Method for suppressing main lobe jamming of multi-station radar system based on oblique projection

Abstract

The application discloses a method for suppressing main lobe interference of a multi-station radar system based on oblique projection. The method aims to solve the problem that the estimation of noise subspace is not accurate due to the energy leakage of interference, so that the noise subspace is no longer orthogonal to the interference subspace, and the orthogonal projection anti-interference performance is invalid. The implementation steps of the application include: directing the receiving antenna beams of each radar station in the multi-station radar system to the target and the area where the jammer is located; aligning the echo baseband signal vectors of each radar station in the multi-station radar system in the time domain to obtain a receiving signal matrix of the radar system; generating a covariance matrix; estimating a signal subspace; constructing an oblique projection operator; and multiplying the receiving signal matrix of the radar system by the oblique projection operator to obtain a signal after interference suppression. The application has the advantage that the suppression type interference signal entering from the main lobe of the radar station antenna can be effectively suppressed in a low signal-to-noise ratio scene.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, and more specifically relates to the field of electronic countermeasures technology, specifically a method for suppressing main lobe suppression jamming in a multi-station radar system based on oblique projection. This invention can be used to suppress suppression jamming signals received by a multi-station radar system. Background Technology

[0002] Suppression jamming is a common type of active jamming that achieves its effect by radiating high-power noise signals into the target radar's operating area. This causes the target echo to be submerged in noise in the time and frequency domains, thus preventing the radar from performing target detection and tracking. Depending on the location of the jamming signal entering the radar pattern, it can be divided into sidelobe suppression jamming and mainlobe suppression jamming. For sidelobe suppression jamming, radar stations can eliminate it during signal processing through methods such as sidelobe cancellation or sidelobe masking; existing methods are very mature. However, for mainlobe suppression jamming, the target echo signal received by the radar station is masked by high-power noise interference, significantly reducing the radar's performance in target detection and tracking. Conventional anti-jamming methods are no longer effective against mainlobe suppression jamming. For mainlobe suppression jamming, monostatic radar systems can suppress the interference through time-domain cancellation or frequency-domain notch filtering. However, monostatic radar can only detect the target environment from a single perspective, resulting in limited information dimensionality and thus limited anti-jamming performance.

[0003] Multi-station radar cooperation is an important trend in the development of radar anti-jamming technology. Due to their spatially distributed nature, multi-station radar systems detect targets from multiple perspectives. By fusing and processing the information detected by multiple radars, they can effectively counter mainlobe suppression jamming. Therefore, multi-station radar systems can significantly improve the anti-jamming capability of radar by effectively mitigating mainlobe suppression jamming.

[0004] Xi'an University of Electronic Science and Technology proposed a multi-station radar anti-suppression jamming method in its patent application "Networked Radar Suppression of Suppressive Main Lobe Jamming Based on Subspace Projection" (Application No.: 2014100198633; Authorization Announcement No.: CN 103728595 B). The implementation scheme utilizes the different fluctuation characteristics of the target echo signal and the jamming signal. Specifically, the target echo signal received by each radar station is decorrelated, while the jamming signal is strongly correlated. The entire multi-station radar system is considered as an array. The energy of the jamming signal exists within a single-rank subspace, while the energy of the target echo signal is distributed throughout the entire signal space of the array. By orthogonally projecting the echo signal onto the noise subspace, the suppressive jamming signal can be effectively suppressed while preserving the target signal. This method does not depend on the time-frequency characteristics of the jamming signal and is applicable to different types of suppressive jamming signals. However, the method has the following drawbacks: when estimating the covariance matrix of the echo signal in low signal-to-noise ratio scenarios, the interference "energy leakage" will occur, resulting in inaccurate estimation of the noise subspace, which will cause the noise subspace and the interference subspace to no longer be orthogonal, and the orthogonal projection anti-interference performance will fail. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the prior art by providing a method for resisting main lobe suppression interference in multi-station radar systems based on oblique projection. This method aims to solve the problem of interference "energy leakage" causing anti-interference performance failure in low signal-to-noise ratio scenarios.

[0006] The idea behind this invention is to construct a generalized likelihood ratio function when estimating the signal subspace in low signal-to-noise ratio (SNR) scenarios. The generalized likelihood ratio function is a function of the signal subspace, representing the ratio of the energy of the signal projected onto the signal subspace to the energy of the signal projected onto its complement. By maximizing the likelihood ratio, the signal subspace corresponding to the maximum energy ratio is obtained, thus solving the problem of inaccurate subspace estimation caused by interference "energy leakage" in existing technologies. This invention utilizes the signal subspace to construct an oblique projection operator. In low SNR scenarios, interference signals are strongly correlated, and target echo signals are correlated. The interference signal exists in a single-rank interference subspace, while the target echo signal exists in a non-full-rank signal subspace. The two subspaces are no longer orthogonal. The oblique projection operator is used to obliquely project the radar system's received signal matrix along the interference subspace onto the signal subspace, solving the problem of orthogonal projection's anti-interference performance failure due to non-orthogonal subspaces in existing technologies.

[0007] The specific steps to achieve the objective of this invention are as follows:

[0008] Step 1: Point the receiving antenna beams of each radar station in the multi-station radar system toward the target and the area where the jammer is located;

[0009] Step 2: Align the time domain of the echo baseband signal vectors of each radar station in the multi-station radar system to obtain the received signal matrix of the radar system.

[0010] Step 3, generate the covariance matrix:

[0011] Multiplying the received signal matrix by its conjugate transpose yields the covariance matrix. The m-th row of the matrix represents the covariance between the signal received by the m-th radar station and the signals received by other radar stations. The n-th column represents the covariance between the signals received by other radar stations and the signals received by the n-th radar station. m and n represent integer values ​​randomly selected in the range [1, N], and m ≠ n. N represents the total number of radar stations in the radar system.

[0012] Step 4: Estimate the signal subspace according to the following formula.

[0013]

[0014] Where arg max represents the maximum value operation, and L(t) represents the ratio of the likelihood function under the condition that the target exists to the likelihood function under the condition that the target does not exist;

[0015] Step 5, Construct the oblique projection operator as follows:

[0016]

[0017] Wherein, orthogonal projection matrix I represents the identity matrix, and F represents the interference subspace spanned by the eigenvectors corresponding to the largest eigenvalue among all eigenvalues ​​obtained after eigenvalue decomposition of the covariance matrix.

[0018] Step 6: Multiply the received signal matrix of the radar system by the oblique projection operator to obtain the signal after interference suppression.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] First, this invention estimates the signal subspace by maximizing the generalized likelihood ratio function, overcoming the shortcomings of existing technologies where interference "energy leakage" leads to inaccurate subspace estimation in low signal-to-noise ratio scenarios. This gives the invention the advantage of being able to accurately estimate the signal subspace under low signal-to-noise ratio conditions.

[0021] Second, this invention utilizes the signal subspace to construct an oblique projection operator, which obliquely projects the radar echo along the interference subspace to the signal subspace to achieve interference suppression. This overcomes the shortcomings of existing technologies in low signal-to-noise ratio scenarios where the noise subspace and interference subspace are not orthogonal, leading to the failure of orthogonal projection anti-interference performance. This invention has the advantage of effectively suppressing suppression interference in low signal-to-noise ratio scenarios. Attached Figure Description

[0022] Figure 1 This is a flowchart of the present invention;

[0023] Figure 2 This is a simulation diagram of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the figures and embodiments.

[0025] Reference Figure 1 The specific implementation steps of the embodiments of the present invention will be described in further detail below.

[0026] In the embodiments of the present invention, the multi-station radar system consists of four radar stations. Only the first node radar operates in the transmit / receive state, while the other node radars operate in the receive state. There is a suppressor jammer in the radar detection area, and there are two real targets near the jammer.

[0027] Step 1: Point the receiving antenna beams of each radar station in the multi-station radar system toward the area where the target and jammer are located, so that the target signal and jamming signal enter from the main lobe of the beam.

[0028] Step 2: Align the baseband received signals of the four radar stations in the time domain with reference to the baseband signal of the first radar station, according to the following formula:

[0029]

[0030] in, Let r represent the time-domain aligned signal received by the i-th radar station. i (t) represents the unaligned signal received by the i-th radar station, i = 1, 2, ..., N, where N represents the total number of radar stations in the radar system, t represents the sampling time of the signal received by each radar station, t ∈ (0, PRT], PRT represents the pulse repetition period of the radar system, and τ 1i This represents the time delay difference that occurs when the correlation coefficient between the signal received by the i-th radar station and the signal received by the 1-th radar station reaches its maximum value.

[0031] The received signal matrix r(t) of the radar system is constructed using the received signals from each radar station after time-domain alignment:

[0032]

[0033] in,[·] T This indicates the transpose operation.

[0034] Step 3, generate the covariance matrix

[0035] Multiplying the received signal matrix r(t) by its conjugate transpose yields the covariance matrix. The m-th row of the matrix represents the covariance of the signal received by the m-th radar station with the signals received by other radar stations, and the n-th column represents the covariance of the signals received by other radar stations with the signals received by the n-th radar station. m and n represent integer values ​​randomly selected in the range [1,4], and m≠n.

[0036] Step 4: Estimate the signal subspace according to the following formula.

[0037]

[0038] Here, arg max represents the operation of taking the maximum value. This represents the ratio of the likelihood function given the existence of the objective to the likelihood function given the non-existence of the objective.

[0039] The formula for the ratio L(t) of the likelihood function under the condition that the target exists to the likelihood function under the condition that the target does not exist is as follows:

[0040]

[0041] Where, f1(r(t)|σ1 2 Let A(t) and B1(t) represent the likelihood functions given the existence of the objective. π represents the mathematical constant Pi, and exp(·) represents exponential operation with the natural constant e as the base. This represents the operation of taking the square of the L2 norm, σ1 2 Let f0(r(t)|σ2) represent the power of the noise signal under the condition that the target is present, D represent the signal subspace, A(t) represent the coordinates of the target echo signal in the signal subspace, F represent the interference subspace spanned by the eigenvector corresponding to the largest eigenvalue among all eigenvalues ​​obtained after eigenvalue decomposition of the covariance matrix, B1(t) represent the coordinates of the interference signal in the interference subspace under the condition that the target is present, and f0(r(t)|σ2 ... 2 B0(t) represents the likelihood function under the condition that the objective exists. σ0 2 B0(t) represents the power of the noise signal under the condition that the target does not exist, and B0(t) represents the coordinates of the interference signal in the interference subspace under the condition that the target does not exist.

[0042] Under the condition that the objective does not exist, L(t) is applied to σ0. 2 Find the partial derivative, set it to zero, and we get σ0. 2 Maximum likelihood estimate:

[0043]

[0044] in, σ0 2 The maximum likelihood estimate.

[0045] Similarly, given the existence of the target, σ1 2 Maximum likelihood estimation:

[0046]

[0047] in, σ1 2 The maximum likelihood estimate.

[0048] Will and Substituting into L(t), we get:

[0049]

[0050] Here, min represents the minimum operation.

[0051] Under the condition that the objective does not exist, the maximum likelihood estimation method is used to obtain the estimate of B0(t).

[0052]

[0053] in,(·) H This indicates the conjugate transpose operation, (·). -1 This indicates the inverse operation.

[0054] Similarly, assuming the objective exists, we can obtain an estimate of B1(t):

[0055]

[0056] in, This represents the estimated value of B1(t).

[0057] Will and Substituting into L(t), we get:

[0058]

[0059] Wherein, orthogonal projection matrix I represents the identity matrix.

[0060] All variables except A(t) are known. Taking the partial derivative of L(t) gives the maximum likelihood estimate of A(t):

[0061]

[0062] in, This represents the maximum likelihood estimate of A(t).

[0063] Will Substituting into L(t), we get:

[0064]

[0065] in, This represents the orthogonal projection matrix.

[0066] Step 5, Construct the oblique projection operator as follows:

[0067]

[0068] in, This represents the estimated signal subspace.

[0069] Step 6: Multiply the received signal matrix of the radar system by the oblique projection operator to obtain the interference-suppressed signal r. s (t).

[0070] The effects of this invention will be further illustrated below with simulation experiments:

[0071] 1. Simulation experimental conditions:

[0072] The software platform for the simulation experiment of this invention is: Windows 11 operating system and MATLAB 2020a.

[0073] In the simulation experiment of this invention, the multi-station radar system consists of four radar stations. Only one radar station operates in both transmitting and receiving mode, while the others operate in receiving mode. The positions of the four nodal radars are [0,0] km, [-5.1,1.02] km, [5.32,0.32] km, and [5.5,6] km. The actual positions of the targets are [0,80] km and [0,81] km, respectively, and the position of the jamming device is [0,79] km.

[0074] The simulation parameters for the anti-jamming capability of the multi-station radar system are shown in Table 1.

[0075] Table 1 Simulation parameters for anti-jamming of multi-station radar system

[0076]

[0077]

[0078] 2. Simulation content and result analysis:

[0079] The simulation experiment of this invention uses this invention and a prior art (a networked radar suppression method based on subspace projection) to suppress interference on the signals received by a multi-station radar system consisting of four radar stations in the simulation experiment of this invention. The resulting diagrams show the suppression effect of the main lobe suppression interference signal on the multi-station radar system. Figure 2 (a) and Figure 2 As shown in (b).

[0080] The prior art refers to a multi-station radar anti-suppression jamming method proposed by Xi'an University of Electronic Science and Technology in its patent application document "Networked Radar Suppression Suppression Main Lobe Jamming Method Based on Subspace Projection" (application number: 2014100198633; authorization announcement number: CN 103728595 B).

[0081] The following is combined Figure 2 The simulation results further illustrate the effects of the present invention.

[0082] Figure 2 (a) is a schematic diagram showing the results of interference suppression on the received signals of a multi-station radar system using the existing subspace projection-based networked radar suppression suppression main lobe interference method. Figure 2 (b) is a schematic diagram showing the results of interference suppression of received signals from a multi-station radar system using the method of the present invention. Figure 2 The horizontal coordinate of the middle circle represents the theoretical distance from the real target to the first radar station.

[0083] Figure 2 In (a), the horizontal axis represents distance in km, the vertical axis represents the signal amplitude after interference suppression, and the curve represents the signal after interference suppression obtained by simulation using existing technology.

[0084] from Figure 2 (a) It can be seen that in the low signal-to-noise ratio scenario, there is no target echo spike at the circle, indicating that the target echo is submerged by radio frequency noise interference and the algorithm's anti-interference performance fails.

[0085] Figure 2 (b) The horizontal axis represents the distance in km, the vertical axis represents the signal amplitude after interference suppression, and the curve represents the signal after interference suppression obtained by simulation using the present invention.

[0086] from Figure 2 (b) It can be seen that in low signal-to-noise ratio scenarios, there are target echo spikes at the circle, proving that the interference suppression effect of the present invention is better than that of the prior art and the anti-interference effect is more ideal.

Claims

1. A method for resisting main lobe suppression interference in a multi-station radar system based on oblique projection, characterized in that, The anti-interference method involves estimating the signal subspace and constructing an oblique projection operator using the signal subspace, and includes the following specific steps: Step 1: Point the receiving antenna beams of each radar station in the multi-station radar system toward the target and the area where the jammer is located; Step 2: Align the time domain of the echo baseband signal vectors of each radar station in the multi-station radar system to obtain the received signal matrix of the radar system. Step 3, generate the covariance matrix: Multiplying the received signal matrix by its conjugate transpose yields the covariance matrix. The m-th row of this matrix represents the covariance between the signal received by the m-th radar station and the signals received by other radar stations, and the n-th column represents the covariance between the signals received by other radar stations and the signal received by the n-th radar station. and They represent in A randomly selected integer value within the range, and N represents the total number of radar stations in the radar system; Step 4: Estimate the signal subspace according to the following formula. : ； in, This indicates the operation of retrieving the maximum value. This represents the ratio of the likelihood function given the existence of the objective to the likelihood function given the non-existence of the objective. This indicates the sampling time of the signals received by each radar station. ; The We obtain it from the following formula: ； in, This indicates that the received signals from each radar station, after time-domain alignment, constitute the received signal matrix of the radar system. This indicates the conjugate transpose operation. This indicates the inverse operation; Step 5, Construct the oblique projection operator as follows: ； Wherein, orthogonal projection matrix , Represents the identity matrix. This represents the interference subspace spanned by the eigenvectors corresponding to the largest eigenvalue among all eigenvalues ​​obtained after eigenvalue decomposition of the covariance matrix; Step 6: Multiply the received signal matrix of the radar system by the oblique projection operator to obtain the signal after interference suppression.

2. The multi-station radar anti-main lobe suppression jamming method based on oblique projection according to claim 1, characterized in that, The time-domain alignment of the echo baseband signal in step 2 is achieved by the following formula: ； in, Indicates the first The radar station receives the signal after time-domain alignment. Indicates the first The radar station received signals that were not time-domain aligned. , This indicates the total number of radar stations in the radar system. Indicates the pulse repetition period of the radar system. Indicates that the first The signal received by the radar station is the same as the first radar station. The time delay difference corresponding to the maximum value of the correlation coefficient of the signals received by each radar station.

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