A single-channel radar main lobe interference separation method, device and computer equipment

Abstract

The application relates to a single-channel radar main lobe interference separation method, device and computer equipment. The method comprises the following steps: receiving an observation signal through a single-channel radar, discretizing the observation signal at a first preset period to obtain a plurality of discrete signals; then, performing virtual multi-channel sampling and rearrangement on the obtained plurality of discrete signals to obtain a virtual multi-channel signal matrix; then, performing pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compression signal; obtaining the number of signal sources according to the virtual multi-channel pulse compression signal; taking the number of signal sources as prior information; and adopting a blind source separation algorithm to perform main lobe interference separation on the virtual multi-channel pulse compression signal. The method can separate target signals under the coexistence of multiple types and quantities of active interference, and still has a good separation effect under a low signal-to-noise ratio.

Description

Technical Field

[0001] This application relates to the field of radar signal processing technology, and in particular to a method, apparatus and computer equipment for separating main lobe interference in a single-channel radar. Background Technology

[0002] Faced with active mainlobe interference, traditional radar sidelobe anti-jamming techniques, such as sidelobe cancellation, become ineffective. Blind source separation, first proposed in the 1980s, separates the source signal from the observed mixed data vector when the prior information and transmission parameters of the source signal are unknown. It has wide applications in wireless communication, radar signal processing, and speech signal processing. In radar anti-jamming, blind source separation algorithms can be used to separate the target echo signal from the interference signal to achieve anti-jamming effects. Currently, most research on radar anti-active mainlobe interference focuses on array signal receiving models. Multi-channel blind source separation under this model is overdetermined (the number of observed signals is greater than the number of source signals). However, as the number of channels increases, system complexity and cost increase, and the error caused by differences between independent channels also increases.

[0003] Therefore, single-channel blind source separation has become an important research direction that has received widespread attention in recent years. Single-channel blind source separation refers to the process of receiving observation signals using only a single receiving sensor and then using this single observation signal to recover the individual source signals. Single-channel blind source separation requires estimating a large number of source signals with a small number of observation signals, making it an underdetermined blind source separation problem, which is extremely difficult to solve. Currently, the theoretical research and development of single-channel blind source separation is still immature. Existing blind source separation methods using multi-PRI sampling and virtual multi-channel reception are limited in their ability to handle a single type and a limited number of interferences. Existing multi-PRI sampling-based blind source separation algorithms perform signal separation in the time domain, resulting in a high degree of overlap between the target signal and interference, leading to limited separation effectiveness. Furthermore, time-domain signals are easily affected by noise, and the separation effect is poor when the signal-to-noise ratio is low. Summary of the Invention

[0004] Therefore, it is necessary to provide a single-channel radar main lobe interference separation method, device, and computer equipment to address the above-mentioned technical problems, so as to achieve the separation of target signals under the coexistence of multiple types and quantities of active interference, while still having a good separation effect under low signal-to-noise ratio.

[0005] A method for separating main lobe interference in a single-channel radar, the method comprising:

[0006] The observation signal is received by a single-channel radar, and the observation signal is discretized with a first preset period to obtain multiple discrete signals.

[0007] The plurality of discrete signals are sampled and rearranged in a virtual multi-channel manner with a second preset period to obtain a virtual multi-channel signal matrix. Each virtual channel signal in the virtual multi-channel signal matrix is ​​pulse compressed to obtain a virtual multi-channel pulse compressed signal. The number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged.

[0008] The number of signal sources is obtained based on the virtual multi-channel pulse compression signal. Using the number of signal sources as prior information, a blind source separation algorithm is used to separate the main lobe interference of the virtual multi-channel pulse compression signal.

[0009] A single-channel radar main lobe interference separation device, the device comprising:

[0010] A discretization module is used to receive observation signals through a single-channel radar and discretize the observation signals with a first preset period to obtain multiple discrete signals.

[0011] The pulse compression module is used to perform virtual multi-channel sampling and rearrangement of the multiple discrete signals with a second preset period to obtain a virtual multi-channel signal matrix, and to perform pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal; wherein the number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order between the discrete signals in each virtual channel signal remains unchanged;

[0012] The interference separation module is used to obtain the number of signal sources based on the virtual multi-channel pulse compression signal, and use the number of signal sources as prior information to perform main lobe interference separation on the virtual multi-channel pulse compression signal using a blind source separation algorithm.

[0013] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0014] The observation signal is received by a single-channel radar, and the observation signal is discretized with a first preset period to obtain multiple discrete signals.

[0015] The plurality of discrete signals are sampled and rearranged in a virtual multi-channel manner with a second preset period to obtain a virtual multi-channel signal matrix. Each virtual channel signal in the virtual multi-channel signal matrix is ​​pulse compressed to obtain a virtual multi-channel pulse compressed signal. The number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged.

[0016] The number of signal sources is obtained based on the virtual multi-channel pulse compression signal. Using the number of signal sources as prior information, a blind source separation algorithm is used to separate the main lobe interference of the virtual multi-channel pulse compression signal.

[0017] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, performs the following steps:

[0018] The observation signal is received by a single-channel radar, and the observation signal is discretized with a first preset period to obtain multiple discrete signals.

[0019] The plurality of discrete signals are sampled and rearranged in a virtual multi-channel manner with a second preset period to obtain a virtual multi-channel signal matrix. Each virtual channel signal in the virtual multi-channel signal matrix is ​​pulse compressed to obtain a virtual multi-channel pulse compressed signal. The number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged.

[0020] The number of signal sources is obtained based on the virtual multi-channel pulse compression signal. Using the number of signal sources as prior information, a blind source separation algorithm is used to separate the main lobe interference of the virtual multi-channel pulse compression signal.

[0021] The aforementioned single-channel radar main lobe interference separation method, apparatus, and computer equipment first receive observation signals through a single-channel radar and discretize the observation signals at a first preset period to obtain multiple discrete signals. Then, the multiple discrete signals are subjected to virtual multi-channel sampling and rearrangement to obtain a virtual multi-channel signal matrix. Next, pulse compression is performed on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal. The number of discrete signals in each virtual channel signal is the ratio of a second preset period to a first preset period, and the relative order between the discrete signals in each virtual channel signal remains unchanged. Finally, the number of signal sources is obtained based on the virtual multi-channel pulse compressed signal. Using the number of signal sources as prior information, a blind source separation algorithm is used to separate the main lobe interference of the virtual multi-channel pulse compressed signal. First, this invention discretizes the received observation signal into multiple discrete signals with a first preset period before performing subsequent sampling and rearrangement operations. Compared to directly sampling and rearranging the observation signal, discretization preprocessing is beneficial for subsequent sampling and rearrangement operations. Second, this invention performs rearrangement after virtual multi-channel sampling, so that in the obtained virtual multi-channel signal matrix, the number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged. In subsequent pulse compression, each virtual channel only needs to perform the same operation to achieve the effects of noise suppression and reduced overlap, which greatly reduces computational complexity and improves target detection speed. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a single-channel radar main lobe interference separation method in one embodiment;

[0023] Figure 2 This is a schematic diagram illustrating virtual multi-channel sampling and rearrangement of discrete signals in one embodiment;

[0024] Figure 3 In one embodiment, this is the aliased pulse compression signal within the first virtual channel before separation;

[0025] Figure 4 This is the separated target pulse compression signal in one embodiment;

[0026] Figure 5 In one embodiment, the separated intermittent sampling forwarding interference pulse compression signal is used.

[0027] Figure 6 This is the separated coherent noise interference pulse compression signal in one embodiment;

[0028] Figure 7 This is the separated dense false target interference pulse compression signal in one embodiment;

[0029] Figure 8 This represents the similarity of target waveforms under different signal-to-noise ratios in one embodiment.

[0030] Figure 9 This represents the target waveform similarity under different interference-to-signal ratios in one embodiment.

[0031] Figure 10 This represents the similarity of target waveforms under different virtual channels in one embodiment.

[0032] Figure 11 This is a structural block diagram of a single-channel radar main lobe interference separation device in one embodiment;

[0033] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] In one embodiment, such as Figure 1 As shown, a single-channel radar main lobe interference separation method is provided, including the following steps:

[0036] Step 102: Receive the observation signal through a single-channel radar, and discretize the observation signal with a first preset period to obtain multiple discrete signals.

[0037] Radars can be categorized into single-channel and multi-channel radars based on the number of receiving channels. Blind source separation in multi-channel radar is an overdetermined blind source separation (the number of observed signals exceeds the number of source signals), while single-channel blind source separation is an extreme case of underdetermined blind source separation. Single-channel radar receives active interference signals that are extremely realistic in the spatial, temporal, and frequency domains and overlap with the target signal. It requires fewer channels to estimate more signal sources, making it difficult for single-channel radar to separate the target signal from the aliased observed signals. When tracking a target, the radar receives the target echo signal. Due to the presence of jammers, the received signal includes both the target signal and the interference signal. The target echo contains information such as the target's position and velocity, while the interference signal interferes with the radar's target tracking and detection. Interference signals refer to active interference, including intermittent sampling relay interference, coherent noise interference, and dense false target relay interference. Simultaneously, it is required that each signal source has a different Doppler frequency shift.

[0038] Assuming the observed signal is x(t), it is discretized with Δ as the first preset period to obtain the discrete form of x(t) as x(kΔ), where k is the number of discrete signals.

[0039] Step 104: Perform virtual multi-channel sampling and rearrangement on multiple discrete signals with a second preset period to obtain a virtual multi-channel signal matrix. Perform pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal.

[0040] Virtual multi-channel sampling is performed on multiple discrete signals at a second preset period, expanding the original single-channel observation signal into a multi-channel signal. The number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged. Furthermore, by performing pulse compression on each virtual channel signal, noise in each virtual channel signal is suppressed, and the overlap between interference and target signals in the range direction is reduced compared to other domains, which is beneficial for subsequent signal source estimation and interference separation.

[0041] like Figure 2 As shown, a schematic diagram of virtual multi-channel sampling and rearrangement of discrete signals is provided, where PRT is the virtual multi-channel sampling period, i.e., the second preset period, L = PRT / Δ, and N is the number of virtual channels. It can be seen that the total number of discrete signals is k = (N × L).

[0042] Step 106: Based on the number of signal sources obtained from the virtual multi-channel pulse compression signal, and using the number of signal sources as prior information, a blind source separation algorithm is used to separate the main lobe interference of the virtual multi-channel pulse compression signal.

[0043] The signal source number estimation method can be based on criteria such as MDL and AIC. Blind source separation algorithms can include Joint Characteristic Matrix Diagonalization (JADE), Fast Independent Component Analysis (Fast-ICA), etc. You can choose the one that suits you best.

[0044] In the aforementioned single-channel radar main lobe interference separation method, firstly, the received observation signal is discretized with a first preset period to obtain multiple discrete signals, and then subsequent sampling and rearrangement operations are performed. Compared with directly sampling and rearranging the observation signal, discretization preprocessing is beneficial to subsequent sampling and rearrangement operations. Secondly, after virtual multi-channel sampling, the present invention also performs rearrangement, so that in the obtained virtual multi-channel signal matrix, the number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order between the discrete signals in each virtual channel signal remains unchanged. In the subsequent pulse compression, each virtual channel only needs to perform the same operation to achieve noise suppression and reduce overlap, which greatly reduces computational complexity and improves target detection speed.

[0045] In one embodiment, multiple discrete signals are virtually sampled and rearranged using a second preset period to obtain a virtual multi-channel signal matrix, including:

[0046] Multiple discrete signals are sampled virtually using a second preset period to obtain multiple virtual channel signals:

[0047] x i (k)=x[((i-1)L+k)Δ]

[0048] Where, x i (k) represents the kth discrete signal in the i-th virtual channel signal, L represents the number of discrete signals in each virtual channel signal, Δ represents the first preset period, L=PRT / Δ, PRT represents the second preset period;

[0049] The signals from multiple virtual channels are rearranged to obtain a virtual multi-channel signal matrix:

[0050]

[0051] Where X(k) represents the virtual multi-channel signal matrix, and N represents the number of virtual channel signals.

[0052] In one embodiment, pulse compression is performed on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal, including:

[0053] A matched filter is used to perform pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain the virtual multi-channel pulse compressed signal:

[0054]

[0055] Where Y(k) represents the virtual multi-channel pulse compression signal, and h(k) represents the impulse response of the matched filter. Represents the convolution symbol.

[0056] Pulse compression can be performed using an FIR filter. Let the discrete form of the single-channel radar transmitted signal be s. t (k), then the impulse response of the matched filter is

[0057] In one embodiment, obtaining the number of signal sources based on the virtual multi-channel pulse compression signal includes:

[0058] Calculate the covariance matrix of the virtual multi-channel pulse compression signal:

[0059]

[0060] Where Y represents the virtual multi-channel pulse compression signal, The covariance matrix representing the virtual multi-channel pulse compression signal;

[0061] Eigenvalue decomposition of the covariance matrix yields multiple eigenvalue vectors. Arranging these eigenvalue vectors in descending order results in the eigenvalue matrix. for eigenvalues, Given the corresponding eigenvectors, arrange the eigenvalue vectors Λ in descending order to obtain Λ. s =sort(Λ)={l1,l2,...l N};

[0062] The number of signal sources is estimated from the eigenvalue matrix using the minimum length description method:

[0063]

[0064] Where MDL stands for Minimum Length Description, M represents the number of signal sources, and l i This represents the characteristic value of the i-th virtual multi-channel pulse compression signal.

[0065] In one embodiment, a blind source separation algorithm is used to separate the main lobe interference of a virtual multi-channel pulse compression signal, including:

[0066] The whitening matrix is ​​calculated based on the covariance matrix of the virtual multi-channel pulse compression signal:

[0067]

[0068] Where W represents the whitening matrix, {l1,l2,…,l M} represents the first M largest eigenvalues ​​of the covariance matrix. Represents {l1,l2,…,l M The corresponding feature vectors, The noise variance estimate is obtained by averaging the remaining NM smaller eigenvalues.

[0069] The whitening signal is obtained based on the whitening matrix and the virtual multi-channel pulse compression signal:

[0070] z=WY(k)=W(As(k)+N(k))=Us(k)+WN(k)

[0071]

[0072] Y(k)=As(k)+N(k)

[0073] A = [a(f1), a(f2), ... a(f...] M )]

[0074]

[0075] s = [s1(k) s2(k) ... s M (k)] T

[0076] N = [n1(k), n2(k), ... n M (k)] T

[0077] Where z represents the whitening signal, f M The Doppler frequency shift of the Mth signal source is represented by s. M (k) represents the echo signal of the Mth signal source, n M (k) represents the noise signal for the M-th signal source, U={μ1,μ2,…,μ M} represents a unitary matrix;

[0078] Calculate the fourth-order cumulant matrix of the whitened signal:

[0079]

[0080]

[0081] Among them, [Q z (T)] ij Q represents the fourth-order cumulant matrix. z The (i,j)th element of (T), where T is any non-zero T*T matrix, [T] lq It is its (l,q)th element, where Cum represents the fourth-order cumulant operator.

[0082] Diagonalize the fourth-order cumulant matrix Q z (T), thus obtaining an estimate of the unitary matrix:

[0083]

[0084] Where ∑ represents a diagonal matrix, This represents the estimate of the unitary matrix;

[0085] The target signal is obtained by estimating the unitary matrix and the whitening signal, thus achieving main lobe interference separation.

[0086]

[0087] in, This represents the estimated target signal.

[0088] The effectiveness of this method is further illustrated by the following experiments:

[0089] Assuming the environment contains three types of active interference: intermittent sampling forwarding interference, dense forwarding interference, and coherent noise interference, the radar parameter settings for the simulation experiment are shown in Table 1:

[0090] Table 1 Radar Parameter Settings

[0091] Relevant parameters / units numerical values <![CDATA[Carrier frequency f0]]> 3.14GHz Pulse width T 20μs Signal bandwidth B 10MHz Pulse Repetition Period (PRT) 100μs Number of transmitted pulses (pm) 32 Target radial velocity v 30m / s

[0092] 1) The sampling period for intermittent sampling direct forwarding interference is set to 10 μs with a duty cycle of 0.5. The sampling duration for dense forwarding interference is 5 μs, with targets set at intervals of 7.5 μs, for a total of 5 false targets. The interference-to-signal ratio (JSR) is set to 20 dB, and the signal-to-noise ratio (SNR) to 10 dB. The parameter settings for each interference source are shown in Table 2.

[0093] Table 2 Interference Source Parameter Settings

[0094]

[0095]

[0096] Using a single channel to receive radar signals, the result of mixed signal pulse compression within a virtual channel is as follows: Figure 3 Following the remaining steps to separate the aliased signals, the separated target pulse compressed signal can be obtained, as shown below. Figure 4 Intermittent sampling and forwarding of interference pulse compressed signals, such as Figure 5 Coherent noise interference pulse compression signal, such as Figure 6 Dense decoy interference pulse compression signal, such as Figure 7 .

[0097] 2) When the fixed number of virtual channels is 32 transmitted pulses, the interference-to-signal ratio is constant at 14dB, and the signal-to-noise ratio varies from -10 to 20dB, the method of this invention is used to perform blind separation of the mixed signal, and the waveform similarity results are as follows: Figure 8Since the waveform similarity of the separation results of the algorithm of this invention tends to stabilize when the signal-to-noise ratio is greater than 10dB, the influence of the interference-to-signal ratio on the performance of the two algorithms was studied when the signal-to-noise ratio was fixed at 10dB and the interference-to-signal ratio was -10 to 20dB. The results are as follows. Figure 9 As shown.

[0098] 3) From the results of 1) and 2), it can be seen that a high waveform similarity can be obtained when the interference-to-signal ratio is 10dB and the signal-to-noise ratio is 15dB, indicating that the algorithm of this invention has a good separation effect. Therefore, with the interference-to-signal ratio fixed at 10dB and the signal-to-noise ratio at 15dB, the impact of the number of virtual channels on the algorithm performance was studied, and the results are as follows. Figure 10 .

[0099] Depend on Figure 3 It can be seen that the target signal pulse compression result is submerged in the interference signal pulse compression result, making it difficult to distinguish the real target. Figures 4 to 7 As can be seen, the pulse compression result obtained by this separation algorithm effectively separates the target signal and the interference signal. Figure 4 The target signal pulse compression result after separation shows that the distance to the target is 3000m, which meets the setting. Figure 5 The result of the pulse compression of the separated intermittently sampled signal is quite similar to the target signal. However, due to the forwarding delay, the distance image result deviates from the setting. Figure 6 The result is the separated noise convolution interference pulse compression result. Some dense forwarding interference pulse compression data still remain in the separation result. Figure 7 The distance profile results after separation of densely relayed interference pulse compression are consistent with the settings, and the five false target spikes conform to the setting of five relays. In summary, when multiple active interferences are present, the method proposed in this paper, which simulates the receiving signal through a multi-PRI virtual channel and performs blind source separation after pulse compression, has a good separation effect, and the target signal pulse compression can be effectively separated.

[0100] Depend on Figure 8 As can be seen, the algorithm of this invention performs better and better with the increase of SNR. Even at a low signal-to-noise ratio, around -5dB, it still maintains a waveform similarity of over 90% for the separated target pulse compression signals. Therefore, the algorithm of this invention has a certain degree of noise suppression performance and good robustness. Figure 9 As can be seen, the similarity of the target waveforms after separation by the algorithm of this invention remains almost unchanged under different JSRs. Therefore, the performance of the algorithm is not significantly related to the JSR, that is, the algorithm is not sensitive to the JSR.

[0101] Depend on Figure 10As can be seen, with the increase of the number of selected virtual channels (PRIs), the amount of data used during separation increases, and the waveform similarity after separation by the algorithm of this invention shows an increasing trend, resulting in better separation performance. This is because the more virtual channels there are, the greater the effective information content of the signal. This transforms the blind source separation algorithm model from underdetermined to positive definite, significantly increasing the amount of information during separation and improving the algorithm's separation effect.

[0102] Experimental results show that this invention achieves effective separation of target signals under conditions of coexisting active interference of multiple main lobe types, thereby suppressing interference and enabling single-channel radar to more accurately identify target information. This invention reduces the impact of noise on the separation effect through pulse compression, maintaining over 90% waveform similarity of the target signal even at low signal-to-noise ratios. Furthermore, the separation effect of this invention improves with increasing numbers of virtual channels, and the waveform similarity gradually approaches 100%.

[0103] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0104] In one embodiment, such as Figure 11 As shown, a single-channel radar main lobe interference separation device is provided, comprising: a discretization module, a pulse compression module, and an interference separation module, wherein:

[0105] The discretization module is used to receive observation signals through a single-channel radar and discretize the observation signals with a first preset period to obtain multiple discrete signals.

[0106] The pulse compression module is used to perform virtual multi-channel sampling and rearrangement of multiple discrete signals with a second preset period to obtain a virtual multi-channel signal matrix. The module then performs pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal. The number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged.

[0107] The interference separation module is used to obtain the number of signal sources based on the virtual multi-channel pulse compression signal, and use the number of signal sources as prior information to perform main lobe interference separation on the virtual multi-channel pulse compression signal using a blind source separation algorithm.

[0108] For specific limitations regarding a single-channel radar main lobe interference separation device, please refer to the limitations of a single-channel radar main lobe interference separation method described above, which will not be repeated here. Each module in the aforementioned single-channel radar main lobe interference separation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0109] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores observed signal data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a single-channel radar main lobe interference separation method.

[0110] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0112] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for separating main lobe interference in a single-channel radar, characterized in that, The method includes: The observation signal is received by a single-channel radar, and the observation signal is discretized with a first preset period to obtain multiple discrete signals. The plurality of discrete signals are sampled and rearranged in a virtual multi-channel manner with a second preset period to obtain a virtual multi-channel signal matrix. Each virtual channel signal in the virtual multi-channel signal matrix is ​​pulse compressed to obtain a virtual multi-channel pulse compressed signal. The number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order of the discrete signals in each virtual channel signal remains unchanged. The number of signal sources is obtained based on the virtual multi-channel pulse compression signal. Using the number of signal sources as prior information, a blind source separation algorithm is used to separate the main lobe interference of the virtual multi-channel pulse compression signal.

2. The method according to claim 1, characterized in that, The plurality of discrete signals are subjected to virtual multi-channel sampling and rearrangement at a second preset period to obtain a virtual multi-channel signal matrix, including: The multiple discrete signals are subjected to virtual multi-channel sampling at a second preset period to obtain multiple virtual channel signals: in, Indicates the first The first virtual channel signal A discrete signal This indicates the number of discrete signals in each virtual channel signal. Indicates the first preset period. , Indicates the second preset period; The signals from multiple virtual channels are rearranged to obtain a virtual multi-channel signal matrix: in, Represents a virtual multi-channel signal matrix. Indicates the number of virtual channel signals.

3. The method according to claim 2, characterized in that, The virtual channel signals in the virtual multi-channel signal matrix are pulse compressed to obtain a virtual multi-channel pulse compressed signal, including: A matched filter is used to perform pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal: in, This indicates a virtual multi-channel pulse compression signal. This represents the impulse response of the matched filter. Represents the convolution symbol.

4. The method according to claim 3, characterized in that, The number of signal sources is obtained based on the virtual multi-channel pulse compression signal, including: Calculate the covariance matrix of the virtual multi-channel pulse compression signal: in, This indicates a virtual multi-channel pulse compression signal. The covariance matrix representing the virtual multi-channel pulse compression signal; The covariance matrix is ​​decomposed into eigenvalues ​​to obtain multiple eigenvalue vectors, and the multiple eigenvalue vectors are arranged in descending order to obtain an eigenvalue matrix. The number of signal sources is estimated from the eigenvalue matrix using the minimum length description method: in, This represents the minimum length description method. Indicates the number of signal sources. Indicates the first The characteristic values ​​of a virtual multi-channel pulse compression signal.

5. The method according to claim 4, characterized in that, The main lobe interference separation of the virtual multi-channel pulse compression signal is performed using a blind source separation algorithm, including: The whitening matrix is ​​calculated based on the covariance matrix of the virtual multi-channel pulse compression signal: in, Represents the whitening matrix. These represent the first M largest eigenvalues ​​of the covariance matrix. express The corresponding feature vectors, By the remaining The noise variance estimate obtained by taking the mean of the eigenvalues; The whitening signal is obtained based on the whitening matrix and the virtual multi-channel pulse compression signal: in, Indicates whitening signal, Indicates the first Doppler frequency shift of each signal source Indicates the first The echo signal from each signal source Indicates the first Noise signal from each signal source Represents a unitary matrix; Calculate the fourth-order cumulant matrix of the whitened signal: in, Represents a fourth-order cumulant matrix The One element, For any non-zero matrix, It is its first One element, This represents a fourth-order cumulant operator. , ; Diagonalizing the fourth-order cumulant matrix The estimate of the unitary matrix is ​​obtained: in, Represents a diagonal matrix. This represents the estimate of the unitary matrix; The target signal is obtained based on the estimation of the unitary matrix and the whitening signal, thereby achieving main lobe interference separation; in, This represents the estimated target signal.

6. A single-channel radar main lobe interference separation device, characterized in that, The device includes: A discretization module is used to receive observation signals through a single-channel radar and discretize the observation signals with a first preset period to obtain multiple discrete signals. The pulse compression module is used to perform virtual multi-channel sampling and rearrangement of the multiple discrete signals with a second preset period to obtain a virtual multi-channel signal matrix, and to perform pulse compression on each virtual channel signal in the virtual multi-channel signal matrix to obtain a virtual multi-channel pulse compressed signal; wherein the number of discrete signals in each virtual channel signal is the ratio of the second preset period to the first preset period, and the relative order between the discrete signals in each virtual channel signal remains unchanged; The interference separation module is used to obtain the number of signal sources based on the virtual multi-channel pulse compression signal, and use the number of signal sources as prior information to perform main lobe interference separation on the virtual multi-channel pulse compression signal using a blind source separation algorithm.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

Images