Method for nonlinear frequency diversity multiple-input multiple-output radar to resist main lobe deception jamming

Abstract

The present application belongs to the field of radar anti-jamming, and discloses a method for resisting main lobe deceptive jamming of a nonlinear frequency diversity multiple-input multiple-output radar, comprising the following technical steps: S1: constructing a nonlinear frequency offset through an analog annealing algorithm, sending the constructed frequency offset into the FDA-MIMO radar, constructing a radar transmitting and receiving model, and obtaining a return signal; S2: under the condition that the distance and angle information of the known target is known, constructing a frequency compensation amount according to the designed frequency offset, and performing transmitting phase compensation on the return signal, so as to distinguish the target and the jamming signal; and S3: calculating the covariance matrix of the jamming and noise through the return signal, designing the optimal weight value of an adaptive filter, and filtering out the jamming. The purpose of the present application is to identify the target and the main lobe deceptive jamming signal and suppress the jamming under the condition that the antenna pattern is decoupled for the FDA-MIMO constructed through the analog annealing method.

Description

Technical Field

[0001] This invention belongs to the field of radar anti-jamming, specifically a method for nonlinear frequency diversity multiple-input multiple-output radar to resist main lobe deception jamming. Technical Background

[0002] Electronic countermeasures have always been crucial in the development of radar technology and systems. In recent years, with advancements in jamming techniques, the operational environment for radar target detection has become increasingly challenging. Active deception jamming, particularly repeater-based jamming, holds a significant position among all currently developed jamming methods. Deception jamming signals are generated by deception target generators. Integrating with a radar detector (DRFM), these generators monitor and store radar signals. They first analyze the recorded signal waveform, then modulate the signal's delay time and Doppler frequency before transmitting it to the radar receiver. This process generates various deception signals that precisely mimic the original signal. At the receiver, the radar simultaneously detects both real and false targets, located in front of and behind the real target. This leads to target tracking errors, consumes radar resources, degrades radar performance, and interferes with radar detection during both detection and tracking.

[0003] Frequency Diversity Arrays (FDAs) attracted considerable attention when first proposed by researchers. With the emergence of frequency deviations, their transmit-receive patterns incorporate corresponding three-dimensional factors of range and time, significantly expanding the control range. The distorted shape of the FDA pattern in space allows for effective study of its anti-jamming capabilities through its characteristics and analysis. With technological advancements, the integration of FDA and MIMO (Multiple-Input Multiple-Output) technologies has become a major development direction in the field of frequency guidance, aiming to enhance its effectiveness. FDA-MIMO offers greater advantages over traditional phased array radars in suppressing main lobe deception jamming.

[0004] The coupled angular range of the traditional linear frequency offset FDA-MIMO radar pattern offers a significant advantage in combating deception and interference. However, the characteristics of this antenna pattern also limit its anti-interference performance; the values ​​of pulse width, frequency offset, and element spacing all affect its anti-interference capabilities. Furthermore, as interference falls over a wider delay range, causing the target and interference signals to overlap at the echo pulse, traditional linear FDA-MIMO cannot effectively distinguish between the interference and target signals, and thus cannot suppress the interference. Summary of the Invention

[0005] The purpose of this invention is to address the phenomenon that linear frequency offset FDA-MIMO radar is affected by the radar's own parameters when resisting deception interference, and to solve the research on nonlinear frequency offset FDA-MIMO anti-main lobe deception interference algorithm based on simulated annealing method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for combating main lobe deception jamming in nonlinear frequency diversity multi-input multi-output radar includes the following steps:

[0008] S1: Construct a nonlinear frequency offset using the simulated annealing algorithm, input the constructed frequency offset into the FDA-MIMO radar, construct a radar transmit and receive model, and obtain the echo signal.

[0009] S2: Given the distance and angle information of the target, construct the frequency compensation amount according to the designed frequency offset, and perform phase compensation on the echo signal to distinguish the target from the interference signal.

[0010] S3: Calculate the covariance matrix of interference plus noise using the echo signal, design the optimal weights of the adaptive filter, and filter out the interference.

[0011] Preferably, in S1: For a nonlinear frequency offset diversity MIMO radar constructed based on simulated annealing, assuming the position of a target point in the far field is R0 and the angle between it and the radar receiving element is θ0, the radar target received echo signal transmitted by this target at the nth element can be expressed as:

[0012]

[0013] In the formula, Let represent the received signal of the nth receiving element at second t, where t represents time; β is the complex scattering coefficient of the point target; and M is the number of transmitting antenna elements. The signal amplitude function, where c represents the speed of light. This represents the delay time. The sum of the time delays between the signal transmitted by the m-th transmitting element and the signal received by the n-th receiving element after passing through the target point can be expressed as:

[0014]

[0015] The distance between adjacent array elements is d. In narrowband applications, ≈ ;but This is a delay time.

[0016] The echo signal can be represented by the steering vector as:

[0017]

[0018] In the formula, Represents the actual target signal; This represents a false target signal; n represents noise. Indicates the distance of the interference. Indicates the angle at which the interference occurs. This indicates the number of interferences; a(R0, θ0) ∈ M×1 is the target launch guidance vector. b(θ0)∈N×1 is the interference transmission steering vector, and b(θ0)∈N×1 is the target reception steering vector. ∈N×1 is the interference reception steering vector. Represents the complex coefficients of the target signal. Represents the complex coefficients of the interference signal. It represents the Kronecker product.

[0019] Preferably, in S2: a specific frequency offset designed using simulated annealing represents the re-emission steering vector of FDA-MIMO, representing the transmitted signal:

[0020] The designed frequency offset is:

[0021]

[0022] M is the number of elements in the transmitting antenna array; This represents the frequency offset of the Mth array element. The obtained... The value is fed into the FDA MIMO radar, and the re-emission space steering vector is represented as:

[0023]

[0024] This represents the phase component of the launch space steering vector. This represents the angular frequency component of the launch space steering vector. For Hamada product; Indicates distance, For carrier frequency, For the spacing between array elements, The angle between the launching element and the target. This represents the frequency offset of the m-th array element, where m = 1, 2, ..., M.

[0025] Therefore, the steering vector of the nonlinear frequency offset FDA MIMO radar is:

[0026]

[0027] in, Indicates distance, This represents the frequency offset of the m-th array element, where m = 1, 2, ..., M. Indicates angle.

[0028] In main lobe deception interference, range deception interference is the primary consideration, and the range phase compensation vector can be constructed as follows:

[0029]

[0030] in For range phase compensation vector, Indicates the region without distance ambiguity. This represents the transpose of a matrix.

[0031] The observed true target R = R0 is the distance from the target to the radar. Compensation is applied to the echo signal, i.e., by substituting... The total received echoes are represented as:

[0032]

[0033] in Indicates the number of distance-free ambiguity regions. The signal echoes from the real and decoy targets. , It is an N×1 dimensional all-one vector; , Represents the scattering coefficients of the undesired signal. It is the product of the signal scattering coefficient measured in the non-observation region and its corresponding propagation gain. This indicates the signal distance measured in the non-observation area. The frequency of the radar array element's transmission center; This represents the phase component of the launch space steering vector. This represents the angular frequency component of the launch space steering vector. For Hamada product, Indicates the Kronecker product; This represents the receiving steering vector; since only main lobe spoofing interference is considered, the receiving angle steering vector varies when k takes different values. The same This represents the distance from the signal in the k-th ambiguous region to the radar; after compensation, the distances of different signals... Let the m-th element be represented as follows:

[0034]

[0035] in, To achieve the maximum unambiguous distance, the pulse repetition frequency is: , For the spacing between array elements, From the perspective of the target, This represents the principal value distance.

[0036] Preferably, in S3: to filter out interference, the covariance matrix of the interference plus noise needs to be calculated, and the optimal weights of the adaptive filter need to be designed. The specific steps are as follows:

[0037] A distance-angle adaptive matched filter is designed based on the covariance matrix of the interference plus noise:

[0038]

[0039] In this formula, This represents a covariance matrix that includes interference and noise; The optimal fitness weights are calculated using Lagrange multiplication. yes The transpose of the matrix;

[0040] The compensated data is passed through this adaptive distance and angle matched filter, and its output signal The results are as follows:

[0041]

[0042] Where x is the echo signal steering vector.

[0043] The beneficial effects of the nonlinear frequency diversity multiple-input multiple-output (FDA-MIMO) radar anti-main lobe deception jamming method constructed based on simulated annealing in this invention are as follows:

[0044] The research on the nonlinear frequency offset FDA-MIMO anti-spoofing interference algorithm based on simulated annealing method makes the signal in the non-detection area discretely distributed after processing instead of being in a high-power state. This makes the anti-spoofing interference unaffected by the parameters of the FDA-MIMO radar itself, and solves the problem of linear FDA-MIMO anti-jamming algorithm failure caused by interference and target aliasing in the large-range ambiguity area. Attached Figure Description

[0045] Figure 1 The following is a flowchart illustrating the specific implementation principle of this invention.

[0046] Figure 2 This is a geometric configuration diagram of the nonlinear frequency offset FDA-MIMO radar of the present invention.

[0047] Figure 3 This is the adaptive receiving pattern of the nonlinear frequency offset FDA-MIMO radar of the present invention.

[0048] Figure 4 The signal is received at the target angle before processing in this invention.

[0049] Figure 5 This is the result of adaptive filtering for the nonlinear frequency offset FDA-MIMO radar of this invention.

[0050] Figure 6 This is the adaptive filtering result for the linear frequency offset FDA-MIMO radar of this invention.

[0051] Figure 7 This invention relates to the nonlinear increasing frequency FDA-MIMO SINR and SNR relationship.

[0052] Figure 8 This is the adaptive filtering result for the nonlinear frequency-increasing FDA-MIMO radar of this invention. Detailed Implementation

[0053] Specific embodiments of the present invention are described in detail with reference to the accompanying drawings to enable those skilled in the art to understand these disclosed solutions. It should be noted that the present invention is not limited to the specific configurations described below. Any obvious modifications or adaptations made by those skilled in the art within the scope and spirit of the appended claims are within the protection scope of the present invention. All technical solutions based on the concepts of the present invention are within the protection scope of the present invention.

[0054] Combination Figure 1 This embodiment describes a method for resisting main lobe deception jamming in nonlinear frequency diversity multiple-input multiple-output radar, comprising:

[0055] Step 1: Construct a nonlinear frequency offset using the simulated annealing algorithm, input the constructed frequency offset into the FDA-MIMO radar, construct the radar transmit and receive model, and obtain the echo signal.

[0056] Step one specifically includes the following:

[0057] Figure 2 For the signal model established by the nonlinear frequency offset diversity MIMO radar constructed based on simulated annealing, assuming the position of a target point in the far field is R0 and the angle between it and the radar receiving element is θ0, the radar target received echo signal at the nth element after passing through the target can be expressed as:

[0058]

[0059] In the formula, Let represent the received signal of the nth receiving element at second t, where t represents time; β is the complex scattering coefficient of the point target; and M is the number of transmitting antenna elements. The signal amplitude function, where c represents the speed of light. This represents the delay time. Let the sum of the time delays between the signal transmitted by the m-th transmitting element and the signal received by the n-th receiving element after passing through the target point be expressed as:

[0060]

[0061] The distance between adjacent array elements is d. In narrowband applications, ≈ ;but This is a delay time.

[0062] The echo signal can be represented by the steering vector as:

[0063]

[0064] In the formula, Represents the actual target signal; This represents a false target signal; n represents noise. Indicates the distance of the interference. Indicates the angle at which the interference occurs. This indicates the number of interferences; a(R0, θ0) ∈ M×1 is the target launch guidance vector. b(θ0)∈N×1 is the interference transmission steering vector, and b(θ0)∈N×1 is the target reception steering vector. ∈N×1 is the interference reception steering vector. Represents the complex coefficients of the target signal. Represents the complex coefficients of the interference signal. It represents the Kronecker product.

[0065] Step 2: Given the target's distance and angle information, construct a frequency compensation amount based on the designed frequency offset, and perform phase compensation on the echo signal to distinguish the target from the interference signal.

[0066] Step two specifically includes the following:

[0067] like Figure 2 Assume the designed frequency offset is:

[0068]

[0069] M is the number of elements in the transmitting antenna array; This represents the frequency offset of the Mth array element. The obtained... The value is fed into the FDA MIMO radar, and the re-emission space steering vector is represented as:

[0070]

[0071] This represents the phase component of the launch space steering vector. This represents the angular frequency component of the launch space steering vector. For Hamada product; Indicates distance, For carrier frequency, For the spacing between array elements, The angle between the launching element and the target. This represents the frequency offset of the m-th array element, where m = 1, 2, ..., M.

[0072] Therefore, the steering vector of the nonlinear frequency offset FDA MIMO radar is:

[0073]

[0074] in, Indicates distance, This represents the frequency offset of the m-th array element, where m = 1, 2, ..., M. Indicates angle.

[0075] When M=8, the designed nonlinear frequency offset FDA-MIMO radar adaptive receiver pattern is as follows: Figure 3 As shown. The target distance is 30km and the angle is 0°. Other interferences are (0°, 20km), (0°, 55km), (-10°, 30km), and (10°, 30km). The simulation results show that the radiation pattern after the frequency offset design is focused only at the target, while the other interferences are discrete and non-energy focused.

[0076] In main lobe deception interference, range deception interference is the primary consideration, and the range phase compensation vector can be constructed as follows:

[0077]

[0078] in For range phase compensation vector, Indicates the region without distance ambiguity. This represents the transpose of a matrix.

[0079] The observed true target R = R0 is the distance from the target to the radar. Compensation is applied to the echo signal, i.e., by substituting... The total received echoes are represented as:

[0080]

[0081] in Indicates the number of distance-free ambiguity regions. The signal echoes from the real and decoy targets. , It is an N×1 dimensional all-one vector; , Represents the scattering coefficients of the undesired signal. It is the product of the signal scattering coefficient measured in the non-observation region and its corresponding propagation gain. This indicates the signal distance measured in the non-observation area. The frequency of the radar array element's transmission center; This represents the phase component of the launch space steering vector. This represents the angular frequency component of the launch space steering vector. For Hamada product, Indicates the Kronecker product; This represents the receiving steering vector; since only main lobe spoofing interference is considered, the receiving angle steering vector varies when k takes different values. The same This represents the distance from the signal in the k-th ambiguous region to the radar; after compensation, the distances of different signals... The following is an example (using the m-th element):

[0082]

[0083] in, To achieve the maximum unambiguous distance, the pulse repetition frequency is: , For the spacing between array elements, From the perspective of the target, This represents the principal value distance.

[0084] Step 3: Calculate the covariance matrix of interference plus noise, design the optimal weights for the adaptive filter, and filter out the interference. The specific steps are as follows:

[0085] Step three specifically includes the following:

[0086] A distance-angle adaptive matched filter is designed based on the covariance matrix of the interference plus noise:

[0087]

[0088] In this formula, This represents a covariance matrix that includes interference and noise; The optimal fitness weights are calculated using Lagrange multiplication. yes The transpose of the matrix;

[0089] The compensated data is passed through this adaptive distance and angle matched filter, and its output signal The results are as follows:

[0090]

[0091] Where x is the echo signal steering vector.

[0092] Select one of the radar antenna array elements to receive the signal, such as Figure 4 It can be seen that the target's distance threshold is 100 at this time, and the other two peaks are the main lobe deception interference (distance deception interference). Figure 5 The adaptive filtering results for the nonlinear frequency-increasing FDA-MIMO radar show that both main lobe deception interferences have been filtered out, leaving only the target signal at the 100 range gate. Figure 6 The result is the adaptive filtering result of a traditional linear FDA-MIMO radar. In this case, because the interference and the target are too far apart, the interference echo falls near the target echo pulse, which makes it impossible to effectively identify and filter out the interference. This shows the anti-deception interference advantage of the nonlinear frequency offset FDA-MIMO radar. Figure 7 The relationship between nonlinear increasing FDA-MIMO SINR and SNR. Figure 8 The relationship between SINR and SNR of nonlinear frequency-increased FDA-MIMO radar and ordinary MIMO radar indirectly illustrates the effectiveness and advantages of the nonlinear frequency diversity multi-input multi-output radar constructed based on simulated annealing method against main lobe deception under complex interference.

[0093] In summary, the beneficial effects of this invention are as follows: Based on the simulated annealing method, a nonlinear frequency diversity multiple-input multiple-output radar, with known target range and angle information, performs transmit phase compensation on the echo signal according to the designed frequency offset, thereby distinguishing between the target and interference signals. The covariance matrix of interference plus noise is calculated from the echo signal, and the optimal weights of an adaptive filter are designed to filter out interference falling within a wider delay range. Compared to traditional linear frequency offset FDA-MIMO, its anti-main lobe spoofing interference performance is improved, overcoming the transmit pattern coupling characteristics of linear FDA-MIMO and handling more complex interference scenarios.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for resisting main lobe deception jamming in nonlinear frequency diversity multi-input multi-output radar, characterized in that, Includes the following steps: S1: Construct a nonlinear frequency offset using the simulated annealing algorithm, input the constructed frequency offset into the FDA-MIMO radar, construct a radar transmit and receive model, and obtain the echo signal. S2: Given the distance and angle information of the target, construct the frequency compensation amount according to the designed frequency offset, and perform phase compensation on the echo signal to distinguish the target from the interference signal. S3: Calculate the covariance matrix of interference plus noise using the echo signal, design the optimal weights of the adaptive filter, and filter out the interference.

2. The method for resisting main lobe deception interference in nonlinear frequency diversity multiple-input multiple-output radar according to claim 1, characterized in that: Based on the nonlinear frequency offset diversity MIMO radar constructed by the simulated annealing method obtained in step S1, let the position of a target point in the far field be R0, and the angle of the radar receiving element be θ0. Then, the radar target received echo signal transmitted by this target at the nth element is represented as: ； ≈ ； In the formula, This represents the received signal of the nth receiving element at second t, where t represents time; β is the complex scattering coefficient of the point target; M is the number of elements in the transmitting antenna array. The signal amplitude function, where c represents the speed of light. Represents the delay time; Let the sum of the time delays between the signal transmitted by the m-th transmitting element and the signal received by the n-th receiving element after passing through the target point be expressed as: ； Where the distance between adjacent array elements is d; in narrow band... ≈ ;but For delay time; Echo signal using steering vector It can be represented as: ； In the formula, Represents the actual target signal; This represents a false target signal; n represents noise. Indicates the distance of the interference. Indicates the angle at which the interference occurs. This indicates the number of interferences; a(R0, θ0) ∈ M×1 is the target launch guidance vector. b(θ0)∈N×1 is the interference transmission steering vector, and b(θ0)∈N×1 is the target reception steering vector. ∈N×1 is the interference reception steering vector. Represents the complex coefficients of the target signal. Represents the complex coefficients of the interference signal. It represents the Kronecker product.

3. The method for resisting main lobe deception interference in nonlinear frequency diversity multiple-input multiple-output radar according to claim 1, characterized in that: S2 is designed with a specific frequency offset and compensation for the echo signal: Design frequency offset for: ； M is the number of elements in the transmitting antenna array; This represents the frequency offset of the Mth array element; the obtained... The value is fed into the FDA MIMO radar, and the re-emission space steering vector is represented as: ； This represents the phase component of the launch space steering vector. This represents the angular frequency component of the launch space steering vector. For Hamada product; Indicates distance, For carrier frequency, For the spacing between array elements, The angle between the launching element and the target. This represents the frequency offset of the m-th array element, where m = 1, 2, ..., M; The steering vector of the nonlinear frequency offset FDA MIMO radar is: ； In main lobe deception interference, range deception interference is the primary consideration, and the range phase compensation vector is constructed as follows: ； in For range phase compensation vector, Indicates the region without distance ambiguity. Represents the transpose of a matrix; The observed true target R = R0 is the distance from the target to the radar. Compensation is applied to the echo signal, i.e., by substituting... The total received echoes are represented as: ； in Indicates the number of distance-free ambiguity regions. The signal echoes from the real and decoy targets. , It is an N×1 dimensional all-one vector; , Represents the scattering coefficients of the undesired signal. It is the product of the signal scattering coefficient measured in the non-observation region and its corresponding propagation gain. This indicates the signal distance measured in the non-observation area. The frequency of the radar array element's transmission center; This represents the phase component of the launch space steering vector. This represents the angular frequency component of the launch space steering vector. For Hamada product, Indicates the Kronecker product; This represents the receiving steering vector; since only main lobe spoofing interference is considered, the receiving angle steering vector varies when k takes different values. The same This represents the distance from the signal in the k-th ambiguous region to the radar; after compensation, the distances of different signals... Let the m-th element be represented as follows: ； in, To achieve the maximum unambiguous distance, the pulse repetition frequency is: , For the spacing between array elements, From the perspective of the target, This represents the principal value distance.

4. The method for resisting main lobe deception interference in nonlinear frequency diversity multi-input multi-output radar according to claim 1, characterized in that, In S3, the covariance matrix of interference plus noise is calculated, and the optimal weights of the adaptive filter are designed to filter out the interference. The specific method is as follows: A distance-angle adaptive matched filter is designed based on the covariance matrix of the interference plus noise: ； This represents a covariance matrix that includes interference and noise; The optimal fitness weights are calculated using Lagrange multiplication. yes The transpose of the matrix; The compensated data is passed through this adaptive distance and angle matched filter, and its output signal The results are as follows: ； Where x is the echo signal steering vector.

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