting data;
storing the dictionary matrix, and judging whether hardware storage limitation or antenna failure condition exists;
generating an analog sampling matrix according to the position of the defective antenna, and multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same specification to obtain an observation matrix;
computing a sparse representation using the valid data;
the sparse representation restores the signal by dictionary projection.
In an embodiment, training a dictionary matrix for data using complete transceiving specifically includes:
collecting multiple groups of data of an antenna array under an ideal lossless condition, wherein the complete array consists of N groups of receiving and transmitting pairs, generating a data set for training, and totally L groups of training data, wherein each group comprises N data points;
randomly selecting a preset number of samples in training data to form an initial dictionary matrix, and initializing a dictionary;
and (4) perfecting a dictionary matrix through dictionary learning, and sparsely representing the training signal on a dictionary projection space.
In one embodiment, the dictionary matrix is perfected through dictionary learning, and the training signal is sparsely represented on a dictionary projection space, specifically including:
the fixed dictionary obtains a sparse coefficient by using a greedy algorithm in a sparse coding stage;
and fixing the sparse coefficient, and solving the updated dictionary by using a least square method in the dictionary updating stage.
In one embodiment, the sparse coefficients are fixed, and after the dictionary updating stage uses a least square method to solve the updated dictionary, the method further comprises:
judging whether the sparsity of the sparse coefficient meets the requirement or not;
if so, ending;
if not, fixing the dictionary again, and obtaining a sparse coefficient by using a greedy algorithm in a sparse coding stage; and fixing the sparse coefficient, and solving the updated dictionary by using a least square method in the dictionary updating stage until the preset iteration times are circulated.
In an embodiment, the determining whether there is a hardware storage limitation or an antenna failure condition specifically includes:
if hardware storage limitation or antenna failure exists, the array reserves M groups of effective transceiving pair data, corresponding row data is extracted from the N-dimensional unit matrix according to the position of the defective antenna, and an analog sampling matrix is obtained;
and if the hardware storage limit or the antenna failure condition does not exist, the antenna is normally used until the antenna is damaged.
The invention relates to a recovery method of collected signals when an FMCW radar antenna is defective based on a compressed sensing technology, which trains a dictionary matrix for data by using complete receiving and transmitting; storing the dictionary matrix, and judging whether hardware storage limitation or antenna failure condition exists; generating an analog sampling matrix according to the position of the defective antenna, and multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same specification to obtain an observation matrix; computing a sparse representation using the valid data; the sparse representation restores the signal by dictionary projection. By utilizing the sparsity of the received signals on the projection domain of the dictionary matrix, the original data can be accurately recovered when the array lacks partial sensor data, and the storage requirement of hardware is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for recovering an acquired signal when an FMCW radar antenna is defective according to an embodiment of the present invention;
fig. 2 is a schematic specific flowchart of a method for recovering an acquired signal when an FMCW radar antenna is defective according to an embodiment of the present invention;
fig. 3 is a schematic diagram of obtaining an analog sampling matrix corresponding to a defect condition.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for recovering an acquired signal when an FMCW radar antenna based on a compressed sensing technology is defective according to an embodiment of the present invention, and specifically, the method for recovering an acquired signal when an FMCW radar antenna based on a compressed sensing technology is defective may include the following steps:
s101, training a dictionary matrix by using complete receiving and transmitting pairs of data;
in the embodiment of the invention, a plurality of groups of data of the antenna array under the ideal lossless condition are collected, the complete array consists of N groups of receiving and transmitting pairs, and a data set D for training is generatedtrain∈RN×LTotal L groups of training data, and each group has N data points; randomly selecting a preset number of samples in training data to form an initial dictionary matrix, namely an initial sparse basis matrix, and initializing a dictionary psi; dictionary learning expression:
Figure BDA0002520365530000031
wherein psi ∈ RN×kAs a dictionary matrix, Si∈Rk×LFor sparse representation of the training data set, k is preset with a specified parameter, and λ represents a regular term coefficient. And (4) perfecting a dictionary matrix through dictionary learning, and sparsely representing the training signal on a dictionary projection space. Specifically, the fixed dictionary ψ, mod (method of Optimal direction) algorithm obtains a sparse coefficient S [ S ] by using an OMP greedy algorithm in the sparse coding stage1,S2,…SL]And satisfies the following conditions:
Figure BDA0002520365530000041
fixing the sparse coefficient, obtaining a sparse coefficient result according to an OMP greedy algorithm, and solving the updated dictionary psi ═ SZ by using a least square method in the dictionary updating stageT(ZZT)-1。
Judging whether the sparsity of the sparse coefficient meets the requirement or not; if so, ending; if not, fixing the dictionary again, and obtaining a sparse coefficient by using a greedy algorithm in a sparse coding stage; and fixing the sparse coefficient, and solving the updated dictionary by using a least square method in the dictionary updating stage until the preset iteration times are circulated, wherein the greedy algorithm is that the best choice is always made at present when the problem is solved, namely, the optimal solution is locally optimal in a certain sense without considering the overall optimization.
S102, storing the dictionary matrix, and judging whether hardware storage limitation or antenna failure condition exists;
in the embodiment of the invention, if hardware storage limit or antenna failure condition exists, M groups of effective transceiving pair data are reserved in the array, corresponding row (N-M) data are extracted from an N-dimensional unit matrix according to the position of a defective antenna, and an analog sampling matrix phi is obtained1∈RM×NThe original signal vector is multiplied by the analog sampling matrix to obtain a defect array signal vector; and if the hardware storage limit or the antenna failure condition does not exist, the antenna is normally used until the antenna is damaged.
S103, generating an analog sampling matrix according to the position of the defective antenna, and multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same model to obtain an observation matrix;
in the embodiment of the invention, the final sampling matrix is designed to be the result of multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same model, namely phi (phi) is1*Φ2)∈RM×NWherein phi2∈RM×NIs a random gaussian matrix. Namely, the finite equidistant property of the compressed sensing theory, namely the irrelevance of the sampling matrix and the dictionary matrix, can be satisfied.
S104, calculating sparse representation by utilizing the effective data;
in the embodiment of the invention, M-dimensional calculation data meeting limited equidistant property is obtained
Figure BDA0002520365530000042
Wherein
Figure BDA0002520365530000043
Representing N-dimensional observation data, including data that damages the sensor. For a low dimensional matrix modified to reduce hardware storage, the missing part data is assumed to be 0. The process simulates the process of extracting M pieces of nondestructive sensor data from the original array to form a new array, and the corresponding analog sampling matrix phi is noticed1And there is no error data in y.
And S105, restoring the signal through dictionary projection in sparse representation.
In the examples of the invention
Figure BDA0002520365530000051
Wherein
Figure BDA0002520365530000052
Specifically, according to y phi x phi psi α phi theta α, a sensing matrix theta phi psi reconstructs signals
Figure BDA0002520365530000053
||α||0The number of non-zero elements representing the sparsity of the signal. In fact, the existing optimization problem solution can solve the reconstruction problem
Figure BDA0002520365530000054
Further recovering the signal from the sparse dictionary
Figure BDA0002520365530000055
Specifically, please refer to fig. 2 and fig. 3, fig. 2 is a schematic diagram of a specific process of a method for recovering an acquired signal when an FMCW radar antenna is defective according to a compressive sensing technique according to an embodiment of the present invention, where the specific process is to begin with training a dictionary matrix by using complete transceiving, if there is a hardware storage limitation or an antenna failure, removing a part of antennas, reducing an amount of received data, generating an analog sampling matrix according to a defective antenna position, multiplying the analog sampling matrix by corresponding elements of a homomorphic random gaussian matrix to obtain an observation matrix, calculating a sparse representation by using valid data, and recovering the signal by dictionary projection in the sparse representation, and ending; if the antenna does not exist, the antenna is normally used until the antenna is damaged; fig. 3 is a schematic diagram of obtaining an analog sampling matrix corresponding to a defect condition, and 10 groups of transmit-receive pairs are arranged into an 8, 10-order unit matrix to an 8 × 10 analog sampling matrix.
The invention relates to a recovery method of collected signals when an FMCW radar antenna is defective based on a compressed sensing technology, which trains a dictionary matrix for data by using complete receiving and transmitting; storing the dictionary matrix, and judging whether hardware storage limitation or antenna failure condition exists; generating an analog sampling matrix according to the position of the defective antenna, and multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same specification to obtain an observation matrix; computing a sparse representation using the valid data; the sparse representation restores the signal by dictionary projection. By utilizing the sparsity of the received signals on the projection domain of the dictionary matrix, the original data can be accurately recovered when the array lacks partial sensor data, and the storage requirement of hardware is reduced. The traditional data transmission is acquisition-compression-transmission-decompression, and equipment for acquiring data is often exposed in a natural environment and can lose energy supply or even partially lose performance at any time.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. A recovery method of collected signals when FMCW radar antennas are defective based on a compressed sensing technology is characterized by comprising the following steps:
training a dictionary matrix by using complete receiving and transmitting data;
storing the dictionary matrix, and judging whether hardware storage limitation or antenna failure condition exists;
generating an analog sampling matrix according to the position of the defective antenna, and multiplying the analog sampling matrix by corresponding elements of a random Gaussian matrix with the same specification to obtain an observation matrix;
computing a sparse representation using the valid data;
the sparse representation restores the signal by dictionary projection.
2. The method for recovering the collected signal when the FMCW radar antenna is defective based on the compressive sensing technology as claimed in claim 1, wherein training the dictionary matrix by using the complete transceiving pair data specifically comprises:
collecting multiple groups of data of an antenna array under an ideal lossless condition, wherein the complete array consists of N groups of receiving and transmitting pairs, generating a data set for training, and totally L groups of training data, wherein each group comprises N data points;
randomly selecting a preset number of samples in training data to form an initial dictionary matrix, and initializing a dictionary;
and (4) perfecting a dictionary matrix through dictionary learning, and sparsely representing the training signal on a dictionary projection space.
3. The method for recovering the collected signal when an FMCW radar antenna is defective based on the compressed sensing technology as claimed in claim 2, wherein the dictionary matrix is perfected through dictionary learning, and the training signal is sparsely represented on a dictionary projection space, specifically comprising:
the fixed dictionary obtains a sparse coefficient by using a greedy algorithm in a sparse coding stage;
and fixing the sparse coefficient, and solving the updated dictionary by using a least square method in the dictionary updating stage.
4. A method for recovering an FMCW radar antenna with defect acquisition based on compressed sensing technology as claimed in claim 3, wherein sparse coefficients are fixed, and after a dictionary updating stage uses a least square method to solve an updated dictionary, the method further comprises:
judging whether the sparsity of the sparse coefficient meets the requirement or not;
if so, ending;
if not, fixing the dictionary again, and obtaining a sparse coefficient by using a greedy algorithm in a sparse coding stage; and fixing the sparse coefficient, and solving the updated dictionary by using a least square method in the dictionary updating stage until the preset iteration times are circulated.
5. The method for recovering the collected signal when the FMCW radar antenna is defective based on the compressive sensing technology as claimed in claim 1, wherein the determining whether the hardware storage limit or the antenna failure condition exists specifically includes:
if hardware storage limitation or antenna failure exists, the array reserves M groups of effective transceiving pair data, corresponding row data is extracted from the N-dimensional unit matrix according to the position of the defective antenna, and an analog sampling matrix is obtained;
and if the hardware storage limit or the antenna failure condition does not exist, the antenna is normally used until the antenna is damaged.
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