Sparse orthogonal filtering anti-interference method based on transmit-receive split radar

Abstract

The invention discloses a sparse orthogonal filtering anti-interference method based on transmitting and receiving split radars. The method comprises the following steps: firstly, acquiring radar echo data and processing the radar echo data to obtain baseband data containing interference signals; then, transmitting and receiving steering vectors are constructed; receiving echo data of each receiving array element is obtained; constructing a sparse base, performing sparse representation on data of each array element after orthogonal waveform matched filtering processing, performing measurement mapping on the data, and processing an obtained comprehensive mapping result to obtain a measurement vector; constructing a cost function according to the measurement vector; and finally, obtaining a target angle measurement estimation value by using second-order cone programming. The invention relates to a method for solving the problem that a transmitting-receiving split radar suffers from main and side lobe interference under complex electronic interference, interference signals can be effectively suppressed, the target discovery probability is improved, and meanwhile, the angle measurement precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and in particular relates to a sparse orthogonal filtering anti-interference method based on a radar with separate transmitter and receiver locations. Background Art

[0002] The sparse representation problem of target angle estimation in a dual-transmitter radar operating in an interference environment has become a research hotspot and a challenge in radar signal processing. Jammers can disrupt target detection and angle tracking by releasing both mainlobe interference and multiple sidelobe interferences, reducing the probability of target detection and further increasing the false alarm rate. Dual-transmitter radars primarily improve target angle estimation accuracy by increasing the equivalent antenna virtual aperture. However, when multiple mixed interferences (mainlobe and sidelobe interference) coexist, the interference signals enter the radar receiver along with the target through the airspace, severely limiting target detection and recognition performance. This poses a particular challenge to target angle estimation. Therefore, addressing the issue of mixed mainlobe and sidelobe interference affecting target angle estimation accuracy in dual-transmitter radars requires effective suppression of interference signal energy, achieving accurate spatial filtering, and leveraging the dual-transmitter radar's inherent advantage of a large equivalent aperture to improve target angle estimation accuracy.

[0003] Chinese patent document CN114236542A proposes a synthetic aperture radar-based integrated anti-interference imaging detection and identification method. This method primarily combines the overall workflow of an actual imaging detection system and, from a systemic perspective, proposes a multi-dimensional joint anti-interference method that integrates the system, signal domain, and image domain. However, this method's use of direct zeroing can result in severe target signal loss, and inappropriate threshold selection can also make target detection and identification difficult. Furthermore, the method's use of sequential statistical updates is not robust to differential forwarding broadband interference and can occasionally fail, severely impacting target detection probability. Therefore, this method is not universally applicable.

[0004] A sparse low-sidelobe recovery method for high-resolution range-Doppler spectrum in a random stepped-frequency radar based on Hankel reconstruction matrix filling is proposed in the Journal of Radars (2024, Vol. 13, pp. 201-214). This method uses a low-rank matrix filling concept to supplement the missing samples caused by sparse waveform sensing in the time-frequency domain, thereby recovering the target's continuous phase coherence information. This method effectively addresses underdetermined estimation problems; however, this algorithm requires the use of a random stepped-frequency radar operating mode and cannot address the impact of both mainlobe and sidelobe interference on target detection. Furthermore, its accuracy in radar target angle measurement has not been verified or validated in practice. Summary of the Invention

[0005] The purpose of the present invention is to provide a sparse orthogonal filtering anti-interference method based on a transmit-receive split radar, which solves the problem that the transmit-receive split radar suffers from main-lobe and side-lobe interference under complex electronic interference, effectively suppresses interference signals, and improves the target angle measurement accuracy.

[0006] To achieve the purpose of the present invention, the present invention provides a sparse orthogonal filtering anti-interference method based on a transmitting and receiving separated radar, comprising the following steps:

[0007] Step 1: The radar echo data containing the interference signal is collected by the analog-to-digital converter of the radar component to obtain intermediate frequency data containing the interference signal, and the baseband data containing the interference signal is obtained after digital down-conversion;

[0008] Step 2: Construct the transmit and receive steering vectors based on the radar wavelength, the number of array elements in the transmit array, the number of array elements in the receive array, and the distance between two adjacent array elements.

[0009] Step 3: Using the constructed transmit and receive steering vectors, a projection mapping is constructed for the baseband data containing the interference signal to obtain the received echo data of each receiving array element;

[0010] Step 4: performing orthogonal waveform matched filtering on the received echo data of each array element, further dividing the entire angular space into discrete sampling space grids, constructing a sparse basis, and performing sparse representation on the data of each array element after the orthogonal waveform matched filtering process;

[0011] Step 5: performing orthogonal waveform matched filtering processing on the echo data received by each array element and performing measurement mapping on the sparsely represented data, performing comprehensive mapping on the data of all array elements, and then performing matrix vectorization on the comprehensive mapping result to obtain a measurement vector;

[0012] Step 6: constructing a cost function based on the measurement vector result;

[0013] Step 7: Based on the constructed cost function, use second-order cone programming to solve the problem and obtain the target angle measurement estimate.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the sparse orthogonal filtering anti-interference method based on a radar with a bi-located transmitter and receiver is implemented.

[0015] A non-transitory computer-readable storage medium, characterized in that the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned sparse orthogonal filtering anti-interference method based on a radar with a bi-located transmitter and receiver.

[0016] A computer program product includes computer program instructions. When the computer program instructions are executed on a computer, the computer is caused to execute the above-mentioned sparse orthogonal filtering anti-interference method based on a radar with bi-located transmitter and receiver.

[0017] Compared with the existing technology, the significant progress of the present invention is that: the present invention is directed to solving the problem of main-lobe and side-lobe interference suffered by the transmitting and receiving separate radar under complex electronic interference, effectively improving the interference signal suppression rate, and greatly improving the target detection probability and angle measurement accuracy.

[0018] In order to more clearly illustrate the functional characteristics and structural parameters of the present invention, further description is given below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flow chart of the method steps of the present invention;

[0021] Figure 2 This is a diagram of target angle measurement in the presence of main-lobe and side-lobe interference in an embodiment of the present invention;

[0022] Figure 3 3 is a comparison chart of the target angle measurement error accuracy and the Cramer-Rao bound after processing using the sparse orthogonal filtering anti-interference method based on the transmitting and receiving radar in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] The present invention is a sparse orthogonal filtering anti-interference method based on a transmitting and receiving radar, combined with Figure 1 , including the following steps:

[0025] Step 1: The radar echo data containing the interference signal is collected by the analog-to-digital converter of the radar component to obtain intermediate frequency data containing the interference signal, and the baseband data containing the interference signal is obtained after digital down-conversion;

[0026] Step 2: Construct the transmit and receive steering vectors based on the radar wavelength, the number of array elements in the transmit array, the number of array elements in the receive array, and the distance between two adjacent array elements.

[0027] The step 2 is specifically shown in the following formula:

[0028] ; ;

[0029] in, represents the launch steering vector, represents the receiving steering vector, Indicates the number of array elements of the uniform transmission linear array of the radar system with separate transmission and reception. ; Indicates the number of array elements of the uniform receiving linear array of the transmitting and receiving radar system. ; represents the azimuth of the kth signal source, and ( ), where K represents the number of signal sources observed in the noise background; Represents complex data with N rows and 1 column, Represents complex data with M rows and 1 column; and Respectively The mth element of The nth element of is the distance between two adjacent array elements, is the radar wavelength.

[0030] Step 3: Using the constructed transmit and receive steering vectors, a projection mapping is constructed for the baseband data containing the interference signal to obtain the received echo data of each receiving array element;

[0031] The step 3 is specifically shown in the following formula:

[0032] ;

[0033] in, represents the received echo of the nth receiving element, Indicates slow time-pulse number, ; and is the number of targets and interferences among the K observed signal sources, ; Indicates the number of active interference or ground scatter active interference, represents its azimuth, ; ( ), ( ), ( ) represent the complex amplitudes of the rth target, the ith signal source interference, and the jth active interference, respectively. , is the azimuth angle of the target and signal source relative to the receiving radar array; Indicates the number of fast time sampling points of a pulse dimensional noise, and Represents the number of fast time sampling points of a pulse dimensional transmit-receive radar signal matrix and the j-th interference signal matrix; since the active interference is independent of the transmit-receive radar transmit array, Represents the transmit steering vector of the active jammer.

[0034] Step 4: performing orthogonal waveform matched filtering on the received echo data of each array element, further dividing the entire angular space into discrete sampling space grids, constructing a sparse basis, and performing sparse representation on the data of each array element after the orthogonal waveform matched filtering process;

[0035] The step 4 is specifically as follows:

[0036] Assume that the entire angle space is first discretized into a series of dense angle grids , ,in Indicates the number of angle grids into which the entire angle space is discretized, so that the measurement angle of each detectable signal source and interference falls on the angle grid, and assumes that each baseband data containing the interference signal is processed by orthogonal waveform matching filter (traditional pulse compression processing), and the number of times the nth array element receives the echo is recorded. After orthogonal waveform matching filtering processing, :

[0037] ;

[0038] in, It represents the noise-free data after the echo data received by the nth array element is processed by orthogonal waveform matching filtering;

[0039] For the nth receiving array element, we can construct dimensional sparse basis As shown in the following formula:

[0040] ;

[0041] Then It can be sparsely represented as:

[0042]

[0043] in, for -dimensional sparse coefficient vector, T represents the transpose operation of the matrix signal.

[0044] Step 5: performing orthogonal waveform matched filtering processing on the echo data received by each array element and performing measurement mapping on the sparsely represented data, performing comprehensive mapping on the data of all array elements, and then performing matrix vectorization on the comprehensive mapping result to obtain a measurement vector;

[0045] Measurement mapping results for:

[0046] ;

[0047] in, Indicates the nth receiving element dimensional perception matrix, express Measurement noise, represents the measurement matrix, and it is related to There is little correlation, Indicates in The data measurement mapping result of the nth receiving array element within the pulse is: express -dimensional sparse coefficient vector;

[0048] Set in The comprehensive mapping output of N receiving array elements within a pulse is recorded as , , the dimension is The comprehensive mapping output of N receiving array elements can be expressed as:

[0049] ;

[0050] in, express -dimensional sparse coefficient vector, express dimensional noise matrix;

[0051] Will Matrix vectorization to obtain measurement vector :

[0052] ;

[0053] in, Represents N receiving elements dimensional perception matrix, Indicates the transpose operation of matrix T. express dimensional noise vector.

[0054] Step 6: constructing a cost function based on the result of the measurement vector;

[0055] The cost function The construction is as follows:

[0056] ;

[0057] in, Represents the perception matrix With sparse coefficient vector The result of multiplication, Represents the minimized sparse coefficient vector The 1-norm of .

[0058] Step 7: Based on the constructed cost function, use second-order cone programming to solve the problem and obtain the target angle measurement estimate;

[0059] Solve the optimization problem according to the second-order cone programming, and use the measurement vector Determine the sparse coefficient vector , to achieve angle measurement estimation; based on the perception matrix The sparse vector of the solution The target among the K signal sources observed in The angle corresponding to the largest non-zero sparse coefficient can be considered as the target angle measurement estimate.

[0060] Example

[0061] Based on the above implementation steps:

[0062] Assume that a radar with separate transmitter and receiver arrays has 16 transmitter elements and 8 receiver elements, and the number of fast time and fast shot is Set to 512, slow time fast shot number The number of array elements is set to 256. The radar operates at a frequency of 3 GHz, and the spacing between adjacent array elements is set to half a wavelength, and the angle grid division interval is 0.1. o , the target signal-to-noise ratio is set to 10dB, the target signal angle is set to 12 degrees and 25 degrees with two targets. The signal source signal noise power is set to 25dB, and the angle distribution is set as follows -40 o and 55 o , the active interference signal noise power is set to 50dB, and the angle distribution is set as follows 12 o , 18 o and 25 o After 1000 Monte Carlo simulation experiments, the target angle measurement accuracy is quantitatively determined based on the comparative analysis of the experimental results and the Cramer-Rao upper bound, and finally the effectiveness of the method of the present invention is analyzed.

[0063] like Figure 2The figure shows the target angle, signal source, and detection probability of active interference targets after the original radar baseband data is subjected to interference suppression using a sparse orthogonal filtering anti-interference method based on a separate transmitter-receiver radar. It can be seen that when the signal-to-noise ratio is greater than 5 dB, the detection probability approaches the theoretical detection probability of the target corresponding to the Cramer-Rao bound.

[0064] like Figure 3 Figure 2 shows the target angle measurement error accuracy statistics after processing using a sparse orthogonal filtering anti-interference method based on a bi-directional radar. The figure shows that the method used in the present invention can effectively improve angle measurement accuracy, approaching the Cramer-Rao bound in the presence of mainlobe and sidelobe interference. This fully demonstrates the effectiveness and feasibility of the method of the present invention.

[0065] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0066] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A sparse orthogonal filtering anti-interference method based on a radar with separate transmitter and receiver locations, characterized in that: The following steps are involved: Step 1: The radar echo data containing the interference signal is collected by the analog-to-digital converter of the radar component to obtain intermediate frequency data containing the interference signal, and the baseband data containing the interference signal is obtained after digital down-conversion; Step 2: Construct the transmit and receive steering vectors based on the radar wavelength, the number of array elements in the transmit array, the number of array elements in the receive array, and the distance between two adjacent array elements. Step 3: Using the constructed transmit and receive steering vectors, a projection mapping is constructed for the baseband data containing the interference signal to obtain the received echo data of each receiving array element; Step 4: performing orthogonal waveform matched filtering on the received echo data of each array element, further dividing the entire angular space into discrete sampling space grids, constructing a sparse basis, and performing sparse representation on the data of each array element after the orthogonal waveform matched filtering process; Step 5: performing orthogonal waveform matched filtering processing on the echo data received by each array element and performing measurement mapping on the sparsely represented data, performing comprehensive mapping on the data of all array elements, and then performing matrix vectorization on the comprehensive mapping result to obtain a measurement vector; Step 6: constructing a cost function based on the measurement vector result; Step 7: Based on the constructed cost function, use second-order cone programming to solve the problem and obtain the target angle measurement estimate.

2. The sparse orthogonal filtering anti-interference method based on a transmitting and receiving radar according to claim 1 is characterized in that: The step 2 is specifically shown in the following formula: ； ； in, represents the launch steering vector, represents the receiving steering vector, Indicates the number of array elements of the uniform transmission linear array of the radar system with separate transmission and reception. ; Indicates the number of array elements of the uniform receiving linear array of the transmitting and receiving radar system. ; represents the azimuth of the kth signal source, and ( ), where K represents the number of signal sources observed in the noise background; Represents complex data with N rows and 1 column, Represents complex data with M rows and 1 column; and Respectively The mth element of The nth element of is the distance between two adjacent array elements, is the radar wavelength.

3. The sparse orthogonal filtering anti-interference method based on a transmitting and receiving radar according to claim 2 is characterized in that: The step 3 is specifically shown in the following formula: ； in, represents the received echo of the nth receiving element, Indicates slow time-pulse number, ; and is the number of targets and interferences among the K observed signal sources, ; Indicates the number of active interference or ground scatter active interference, represents its azimuth, ; ( ), ( ), ( ) represent the complex amplitudes of the rth target, the ith signal source interference, and the jth active interference, respectively. , is the azimuth angle of the target and signal source relative to the receiving radar array; Indicates the number of fast time sampling points of a pulse dimensional noise, and Represents the number of fast time sampling points of a pulse dimensional transmit-receive radar signal matrix and the j-th interference signal matrix; since the active interference is independent of the transmit-receive radar transmit array, Represents the transmit steering vector of the active jammer.

4. The sparse orthogonal filtering anti-interference method based on a transmitting and receiving radar according to claim 3 is characterized in that: The step 4 is specifically as follows: Assume that the entire angle space is first discretized into a series of dense angle grids , ,in Indicates the number of angle grids into which the entire angle space is discretized, so that the measurement angle of each detectable signal source and interference falls on the angle grid, and assumes that each baseband data containing the interference signal is processed by orthogonal waveform matching filtering, and the nth array element receives the echo. After orthogonal waveform matching filtering processing, : ； in, It represents the noise-free data after the echo data received by the nth array element is processed by orthogonal waveform matching filtering; For the nth receiving array element, we can construct dimensional sparse basis As shown in the following formula: ； Then It can be sparsely represented as: ； in, for -dimensional sparse coefficient vector, T represents the transpose operation of the matrix signal.

5. The sparse orthogonal filtering anti-interference method based on a transmitting and receiving separated radar according to claim 4 is characterized in that: The result of the measurement mapping in step 5 for: ； in, Indicates the nth receiving element dimensional perception matrix, express measurement noise, represents the measurement matrix, and it is related to There is little correlation, Indicates in The data measurement mapping result of the nth receiving array element within the pulse is: express -dimensional sparse coefficient vector; Set in The comprehensive mapping output of N receiving array elements within a pulse is recorded as , , the dimension is The comprehensive mapping output of N receiving array elements can be expressed as: ； in, express -dimensional sparse coefficient vector, express dimensional noise matrix; Will Matrix vectorization to obtain measurement vector : ； in, Represents N receiving elements dimensional perception matrix, Indicates the transpose operation of matrix T. express dimensional noise vector.

6. The sparse orthogonal filtering anti-interference method based on a transmitting and receiving radar according to claim 5 is characterized in that: The cost function of step 6 Build: ； in, Represents the perception matrix With sparse coefficient vector The result of multiplication, Represents the minimized sparse coefficient vector The 1-norm of .

7. The sparse orthogonal filtering anti-interference method based on a transmitting and receiving radar according to claim 6 is characterized in that: The step 7 solves the optimization problem according to the second-order cone programming, using the measurement vector Determine the sparse coefficient vector , to achieve angle measurement estimation; based on the perception matrix The sparse vector of the solution The target among the K signal sources observed in The angle corresponding to the largest non-zero sparse coefficient can be considered as the target angle measurement estimate.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising computer program instructions, characterized in that When the computer program instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 7.

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