CN117607849A - Multi-target complex scene-oriented distance and speed combined high-precision sensing method and system - Google Patents
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Abstract
The invention relates to the field of signal processing, in particular to a distance and speed combined high-precision sensing method and system for a multi-target complex scene. The method comprises the following steps: processing the received OFDM time domain signal into an OFDM frame matrix, and retrieving an information bit matrix after constellation mapping; dividing the OFDM frame matrix by the information bit matrix to obtain a radar perception information matrix; preprocessing a radar sensing information matrix by using a conjugate reverse order space method and a space smoothing method, reconstructing the radar sensing information matrix into a one-dimensional radar sensing information vector, and then constructing a distance and speed combined autocorrelation matrix; and carrying out eigenvalue decomposition on the distance velocity joint autocorrelation matrix, constructing a distance velocity joint noise subspace matrix, constructing a distance velocity joint vector, calculating to obtain a two-dimensional distance velocity joint power spectrogram, and carrying out acceleration operation by using fast Fourier transform. The invention improves the resolution, precision and anti-noise interference performance of the algorithm, reduces the operand and ensures that the algorithm has practical timeliness.
Description
Technical Field
The invention relates to the technical field of communication signal processing, in particular to a distance and speed combined high-precision sensing method and system for a multi-target complex scene.
Background
In the past development, wireless communication and radar perception are two applications most common and important in modern radio technologies, and the system design is independently designed and developed according to different functions and frequency bands, wherein the radar is an active sensing device and performs target detection, estimation and tracking by means of transmission waveforms. For communication systems, the transmission of information is accomplished by waveform modulation and demodulation, and the primary goals of communication are spectral efficiency and throughput. Although there are many differences between the radar system and the communication system in function and working frequency band, there are similarities in hardware construction and working principle, and with the continuous development of radar and communication technology, the working frequency band for communication transmission and radar sensing frequency band overlap more and more, and the difference between the radar system and the communication system in working frequency band and system composition is gradually reduced, so that radar and communication integration becomes possible. The waveform design plays an extremely important role in the communication perception integrated system, and the strategy of the waveform design determines whether the communication perception integration of the signaling layer can be realized.
The OFDM waveform has the advantages of multipath resistance, frequency selective fading resistance, high frequency spectrum efficiency, easy synchronization and equalization, high flexibility, and the like, so that the OFDM waveform can adapt to various channel characteristics, and the OFDM technology is widely applied in the field of wireless communication at present. Meanwhile, the OFDM waveform can also be used for radar perception, such as a traditional radar processing method is used for carrying out correlation processing on the waveform, but the method of correlation processing can lead to higher side lobe and is easy to submerge adjacent targets. The novel processing method based on the modulation symbol domain has higher peak-to-side lobe ratio compared with the related processing method, however, the current processing method based on the modulation symbol domain has the defects of large main lobe width, limited resolution and incapability of distinguishing targets with similar distance and speed in the maximum likelihood estimation two-dimensional periodogram algorithm, and the one-dimensional MUSIC algorithm has the advantages of high resolution but can only aim at a single target and incapability of adapting to scenes of a plurality of perception targets.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a multi-target complex scene-oriented distance and speed combined high-precision sensing method and system, which adopt a multi-target combined distance and speed sensing estimation algorithm based on an OFDM communication sensing integrated waveform modulation symbol domain, and further utilize fast Fourier transform to perform acceleration operation, so that the operation amount realized by the algorithm is reduced, and the method and the system have practical timeliness.
The technical scheme adopted by the sensing method of the invention is as follows: a distance and speed combined high-precision sensing method for a multi-target complex scene comprises the following steps:
s1, receiving 0FDM time domain signal, and setting the number of subcarriers as N and the number of symbols as M in one frame of OFDM time domain signal;
s2, carrying out N-point FFT processing on each symbol in the OFDM time domain signal, and arranging data after each symbol FFT processing into an OFDM frame matrix according to columns;
s3, the information bit matrix is called, and each element in the information bit matrix represents a modulation symbol generated by constellation mapping of information bits;
s4, dividing the OFDM frame matrix by the information bit matrix to obtain a final radar perception information matrix;
s5, preprocessing a radar sensing information matrix by using a conjugate reverse-order space method, and performing reverse-order overturning after conjugation is carried out on each element in the radar sensing information matrix to obtain a conjugate reverse-order matrix; averaging the conjugated inverted sequence matrix and the original radar sensing information matrix, and reconstructing a front-back sensing information matrix;
s6, performing further optimization pretreatment on the forward and backward perception information matrix by using a space smoothing method, and setting the size of the subspace matrix as N Sub ×M Sub ，N Sub ＜N，M Sub < M, dividing the forward and backward sense information matrix into (N-N) in a stepwise shift manner according to the size of the subspace matrix Sub +1)(M-M Sub +1) subarrays; finally, overlapping and averaging a plurality of subarrays to obtain a space smoothing matrix;
s7, reconstructing the radar sensing information matrix preprocessed by the conjugate inverted sequence spatial method and the spatial smoothing method into a one-dimensional radar sensing information vector;
s8, constructing a distance and speed combined autocorrelation matrix through the reconstructed one-dimensional radar perception information vector;
s9, carrying out eigenvalue decomposition on the distance speed combined autocorrelation matrix to obtain an eigenvector matrix formed by eigenvectors; constructing a distance speed joint noise subspace matrix from feature vectors corresponding to feature values except the K maximum feature values in the feature vector matrix;
s10, constructing a distance and speed joint vector, and calculating according to a distance and speed joint noise subspace matrix to obtain a two-dimensional distance and speed joint power spectrogram;
s11, carrying out spectral peak search on the two-dimensional distance and speed combined power spectrogram, wherein indexes corresponding to the spectral peaks are the estimated distance and speed of the perception target;
s12, reconstructing each column vector of the distance velocity joint noise subspace matrix into a matrix to obtain a plurality of reconstructed matrices, performing acceleration operation on the two-dimensional distance velocity joint power spectrogram by using fast Fourier transform based on the reconstructed matrices, and updating the calculation of the two-dimensional distance velocity joint power spectrogram.
The technical scheme adopted by the sensing system of the invention is as follows: a distance and speed combined high-precision sensing system for a multi-target complex scene comprises the following modules:
the signal processing module is used for receiving the OFDM time domain signal, and setting the number of subcarriers in one frame of OFDM time domain signal as N and the number of symbols as M; performing N-point FFT processing on each symbol in the OFDM time domain signal, wherein the data after the FFT processing of each symbol are arranged into an OFDM frame matrix according to columns; retrieving an information bit matrix, wherein each element in the information bit matrix represents a modulation symbol generated by constellation mapping of information bits;
the first preprocessing module is used for dividing the OFDM frame matrix by the information bit matrix to obtain a final radar perception information matrix; preprocessing a radar sensing information matrix by using a conjugate reverse-order space method, and performing reverse-order overturning after conjugation of each element in the radar sensing information matrix to obtain a conjugate reverse-order matrix; averaging the conjugated inverted sequence matrix and the original radar sensing information matrix, and reconstructing a front-back sensing information matrix;
a second preprocessing module for performing spatial smoothing on the objectPerforming further optimization pretreatment on the front and rear perception information matrix, and setting the size of the subspace matrix as N Sub ×M Sub ，N Sub ＜N，M Sub < M, dividing the forward and backward sense information matrix into (N-N) in a stepwise shift manner according to the size of the subspace matrix Sub +1)(M-M Sub +1) subarrays; finally, overlapping and averaging a plurality of subarrays to obtain a space smoothing matrix;
the matrix reconstruction module is used for reconstructing the radar sensing information matrix preprocessed by the conjugate inverted sequence space method and the space smoothing method into a one-dimensional radar sensing information vector; constructing a distance-speed joint autocorrelation matrix by the reconstructed one-dimensional radar perception information vector;
the characteristic value decomposition module is used for carrying out characteristic value decomposition on the distance speed combined autocorrelation matrix to obtain a characteristic vector matrix formed by characteristic vectors; constructing a distance speed joint noise subspace matrix from feature vectors corresponding to feature values except the K maximum feature values in the feature vector matrix;
the distance speed joint power spectrum calculation module is used for constructing a distance speed joint vector and calculating according to a distance speed joint noise subspace matrix to obtain a two-dimensional distance speed joint power spectrum; carrying out spectrum peak search on the two-dimensional distance and speed combined power spectrogram, wherein indexes corresponding to the spectrum peaks are the estimated distance and speed of the perception target; reconstructing each column vector of the distance velocity joint noise subspace matrix into a matrix to obtain a plurality of reconstructed matrices, performing acceleration operation on the two-dimensional distance velocity joint power spectrogram by using fast Fourier transform based on the reconstructed matrices, and updating the calculation of the two-dimensional distance velocity joint power spectrogram.
Compared with the prior art, the invention has the following technical effects:
the invention not only provides a two-dimensional MUSIC estimation algorithm based on a modulation symbol domain to cope with a scene of multi-target estimation, but also deeply mines potential information of a radar sensing information matrix through a preprocessing optimization means of conjugate reverse order space and space smoothing, further improves resolution, precision and anti-noise interference performance of the two-dimensional MUSIC estimation algorithm, and carries out acceleration operation on the two-dimensional MUSIC estimation algorithm through fast Fourier transform, thereby greatly reducing operation amount and enabling the algorithm to have practical timeliness.
Drawings
FIG. 1 is a flow chart of a distance and speed combined high-precision sensing method in an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating the partitioning of a forward and backward sense information matrix according to an embodiment of the present invention;
FIG. 3a is a schematic diagram of a one-dimensional independent estimation effect of distance in an embodiment of the present invention;
FIG. 3b is a schematic diagram of a one-dimensional independent estimation of velocity in an embodiment of the invention;
FIG. 4 is a schematic diagram of an estimation effect of a two-dimensional periodogram algorithm according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an estimation effect of a two-dimensional MUSIC algorithm without preprocessing optimization in an embodiment of the present invention;
fig. 6 is a schematic diagram of the estimation effect of the two-dimensional MUSIC algorithm with improved conjugate smoothing in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It will be apparent that the described embodiments are some, but not all, embodiments of the invention.
Example 1
The embodiment is a multi-target complex scene-oriented distance and speed combined high-precision sensing method, which comprises the steps of performing fast Fourier transform on received OFD M time domain echo signals to obtain a two-dimensional time-frequency domain matrix, and obtaining a radar sensing information matrix based on a modulation symbol domain after processing random bit information; based on a radar perception information matrix, a two-dimensional MUSIC estimation algorithm combining distance speeds is provided, and a plurality of targets with similar distance speeds can be identified; the method comprises the steps of carrying out conjugate inverted sequence averaging and sub-matrix smoothing pretreatment on a radar perception information matrix, and then carrying out joint estimation on distance and speed by using a two-dimensional MUSIC algorithm; it is proposed to use a fast fourier transform to accelerate the two-dimensional MUSIC algorithm. Specifically, the method comprises the following steps:
s1, receiving an OFDM time domain signal through a signal processing module, and setting the number of subcarriers in one frame of OFDM time domain signal as N and the number of symbols as M.
In this embodiment, assuming that the OFDM time domain signal of the transmitting end is s (t), the received waveform r (t) after being reflected by K targets may be expressed as:
wherein a is k Representing the amplitude attenuation multiple of the kth target reflected echo as a reflected amplitude attenuation factor; τ k Representing target echo delay, f D，k Indicating the doppler shift frequency,for the additional random phase, w (t) represents the superimposed white gaussian noise.
The echo received after being reflected by the kth target has a certain time delay, the light speed is set as c, and the target distance is set as R k The target echo delay may be expressed as:
relative movement velocity V of kth target k Will result in Doppler shift when the carrier frequency is f c At the time of Doppler shift f D，k Can be expressed as:
assuming that there are N subcarriers and M symbols in one OFDM frame at the transmitting end, the data at the transmitting end can be represented as a matrix by a matrix arrangement
Transmitting end data matrixEach element b of (b) n，m Represents a modulation symbol generated by constellation mapping of information bits, where n=0, N-1, m=0, M-1, each row represents a subcarrier, and each column represents an OFDM symbol before IFFT processing in the transmitting end.
S2, carrying out N-point FFT processing on each symbol in the one-frame OFDM time domain signal in the step S1, and arranging data after each symbol FFT processing into an OFDM frame matrix according to columns
In this embodiment, elements in the OFDM frame matrix may be expressed as follows:
wherein T is s ＝1/Δf+T G For a single OFDM symbol period, T G For the guard interval length, Δf is the subcarrier spacing, f 0 Is the initial subcarrier frequency; w represents a matrix of Gaussian white noise channels, individual elements of the matrix W (W) n，m ～N(0，σ 2 ) Is complex random variable with independent and same distribution, sigma 2 The value of (2) depends on the signal-to-noise ratio of the scene.
S3, information bit matrix is called through the signal processing moduleEach element in the information bit matrix represents a modulation symbol generated by constellation mapping of information bits.
S4, dividing the OFDM frame matrix obtained in the step S2 by the information bit matrix of the step S3 to obtain a final radar perception information matrix ψ. The expression of the radar sensing information matrix is as follows:
wherein,representing elements in an OFDM frame matrix, +.>Representing the elements in the matrix of information bits, the same constant term for each element in the radar awareness information matrix,representing white noise divided by constellation mapping data.
S5, preprocessing the radar sensing information matrix psi of the step S4 by using a conjugate reverse order space method, and performing reverse order overturning after conjugation on each element in the radar sensing information matrix psi to obtain a conjugate reverse order matrix psi b ：
Ψ b ＝J N Ψ * J M ( 7 )
Wherein ( * The operation of taking the conjugate is indicated,and->An inverse identity matrix with sub diagonal elements of 1; matrix of conjugate reverse orderThe original radar sensing information matrix is averaged, and a front-back sensing information matrix psi is reconstructed fb ＝(Ψ+Ψ b )/2。
The estimation algorithm based on the modulation symbol domain is to perform data processing according to the phase differences of different row elements and different column elements in the radar perception information matrix ψ to obtain the distance and speed estimation of the target. Due to noise of elements in the matrixIs white noise and independent of each other, and is used for averaging the front and back matrix psi fb The noise power of the (a) becomes half of the original noise power, and the information items with different phases in the elements are related, namely, the phase difference value among the different elements is unchanged; therefore, the two-dimensional MUSIC algorithm is optimized by constructing the conjugate inverted sequence matrix through the conjugate inverted sequence space method, so that the influence of noise can be reduced, and the noise resistance is improved to reach a lower signal-to-noise ratio threshold. Meanwhile, based on a forward-backward matrix ψ by a conjugate reverse-order space method fh The rank of the constructed autocorrelation matrix is twice that of the autocorrelation matrix constructed based on the radar sensing information matrix F, and the non-zero eigenvalue is twice that of the original, which is important for the subsequent two-dimensional MUSIC algorithm. Because the MUSIC algorithm is normally operated on the premise of ensuring that the number of non-zero eigenvalues contained in the autocorrelation matrix in eigenvalue decomposition is larger than the target number, and the more the number of the non-zero eigenvalues is than the target number, the more stable the algorithm is.
S6, using a space smoothing method to perform a space smoothing on the forward and backward perception information matrix ψ of the step S5 fb Further optimizing and preprocessing the subspace matrix ψ Sub Is set to be N Sub ×M Sub (N Sub ＜N，M Sub < M), then according to the set subspace matrixIs to shift the size of the forward and backward sense information matrix ψ stepwise fb Divided into (N-N) Sub +1)(M-M Sub +1) subarrays ψ Sub(n,m) (n＝1，2，…，N-N Sub +1；m＝1，2，…，M-M Sub +1), as shown in fig. 2; and finally, superposing and averaging a plurality of subarrays to obtain a space smoothing matrix:
after spatial smoothing by a plurality of subarrays, the rank of the autocorrelation matrix constructed based on the spatial smoothing matrix becomes (N-N) based on the radar sensing information matrix Sub +1)(M-M Sub +1) times, i.e. having (N-N) Sub +1)(M-M Sub +1) non-zero eigenvalues. Meanwhile, after sub-array smoothing processing, the noise power of each element in the space smoothing matrix becomes 1/(N-N) Sub +1)(M-M Sub +1), the influence of noise can be significantly reduced.
The step further calculates a forward and backward space smooth perception information matrix psi according to the space smooth matrix fbss The method comprises the following steps:
wherein N is Sub Representing subspace matrix ψ Sub Line number M of (2) Sub Representing subspace matrix ψ Sub Number of columns, ψ Sub(n，m) Then the forward and backward sense information matrix ψ is represented fbss N to N of (2) Sub +n-1 rows and M to M Sub +m-1 columns.
S7, reconstructing the radar sensing information matrix psi which is preprocessed by the conjugate inverted sequence space method and the space smoothing method into a one-dimensional radar sensing information vector psi:
ψ＝[(Ψ) 1，1 ，(Ψ) 2，1 ，…，(Ψ) N，1 ，(Ψ) 1，2 ，(Ψ) 2，2 ，…，(Ψ) N，2 ，…，(Ψ) 1，M ，(Ψ) 2，M ，…，(Ψ) N，M ] T (10)
s8, constructing a one-dimensional radar perception information vector psi after reconstruction in the step S7Distance velocity joint autocorrelation matrix C R，V ：
S9, carrying out eigenvalue decomposition on the distance speed joint autocorrelation matrix in the step S8: c (C) R，V ＝U∑U H And U is a eigenvector matrix formed by eigenvectors after eigenvalue decomposition of the distance-velocity combined autocorrelation matrix, so that an eigenvector matrix formed by the eigenvectors is obtained, and the superscript H represents the conjugate transpose of the matrix.
After the pretreatment of the conjugate reverse space method and the airborne smoothing method, the distance velocity combined autocorrelation matrix is provided with 2 (N-N) Sub +1)(M-M Sub +1) non-zero eigenvalues, so that eigenvectors corresponding to eigenvalues other than the K largest eigenvalues in the eigenvector matrix U are constructed into a distance-velocity joint noise subspace matrix according to the known perception target number K
S10, constructing a distance velocity joint vector S (r, v), and calculating according to the distance velocity joint noise subspace matrix of the step S9 to obtain a two-dimensional distance velocity joint power spectrogram P R，V (r，v)。
Wherein R is distance, v is speed, R max At the maximum distance, V max At the most speedLarge value.
S11, combining the two-dimensional distance velocity with the power spectrum P in the step S10 R，V (r, v) searching for spectral peaks, and indexing corresponding to spectral peaksI.e. the estimated distance and speed for the perceived target.
S12, combining the distance speed in the step S9 with the noise subspace matrix U noise Each column vector is reconstructed into a matrix NM-K reconstruction matrixes can be obtained, and the two-dimensional distance speed joint power spectrogram in the step S10 is subjected to acceleration operation by using fast Fourier transform based on the reconstruction matrixes, so that the calculation of the two-dimensional distance speed joint power spectrogram is updated as follows:
wherein C is k Representing the joint noise subspace matrix U by range velocity noise The kth column vector reconstructs the matrix.
In the present embodiment, when the fast fourier transform is used for the acceleration operation, the distance velocity is combined with the noise subspace matrix U noise Multiple vectors u can be used k Expressed as:
where k=1, 2,...
The matrix operation can be expressed as:
vector u is then added k Remodelling into a matrix representationThe vector multiplication result at this time can be expressed as:
the vector multiplication result is essentially equivalent to the matrix C for remodeling k Since the IFFT operation is performed on the column-wise data and the FFT operation is performed on the row-wise data, the calculation of the two-dimensional distance velocity joint power spectrum can be updated as shown in equation (14).
The present embodiment is optimized by using the fast fourier transform for a size N Per ×M Per The joint power spectrum requires (NM-K) NM Per (1+log 2 (N Per M Per ) Complex multiplication, corresponding to an algorithm complexity O ((NM) 2 log (NM)), while the (NM-K) N is needed before the optimization operation Per M Per (1+2NM) complex multiplication operations, the corresponding algorithm complexity is O ((NM) 3 ) It can be seen that the calculation amount of the two-dimensional distance speed combined power spectrogram is greatly reduced after operation optimization.
For the MUSIC algorithm, if the non-zero eigenvalue of the autocorrelation matrix is not far greater than the target number, the normal performance of the algorithm cannot be ensured, and the effect of super resolution is achieved. In the embodiment, in the steps S7 and S8, since the algorithm only uses OFDM data of one frame to perform estimation, the autocorrelation matrix constructed by directly using the radar sensing information matrix ψ without any preprocessing has only a single non-zero eigenvalue and cannot normally operate under the condition that a plurality of sensing targets exist, so the steps S5 and S6 reduce noise power and increase the number of the non-zero eigenvalues of the autocorrelation matrix through preprocessing, which is very important in order to ensure that the MUSIC algorithm can normally operate under a multi-target complex scene and have complete super-resolution performance.
That is, the two-dimensional MUSIC algorithm is expanded to a two-dimensional level and optimized through a preprocessing means, so that the problem of fuzzy distance speed matching of the one-dimensional MUSIC algorithm to multiple targets is solved, the algorithm not only plays the advantages of the MUSIC algorithm, but also can estimate the multiple targets. Specifically, the two-dimensional MUSIC algorithm is realized through the steps S7-S10, and the preprocessing of the steps S5-S6 ensures the normal operation of the two-dimensional MUSIC algorithm under a multi-target scene.
For four targets to be perceived with two pairs of similar speed distances: (400 m, -100 m/s), (405 m, -105 m/s), (80 m,13 m/s), (92 m,10 m/s). Fig. 3a and fig. 3b show that, although the one-dimensional MUSIC algorithm has higher resolution and super-resolution compared with the one-dimensional periodic graph method, the one-dimensional MUSIC algorithm can distinguish targets with different distances or speeds, but the estimation of the distances and the speeds is one-dimensional independent estimation, and the distances and the speeds of different targets cannot be matched, so that the perception task of the multi-target scene cannot be completed. In comparison with fig. 4 and 5, the two-dimensional periodogram algorithm and the one-dimensional periodogram algorithm have the same problems, namely, the problems of too low resolution and too large side lobe, and two groups of targets adjacent to each other in distance speed still cannot be successfully distinguished. Compared with a two-dimensional periodogram algorithm, the two-dimensional MUSIC algorithm without pretreatment has smaller main lobe width, but the resolution is still not fine enough, only one group of targets can be distinguished, and the targets still cannot be distinguished for a group of targets which are more similar, so that the performance of super resolution cannot be achieved; as shown in FIG. 6, the two-dimensional MUSIC algorithm with improved conjugate smoothing provided by the invention successfully distinguishes two groups of adjacent targets, and the estimation effect is very good. By carrying out two-dimensional estimation by combining the distance speed, the algorithm provided by the invention exerts the super-resolution advantage of the MUSCI method, and meanwhile, compared with the one-dimensional MUSIC algorithm, the distance and speed matching problem can not occur, and the method can be suitable for multi-target perception scenes. Besides, after the algorithm provided by the invention is optimized through double preprocessing of conjugate inverted sequence and space smoothing, the width of the main lobe is very small, and almost no side lobe exists, the energy is more concentrated, the probability of false alarm caused by coverage of adjacent weaker targets by the side lobe is effectively reduced, the resolution and the precision are higher, and the performance of super resolution is realized; in addition, through double preprocessing optimization of conjugate reverse order and space smoothing, compared with a two-dimensional MUSIC which is not preprocessed and optimized, the two-dimensional distance speed combined power spectrogram noise amplitude is obviously reduced, and the algorithm provided by the invention has excellent anti-noise performance. Finally, when the two-dimensional distance velocity combined power spectrogram is calculated, the column vectors of the distance velocity combined noise subspace are remodeled into a matrix, and the fast Fourier transform is used for accelerating operation, so that the operation amount is greatly reduced, and the algorithm has practical timeliness.
Example 2
Based on the same inventive concept as embodiment 1, the present embodiment provides a distance and speed combined high-precision sensing system for a multi-target complex scene, which includes the following modules:
the signal processing module is used for receiving the OFDM time domain signal, and setting the number of subcarriers in one frame of OFDM time domain signal as N and the number of symbols as M; performing N-point FFT processing on each symbol in the OFDM time domain signal, wherein the data after the FFT processing of each symbol are arranged into an OFDM frame matrix according to columns; retrieving an information bit matrix, wherein each element in the information bit matrix represents a modulation symbol generated by constellation mapping of information bits;
the first preprocessing module is used for dividing the OFDM frame matrix by the information bit matrix to obtain a final radar perception information matrix; preprocessing a radar sensing information matrix by using a conjugate reverse-order space method, and performing reverse-order overturning after conjugation of each element in the radar sensing information matrix to obtain a conjugate reverse-order matrix; averaging the conjugated inverted sequence matrix and the original radar sensing information matrix, and reconstructing a front-back sensing information matrix;
the second preprocessing module is used for further optimizing and preprocessing the forward and backward perception information matrix by using a space smoothing method, and setting the size of the subspace matrix as N Sub ×M Sub ，N Sub ＜N，M Sub < M, dividing the forward and backward sense information matrix into (N-N) in a stepwise shift manner according to the size of the subspace matrix Sub +1)(M-M Sub +1) subarrays; finally, a plurality of subarrays are overlapped and flattenedObtaining a space smoothing matrix;
the matrix reconstruction module is used for reconstructing the radar sensing information matrix preprocessed by the conjugate inverted sequence space method and the space smoothing method into a one-dimensional radar sensing information vector; constructing a distance-speed joint autocorrelation matrix by the reconstructed one-dimensional radar perception information vector;
the characteristic value decomposition module is used for carrying out characteristic value decomposition on the distance speed combined autocorrelation matrix to obtain a characteristic vector matrix formed by characteristic vectors; constructing a distance speed joint noise subspace matrix from feature vectors corresponding to feature values except the K maximum feature values in the feature vector matrix;
the distance speed joint power spectrum calculation module is used for constructing a distance speed joint vector and calculating according to a distance speed joint noise subspace matrix to obtain a two-dimensional distance speed joint power spectrum; carrying out spectrum peak search on the two-dimensional distance and speed combined power spectrogram, wherein indexes corresponding to the spectrum peaks are the estimated distance and speed of the perception target; reconstructing each column vector of the distance velocity joint noise subspace matrix into a matrix to obtain a plurality of reconstructed matrices, performing acceleration operation on the two-dimensional distance velocity joint power spectrogram by using fast Fourier transform based on the reconstructed matrices, and updating the calculation of the two-dimensional distance velocity joint power spectrogram.
In this embodiment, the second preprocessing module further calculates the front-back direction spatial smooth perception information matrix ψ according to the spatial smoothing matrix fbss The method comprises the following steps:
wherein N is Sub Representing the number of rows, M, of the subspace matrix Sub Representing the number of columns of the subspace matrix, ψ Sub(n，m ) N to N representing the matrix of forward and backward sense information Sub +n-1 rows and M to M Sub +m-1 columns, n=0,..n-1, m=0,..m-1.
In this embodiment, the distance velocity joint vector constructed by the distance velocity joint power spectrum calculation module is s (r, v):
r∈(0，R max )，v∈(-|V max |，|V max |)
wherein R is distance, v is speed, c is speed of light, R max At the maximum distance, V max At maximum speed, T s ＝1/Δf+T G For a single OFDM symbol period, T G For the guard interval length, Δf is the subcarrier spacing, f c Is the carrier frequency;
two-dimensional distance and speed combined power spectrum is P R，V (r，v)：
U noise For the range-rate joint noise subspace matrix, the superscript H denotes the conjugate transpose of the matrix.
The above modules of this embodiment are used to implement the corresponding steps of embodiment 1, respectively, and for detailed implementation procedures, see embodiment 1.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative, not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are all within the protection of the present invention.
Claims (10)
1. The distance and speed combined high-precision sensing method for the multi-target complex scene is characterized by comprising the following steps of:
s1, receiving an OFDM time domain signal, and setting the number of subcarriers in one frame of OFDM time domain signal as N and the number of symbols as M;
s2, carrying out N-point FFT processing on each symbol in the OFDM time domain signal, and arranging data after each symbol FFT processing into an OFDM frame matrix according to columns;
s3, the information bit matrix is called, and each element in the information bit matrix represents a modulation symbol generated by constellation mapping of information bits;
s4, dividing the OFDM frame matrix by the information bit matrix to obtain a final radar perception information matrix;
s5, preprocessing a radar sensing information matrix by using a conjugate reverse-order space method, and performing reverse-order overturning after conjugation is carried out on each element in the radar sensing information matrix to obtain a conjugate reverse-order matrix; averaging the conjugated inverted sequence matrix and the original radar sensing information matrix, and reconstructing a front-back sensing information matrix;
s6, performing further optimization pretreatment on the forward and backward perception information matrix by using a space smoothing method, and setting the size of the subspace matrix as N Sub ×M Sub ，N Sub <N,M Sub <M, dividing the forward and backward sense information matrix into (N-N) in a stepwise shift mode according to the size of the set subspace matrix Sub +1)(M-M Sub +1) subarrays; finally, overlapping and averaging a plurality of subarrays to obtain a space smoothing matrix;
s7, reconstructing the radar sensing information matrix preprocessed by the conjugate inverted sequence spatial method and the spatial smoothing method into a one-dimensional radar sensing information vector;
s8, constructing a distance and speed combined autocorrelation matrix through the reconstructed one-dimensional radar perception information vector;
s9, carrying out eigenvalue decomposition on the distance speed combined autocorrelation matrix to obtain an eigenvector matrix formed by eigenvectors; constructing a distance speed joint noise subspace matrix from feature vectors corresponding to feature values except the K maximum feature values in the feature vector matrix;
s10, constructing a distance and speed joint vector, and calculating according to a distance and speed joint noise subspace matrix to obtain a two-dimensional distance and speed joint power spectrogram;
s11, carrying out spectral peak search on the two-dimensional distance and speed combined power spectrogram, wherein indexes corresponding to the spectral peaks are the estimated distance and speed of the perception target;
s12, reconstructing each column vector of the distance velocity joint noise subspace matrix into a matrix to obtain a plurality of reconstructed matrices, performing acceleration operation on the two-dimensional distance velocity joint power spectrogram by using fast Fourier transform based on the reconstructed matrices, and updating the calculation of the two-dimensional distance velocity joint power spectrogram.
2. The distance-velocity joint high-precision sensing method according to claim 1, wherein elements in the OFDM frame matrix are expressed as:
wherein b n,m For elements in the data matrix of the transmitting end, representing modulation symbols generated by constellation mapping of information bits, n=0, …, N-1, m=0, …, M-1, each row of the data matrix of the transmitting end represents a subcarrier, and each column represents an OFDM symbol before IFFT processing in the transmitting end; a, a k Representing the amplitude attenuation multiple of the kth target reflected echo as a reflected amplitude attenuation factor; τ k Representing target echo delay, f D,k Indicating the doppler shift frequency,is an additional random phase;
T s ＝1/Δf+T G for a single OFDM symbol period, T G For the guard interval length, Δf is the subcarrier spacing, f 0 Is the initial subcarrier frequency; w represents a matrix of Gaussian white noise channels, individual elements of the matrix W (W) n,m ～N(0,σ 2 ) Is complex random variable with independent and same distribution, sigma 2 The value of (2) depends on the signal-to-noise ratio of the scene.
3. The distance and speed combined high-precision sensing method according to claim 2, wherein,radar perception information matrix (ψ) n,m The expression of (2) is:
wherein, (F) Tx ) n,m Representing elements in the information bit matrix;constant term, which is identical for every element in the radar-aware information matrix, < >>Representing white noise divided by constellation mapped modulation symbols.
4. The distance and speed joint high-precision sensing method according to claim 1, wherein step S6 further calculates a forward and backward spatial smoothing sensing information matrix ψ according to a spatial smoothing matrix fbss The method comprises the following steps:
wherein N is Sub Representing the number of rows, M, of the subspace matrix Sub Representing the number of columns of the subspace matrix, ψ Sub(n,m) N to N representing the matrix of forward and backward sense information Sub +n-1 rows and M to M Sub +m-1 columns, n=0, …, N-1, m=0, …, M-1.
5. The method of claim 1, wherein the range-velocity joint vector constructed in step S10 is S (r, v):
wherein R is distance, v is speed, c is speed of light, R max At the maximum distance, V max At maximum speed, T s ＝1/Δf+T G For a single OFDM symbol period, T G For the guard interval length, Δf is the subcarrier spacing, f c Is the carrier frequency;
two-dimensional distance and speed combined power spectrum is P R,V (r,v)：
U noise For the range-rate joint noise subspace matrix, the superscript H denotes the conjugate transpose of the matrix.
6. The method for combining distance and speed with high precision according to claim 5, wherein step S12 updates the calculation of the two-dimensional distance and speed combined power spectrum to:
wherein C is k Representing the joint noise subspace matrix U by range velocity noise The kth column vector reconstructs the matrix, and NM-K reconstructs the number of matrices.
7. The method of claim 6, wherein the distance velocity joint noise subspace matrix uses a plurality of vectors u k Expressed as:
the matrix operation is expressed as:
vector u is then added k Remodelling into a matrix representationThe vector multiplication result at this time is expressed as:
where k=1, 2, …, NM-K.
8. A distance and speed combined high-precision sensing system for a multi-target complex scene is characterized by comprising the following modules:
the signal processing module is used for receiving the OFDM time domain signal, and setting the number of subcarriers in one frame of OFDM time domain signal as N and the number of symbols as M; performing N-point FFT processing on each symbol in the OFDM time domain signal, wherein the data after the FFT processing of each symbol are arranged into an OFDM frame matrix according to columns; retrieving an information bit matrix, wherein each element in the information bit matrix represents a modulation symbol generated by constellation mapping of information bits;
the first preprocessing module is used for dividing the OFDM frame matrix by the information bit matrix to obtain a final radar perception information matrix; preprocessing a radar sensing information matrix by using a conjugate reverse-order space method, and performing reverse-order overturning after conjugation of each element in the radar sensing information matrix to obtain a conjugate reverse-order matrix; averaging the conjugated inverted sequence matrix and the original radar sensing information matrix, and reconstructing a front-back sensing information matrix;
the second preprocessing module is used for further optimizing and preprocessing the forward and backward perception information matrix by using a space smoothing method, and setting the size of the subspace matrix as N Sub ×M Sub ，N Sub <N,M Sub <M, dividing the forward and backward sense information matrix into (N-N) in a stepwise shift mode according to the size of the set subspace matrix Sub +1)(M-M Sub +1) number ofSubarray; finally, overlapping and averaging a plurality of subarrays to obtain a space smoothing matrix;
the matrix reconstruction module is used for reconstructing the radar sensing information matrix preprocessed by the conjugate inverted sequence space method and the space smoothing method into a one-dimensional radar sensing information vector; constructing a distance-speed joint autocorrelation matrix by the reconstructed one-dimensional radar perception information vector;
the characteristic value decomposition module is used for carrying out characteristic value decomposition on the distance speed combined autocorrelation matrix to obtain a characteristic vector matrix formed by characteristic vectors; constructing a distance speed joint noise subspace matrix from feature vectors corresponding to feature values except the K maximum feature values in the feature vector matrix;
the distance speed joint power spectrum calculation module is used for constructing a distance speed joint vector and calculating according to a distance speed joint noise subspace matrix to obtain a two-dimensional distance speed joint power spectrum; carrying out spectrum peak search on the two-dimensional distance and speed combined power spectrogram, wherein indexes corresponding to the spectrum peaks are the estimated distance and speed of the perception target; reconstructing each column vector of the distance velocity joint noise subspace matrix into a matrix to obtain a plurality of reconstructed matrices, performing acceleration operation on the two-dimensional distance velocity joint power spectrogram by using fast Fourier transform based on the reconstructed matrices, and updating the calculation of the two-dimensional distance velocity joint power spectrogram.
9. The distance-velocity joint high-precision sensing system according to claim 8, wherein the second preprocessing module further calculates a forward-backward spatial smoothing sensing information matrix ψ according to a spatial smoothing matrix fbss The method comprises the following steps:
wherein N is Sub Representing the number of rows, M, of the subspace matrix Sub Representing the number of columns of the subspace matrix, ψ Sub(n,m) N to N representing the matrix of forward and backward sense information Sub +n-1 rows and M to M Sub +m-1 columnA sub-matrix of composition, n=0, …, N-1, m=0, …, M-1.
10. The system of claim 8, wherein the distance velocity joint power spectrum calculation module constructs a distance velocity joint vector s (r, v):
wherein R is distance, v is speed, c is speed of light, R max At the maximum distance, V max At maximum speed, T s ＝1/Δf+T G For a single OFDM symbol period, T G For the guard interval length, Δf is the subcarrier spacing, f c Is the carrier frequency;
two-dimensional distance and speed combined power spectrum is P R,V (r,v)：
U noise For the range-rate joint noise subspace matrix, the superscript H denotes the conjugate transpose of the matrix.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137410A (en) * | 2015-07-24 | 2015-12-09 | 西安电子科技大学 | OFDM-based high-resolution radar communication integration waveform optimization method |
| DE102015222043A1 (en) * | 2015-11-10 | 2017-05-11 | Robert Bosch Gmbh | Method for operating an OFDM radar device |
| CN112986924A (en) * | 2021-01-28 | 2021-06-18 | 西安电子科技大学 | OFDM radar communication integrated range and speed fast super-resolution estimation method |
| US20210364616A1 (en) * | 2020-05-21 | 2021-11-25 | Hertzwell Pte. Ltd. | Radar system and computer-implemented method for radar target detection |
| CN113805170A (en) * | 2021-09-02 | 2021-12-17 | 江苏科技大学 | OFDM radar communication integrated high-speed target distance and speed estimation method |
| CN114422303A (en) * | 2021-12-29 | 2022-04-29 | 哈尔滨工程大学 | Range-speed joint rapid estimation method for radar communication shared signals |
| CN114488064A (en) * | 2022-01-26 | 2022-05-13 | 哈尔滨工程大学 | Distance-speed joint estimation method |
| CN115685115A (en) * | 2022-09-27 | 2023-02-03 | 北京大学 | Target fusion sensing method based on superposition type communication sensing integrated waveform |
| US11632281B1 (en) * | 2020-12-07 | 2023-04-18 | University Of South Florida | System and method for joint radar-sensing and communication waveform utilizing unique word OFDM (UW-OFDM) |
| CN116008939A (en) * | 2023-01-03 | 2023-04-25 | 南京航空航天大学 | Ground penetrating radar coherent echo time delay estimation method based on sparse sampling |
-
2023
- 2023-11-13 CN CN202311500576.XA patent/CN117607849B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137410A (en) * | 2015-07-24 | 2015-12-09 | 西安电子科技大学 | OFDM-based high-resolution radar communication integration waveform optimization method |
| DE102015222043A1 (en) * | 2015-11-10 | 2017-05-11 | Robert Bosch Gmbh | Method for operating an OFDM radar device |
| US20210364616A1 (en) * | 2020-05-21 | 2021-11-25 | Hertzwell Pte. Ltd. | Radar system and computer-implemented method for radar target detection |
| US11632281B1 (en) * | 2020-12-07 | 2023-04-18 | University Of South Florida | System and method for joint radar-sensing and communication waveform utilizing unique word OFDM (UW-OFDM) |
| CN112986924A (en) * | 2021-01-28 | 2021-06-18 | 西安电子科技大学 | OFDM radar communication integrated range and speed fast super-resolution estimation method |
| CN113805170A (en) * | 2021-09-02 | 2021-12-17 | 江苏科技大学 | OFDM radar communication integrated high-speed target distance and speed estimation method |
| CN114422303A (en) * | 2021-12-29 | 2022-04-29 | 哈尔滨工程大学 | Range-speed joint rapid estimation method for radar communication shared signals |
| CN114488064A (en) * | 2022-01-26 | 2022-05-13 | 哈尔滨工程大学 | Distance-speed joint estimation method |
| CN115685115A (en) * | 2022-09-27 | 2023-02-03 | 北京大学 | Target fusion sensing method based on superposition type communication sensing integrated waveform |
| CN116008939A (en) * | 2023-01-03 | 2023-04-25 | 南京航空航天大学 | Ground penetrating radar coherent echo time delay estimation method based on sparse sampling |
Non-Patent Citations (4)
| Title |
|---|
| BEIBEI ZHU ET AL.: "Joint Matrix Completion and Randomized SVD Channel Estimation of the mmWave Massive MIMO", 2021 COMPUTING, COMMUNICATIONS AND IOT APPLICATIONS (COMCOMAP), 30 December 2021 (2021-12-30), pages 210 - 215 * |
| RUI ZHANG ET AL.: "Joint High-Resolution Range and DOA Estimation via MUSIC Method Based on Virtual Two-Dimensional Spatial Smoothing for OFDM Radar", HINDAWI, 21 November 2018 (2018-11-21), pages 1 - 10 * |
| 刘永军;廖桂生;杨志伟;许京伟;: "一种超分辨OFDM雷达通信一体化设计方法", 电子与信息学报, no. 02, 15 February 2016 (2016-02-15) * |
| 李明兵;谷亚彬;张林让;王正海;: "一种OFDM雷达通信一体化的距离超分辨方法", 电讯技术, no. 06, 28 June 2017 (2017-06-28) * |
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