A distributed radar jamming suppression method based on joint generalized inner product

Abstract

The application provides a distributed radar interference suppression method based on a joint generalized inner product, which can simultaneously realize joint suppression of main lobe interference and clutter under the condition that there are unit radar position errors in a distributed radar system and the phase errors between channels are large. The application utilizes the high angle resolution of the distributed radar to realize effective suppression of the main lobe interference, and then utilizes the flexible advantage of the distributed radar aperture to design a multi-dimensional generalized inner product parameter to realize target detection in a clutter environment. The algorithm can simultaneously realize joint suppression of main lobe interference and clutter under the condition that there are unit radar position errors in a distributed radar system and the phase errors between channels are large, thereby improving the target detection performance of the distributed radar system under non-ideal conditions.

Description

Technical Field

[0001] This invention relates to the field of distributed radar anti-jamming technology, and specifically to a distributed radar jamming suppression method based on joint generalized inner product. Background Technology

[0002] Target detection in scenarios with strong clutter and interference has become a key challenge in the modern radar field. When radar detects low-altitude targets, clutter severely affects target detection, and in reality, clutter often fluctuates, affecting the effectiveness of multi-pulse processing. With the development of electronic countermeasures technology, radar faces a serious threat of active interference. When interference enters from the antenna sidelobe, conventional sidelobe cancellers (SLCs) can achieve good results. However, when interference enters from the antenna main lobe, it is modulated by the radar antenna gain, resulting in greater interference energy and a more severe interference effect. If traditional adaptive array processing is used, it will cause distortion of the main lobe of the radar receiving pattern.

[0003] Existing monostatic array radar main lobe interference suppression mainly relies on projection matrix-based methods. However, conventional monostatic radar main lobe interference suppression methods can only suppress near-main lobe interference, and also cause significant loss of target signal energy. When the target and the jammer are closer, the monostatic radar cannot overcome the limitations of its aperture to distinguish between the jammer and the target in space. At this time, the monostatic radar will lose its target detection capability.

[0004] The large aperture of distributed radar gives it extremely high angular resolution, enabling it to distinguish between interference and targets within the main lobe by angle. However, the extremely long baseline of distributed radar also makes precise localization of individual radar cells very difficult. Furthermore, the different transmission delays and initial phases of each radar cell lead to delay and phase differences between channels. While delay differences can usually be accurately estimated, phase differences are difficult to estimate precisely. The presence of phase differences severely affects the coherence of the distributed radar system. Literature has also analyzed the relationship between signal-to-noise ratio gain loss and phase synchronization error in distributed radar systems. Phase errors cause steering vector mismatch, severely degrading the performance of conventional interference suppression algorithms.

[0005] To achieve main lobe interference suppression and target detection under arbitrary phase errors, the Generalized Inner Product (GIP) algorithm can be used. Since the GIP algorithm does not require a steering vector, it is insensitive to position errors, and its inner product process can suppress phase errors. However, in real-world complex environments, various types of clutter (such as ground clutter, sea clutter, and weather clutter) are unavoidable, and conventional GIP algorithms cannot suppress clutter in the presence of main lobe interference. Summary of the Invention

[0006] In view of this, the present invention proposes a distributed radar interference suppression method based on joint generalized inner product, which can simultaneously suppress main lobe interference and clutter in a distributed radar system with unit radar position errors and large phase errors between channels.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] The present invention discloses a distributed radar interference suppression method based on joint generalized inner product. First, the angular resolution of the distributed radar is used to suppress the main lobe interference. Then, a multidimensional generalized inner product parameter is designed using the distributed radar aperture. Based on the multidimensional generalized inner product parameter, target detection is achieved in a clutter environment.

[0009] The specific implementation method is as follows:

[0010] Step 1: Obtain the initial sample s based on the full array echo and the main radar array echo respectively. A and s M ;

[0011] Step two, based on s A and s M Calculate the covariance matrix R respectively A and R M :

[0012]

[0013] Where L is the sample s A and s M The number of snapshots in the data, according to the generalized inner product formula, is... and Summing the columns yields G. A and G M , where ⊙ is the Hadamard product; the superscript H denotes the conjugate transpose of the matrix;

[0014] Step 3, from G A and G M Remove points that exceed their respective set thresholds and update sample s. A and s M Repeat step two until the sample no longer changes, then proceed to the next step.

[0015] Step 4, according to Calculate the joint generalized inner product result and select the target snapshot with a threshold of 1.5M.

[0016] In step 3, for G A The threshold is set to M, where M is the total number of channels in the distributed full array.

[0017] In step 3, for GM The set threshold is M′, where M′ is the number of main radar channels.

[0018] In step 3, for G A The set threshold is 1.5M, where M is the total number of channels in the distributed full array; for G M The set threshold is 1.5M′, where M′ is the number of main radar channels.

[0019] Beneficial effects:

[0020] 1. This invention utilizes the extremely high angular resolution of distributed radar to effectively suppress main lobe interference. Furthermore, leveraging the flexible aperture of distributed radar, a multi-dimensional generalized inner product parameter is designed to achieve target detection in clutter environments. This algorithm can simultaneously suppress both main lobe interference and clutter in distributed radar systems with unit radar position errors and significant phase errors between channels, thus improving the target detection performance of distributed radar systems under non-ideal conditions.

[0021] 2. This invention uses an adaptive weighting method to process the GIP results of two sets of apertures. After weighting, clutter is successfully suppressed. To ensure the clutter suppression effect, an iterative method is adopted for weight optimization. A threshold is set according to the number of channels to remove some snapshots from the samples. The weights are recalculated until the iteration termination condition is reached. Multiple iterations can effectively avoid target energy loss.

[0022] 3. The GIP value at the target point is related to the dot product of the steering vectors of the interference and the target, and the signal power, and is always greater than or equal to M. In other words, if the target has a certain signal strength, and the target and interference are angularly separable from the current array, a high GIP value can be obtained in the target snapshot. Therefore, this invention uses M as a threshold (a value slightly larger than M can be used to ensure robustness) to detect the snapshot where the target is located without needing to know the specific angle of the target in advance. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of a distributed radar system.

[0024] Figure 2 This is a schematic diagram of the interference suppression front echo of the present invention.

[0025] Figure 3 This is a flowchart of the combined generalized inner product algorithm of this invention.

[0026] Figure 4 This is a schematic diagram comparing the GIP results of the main full array of the present invention.

[0027] Figure 5 This is a schematic diagram of the JGIP processing results of the present invention. Detailed Implementation

[0028] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] This invention provides a distributed radar jamming suppression method based on Joint Generalized Inner Product (JGIP). It utilizes JGIP to jointly suppress main lobe interference and sidelobe clutter. The proposed JGIP algorithm first uses the extremely high angular resolution of distributed radar to suppress main lobe interference, and then leverages the flexible aperture advantage of distributed radar to design multi-dimensional generalized inner product parameters for target detection in cluttered environments. Distributed radar, such as... Figure 1 As shown. Assuming the scenario contains one main lobe suppression interference, one target, and C clutter, the array-received echo will be:

[0030]

[0031] In the formula, a(θ0) is the target signal steering vector, s0(t) is the target signal complex envelope, and a(θ j ) represents the interference signal steering vector, s j (t) represents the complex envelope of the interference signal, a(θ) i ) represents the i-th clutter steering vector, s i Let (t) be the complex envelope of the i-th clutter, and n(t) be the Gaussian white noise matrix. Assume that the interference is a noise-suppressed interference that spreads throughout the entire range gate, while clutter and the target only exist in a portion of the range cells.

[0032] In practical processing, it is difficult to obtain the theoretical signal covariance matrix. Usually, the sampled data covariance matrix is ​​used for estimation. Assuming that the complex envelopes of each source are uncorrelated, the data covariance matrix can be further obtained according to equation (1):

[0033]

[0034] In the formula The total power of the target signal. For the total power of interference, Let be the total power of the i-th clutter. Let L be the total noise power, and L be the number of snapshots used to estimate the covariance matrix.

[0035] Since interference and noise exist throughout the entire distance gate, their total power can be replaced by the average power and simplified to the following formula:

[0036]

[0037] Due to the extremely high angular resolution of distributed radar, we can assume a(θ0), a(θ) j ) and a(θ i If the variables are uncorrelated, the inverse of the covariance matrix can be obtained as follows:

[0038]

[0039] In the formula, M = a(θ) H a(θ) is the number of channels.

[0040] According to equation (4), it can be found that due to the scaling effect of the number of snapshots L, the coefficients of the second and third terms in the equation will be smaller, so that R -1 The inability to completely whiten both signal and clutter components results in significant peaks in the snapshots containing both the target and clutter after GIP processing. Based on this characteristic, multiple iterations can be performed to filter out the snapshots containing both the target and clutter, continuously updating the covariance matrix to ultimately approximate the interference + noise covariance matrix.

[0041]

[0042] According to the Sherman-Morrison formula:

[0043]

[0044]

[0045] In the formula This is the noise-to-interference ratio.

[0046] To analyze the specific values ​​of each snapshot after processing by the GIP algorithm, we will discuss two cases below.

[0047] 1. Snapshots with only interference

[0048] The data is processed using GIP (Guided Inspiration Processing). For a snapshot containing only interference, the GIP value is:

[0049]

[0050] Due to the noise-to-interference ratio ε j Generally larger, in the formula... Equation (7) can be further simplified to:

[0051]

[0052] In the formula n(t) j ) is an M×1 dimensional noise column vector.

[0053] Due to n(t) j ) and a(θ j If they are linearly independent, then Because n(t) j Each element satisfies The Gaussian distribution, from a statistical perspective, yields... but:

[0054]

[0055] From the above derivation, we can conclude that the GIP value of a data segment with only interference is approximately equal to M.

[0056] 2. Snapshots with a target (or clutter) present.

[0057] From the perspective of a single snapshot, clutter can also be regarded as a target (or a collection of multiple targets) with spatial characteristics (which can be represented by a steering vector), so the subsequent derivation applies to both the target and clutter.

[0058] For snapshots that simultaneously contain both a target and interference, the GIP value is:

[0059]

[0060] s0(t0) H s0(t0) represents the total energy of the target signal in this snapshot, which can be expressed as... By substitution, equation (10) is further simplified to:

[0061]

[0062] As can be seen from the above formula, the GIP value at the target point is related to the inner product of the steering vectors of the interference and the signal power, and is always greater than or equal to M. In other words, if the target has a certain signal strength, and the target and interference are angularly separable from the current array, a high GIP value can be obtained in the target snapshot. With M as the threshold (a value slightly larger than M can be used to ensure robustness), the snapshot where the target is located can be detected without knowing the specific angle of the target in advance.

[0063] 3. Distributed radar with flexible aperture

[0064] Based on the derivation in the previous section, the key to whether the GIP algorithm can distinguish targets (clutter) lies in whether it can be angularly differentiated from interference. Distributed radar, with multiple nodes along a long baseline, can flexibly combine apertures to effectively utilize this characteristic of the GIP algorithm. For ease of analysis, the following analysis will primarily consider two sets of apertures: the distributed radar's full array and the main radar's own array.

[0065] For distributed radar, its large array aperture brings extremely high angular resolution, making the target and main lobe interference separable. That is, even under main lobe interference, the target's location in the snapshot can be detected through the GIP value. Assuming the total number of channels in the distributed full array is M, and the auxiliary radar gain is the same as the main radar subarray gain, then |a(θ) j ) H a(θ0)| 2 <<M 2In relation to equation (11), considering a more ideal scenario, we can assume:

[0066]

[0067] A set of GIP values ​​can also be obtained using only the main radar array. The GIP value at the target is:

[0068]

[0069] In the formula, M′ is the number of main radar channels, a′(θ0) is the target guidance vector of the main radar, and a′(θ) is the target guidance vector of the main radar. j ) is the main radar interference steering vector.

[0070] Since the main radar does not have the ability to distinguish between interference and targets in terms of angle at this time, it can be approximated as... Considering a more ideal scenario, we can assume G′ t ≈M′.

[0071] As for clutter located on the sidelobe of the main radar, both the main radar itself and the entire distributed radar system can distinguish it from the interference angularly. Therefore, the values ​​of the two sets of GIP results for the sidelobe clutter are as follows:

[0072]

[0073] Based on the above derivation, the typical values ​​for each snapshot under the two apertures can be obtained:

[0074] Table 1 Typical values ​​of each signal component at different aperture sizes

[0075]

[0076] The table above shows that the target amplitude in the main radar GIP results is basically consistent with the suppressed interference. Both aperture GIP results exhibit high amplitudes at the sidelobe clutter, satisfying a certain proportion. Therefore, weighted cancellation of the two sets of results can suppress the sidelobe clutter while preserving the target amplitude within the main lobe. However, in actual processing, since an ideal interference and noise covariance matrix cannot be obtained, the amplitudes of the two sets of results at the clutter may not completely satisfy the above formula. To ensure robustness, an adaptive weighting method should be used to process the two sets of results. This leads to an optimization problem:

[0077]

[0078] The optimal weight vector can be obtained by solving using the Lagrange method:

[0079]

[0080] After weighting using the above formula, the clutter was successfully suppressed. A schematic diagram of the echo before interference suppression is shown below. Figure 2 As shown. To ensure clutter suppression, weight optimization can also be performed iteratively. A threshold is set based on the number of channels to remove some snapshots from the samples, and the weights w are recalculated until the iteration termination condition is met. Multiple iterations can effectively avoid target energy loss.

[0081] The specific process of this invention is illustrated by a simulation experiment using a one-dimensional distributed radar system. The specific parameters of the one-dimensional distributed radar system used are shown in Table 2.

[0082] Table 2 Parameters of Distributed Radar System

[0083]

[0084] The specific locations of each radar after the distributed radar is designed with a sparse array configuration are shown in Table 3.

[0085] Table 3 Radar Locations

[0086]

[0087]

[0088] The source parameters for the simulation scenario are shown in Table 4.

[0089] Table 4 Source Parameters

[0090]

[0091] The flowchart of the joint generalized inner product algorithm of this invention is as follows: Figure 3 As shown, the specific steps are as follows:

[0092] Step 1: Initialize the sample, specifically based on the full array echo S. A (21 channels) and main radar array echo S M (16 channels) respectively obtain the initial sample s A and s M .

[0093] Step 2: Calculate the generalized inner product, as follows:

[0094] First, according to s A and s M Calculate the covariance matrix R respectively A and R M :

[0095]

[0096] Where L is the sample s A and s M The number of snapshots in the data, according to the generalized inner product formula, is... and Summing the columns yields G. A and G M (⊙ represents the Hadamarda accumulation.)

[0097] Step 3: Using 1.5M and 1.5M′ as thresholds respectively, start from G A and G M Remove points exceeding the threshold and update sample s. A and s M Repeat step two. This step makes the covariance matrix closer to the covariance matrix containing only interference and noise. When the sample no longer changes, proceed to the next step. The output result is as follows. Figure 5 As shown.

[0098] Step 4, according to Calculate the joint generalized inner product result, and select target snapshots with a threshold of 1.5M. The processing result is as follows: Figure 4 As shown.

[0099] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed radar jamming suppression method based on joint generalized inner product, characterized in that, First, the angular resolution of the distributed radar is used to suppress main lobe interference. Then, a multidimensional generalized inner product parameter is designed using the distributed radar aperture. Based on the multidimensional generalized inner product parameter, target detection in clutter environments is achieved. The specific implementation method is as follows: Step 1: Obtain initial samples from the full array echo and the main radar array echo respectively. and ; Step two, according to and Calculate the covariance matrix respectively and : in For the sample and The number of snapshots in the data, according to the generalized inner product formula, is... and Summing the columns yields... and ,in For Hadama; superscript H The superscript "-" indicates the conjugate transpose of a matrix; the superscript "-" indicates the inverse of a matrix. Step 3, from and Remove points that exceed their respective thresholds and update the sample. and Repeat step two until the sample no longer changes, then proceed to the next step. Step 4, according to Calculate the joint generalized inner product result to set a threshold for selecting target snapshots.

2. The method as described in claim 1, characterized in that, In step three, for The set threshold is , This represents the total number of channels in the distributed full array.

3. The method as described in claim 1, characterized in that, In step three, for The set threshold is , The number of main radar channels.

4. The method as described in claim 1, characterized in that, In step three, for The set threshold is 1.

5. , For the total number of channels in the distributed full array; The set threshold is 1.

5. , The number of main radar channels.

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