A SAR anti-interference method based on multi-parameter optimization of RD imaging

Abstract

The present application discloses a SAR anti-interference method based on multi-parameter optimization of RD imaging. The method obtains phase parameters and azimuth filter coefficients through an optimization algorithm, uses the phase parameters to modulate a linear frequency modulation signal as the transmission signal of a synthetic aperture radar, and uses the filter coefficients to modulate the azimuth matched filter function in the RD imaging algorithm. The cost function used to optimize the waveform phase and filter coefficients consists of sidelobe energy integral, interference signal energy integral, integrated sidelobe ratio constraint, non-matched filter peak gain loss constraint, and constant modulus constraint, so that the SAR waveform agility has anti-deception interference performance; the cost function is maximized by using a second-order Taylor expansion and solved using the alternating direction multiplier method, so that the algorithm has strong robustness. The SAR anti-interference method disclosed in the present application can be applied to a SAR imaging system based on an RD imaging algorithm and has the effect of resisting deception interference.

Description

Technical Field

[0001] The application relates to the field of SAR radar imaging technology, and specifically to a SAR anti-interference method based on multi-parameter optimization of RD imaging. Background Art

[0002] As an all-weather information sensing device, radar has become a core piece of equipment for reconnaissance, surveillance, target identification, and coordinated operations. Precisely because of its importance on the battlefield, radar has been subject to a variety of electronic countermeasures (ECM) since its inception. With the maturity of Digital Radio Frequency Memory (DRFM) technology, ECM seeks to develop radar active deception jammers with flexible and diverse jamming methods. Radar active deception jammers can accurately simulate radar echo signals and enter the radar system through the radar antenna's main lobe at low power, thereby posing a significant threat to radar reconnaissance. Furthermore, with the continuous development of ECM technology, more radar active deception jammer patterns with specific jamming effects have been proposed, posing new challenges to modern radars' ability to perceive and resist jamming environments.

[0003] In order to counter deceptive jamming, various electronic countermeasures (ECCM) methods have been proposed. Existing countermeasures can be divided into two types: methods based on reception processing and methods based on transmission modulation.

[0004] Receive processing-based methods attempt to filter or identify deceptive jammers based solely on the received signal, such as the multi-channel technology in the document "Anti-jamming techniques for multichannel SAR imaging", which works well in mitigating deceptive jamming but has high deployment and maintenance costs. The dynamic synthetic aperture technology in the document "Single Channel SAR DeceptionJamming Suppression via Dynamic Aperture Processing" provides multiple observations of the time-frequency distribution of deceptive jammers and real SAR echoes through a dynamic aperture, performs loss modeling, and transforms it into an optimization problem to effectively suppress deceptive jammers. However, there are problems with incomplete separation of high-fidelity jammers and residual energy.

[0005] Methods based on transmit modulation reduce the probability of radar being blocked by modulating the detection signal or transmission mode, such as waveform diversity, frequency agility, and radio frequency shielding. This means that the waveform or waveform group emitted by the radar within different pulse repetition intervals has one or more different parameters, such as carrier frequency, modulation mode, modulation rate, initial phase, and coding codeword.

[0006] Among them, the method of using phase perturbation to achieve anti-jamming has attracted widespread attention from scholars. For example, the paper "SAR anti-jamming technique using orthogonal LFM-PC hybrid modulated signal" uses a set of orthogonal phase-coded signals with different pulse repetition intervals (PRI) to generate LFM-PC signals, and the paper "High-Fidelity SAR Intermittent Sampling Deceptive Jamming Suppression Using Azimuth Phase Coding" uses a phase modulation factor to perform azimuth phase coding to suppress deceptive jamming.

[0007] However, most existing methods use phase jump or fixed phase coding sequences and matched filtering. If the opponent knows the phase change pattern and launches targeted interference, it will have a huge impact on the related work of SAR, and there is no constraint on the target signal sidelobes and imaging performance. Summary of the Invention

[0008] To address these shortcomings, this application proposes a SAR (Synthetic Aperture Radar) anti-interference method based on multi-parameter optimization of RD (Range Doppler Algorithm, RDA) imaging, aiming to integrate waveform modulation and reception processing. This method reduces the gain of the synthesized beam in the direction of the jammer without overly complicating the detection waveform, thereby improving the probability of intercepting the jammer and imaging performance.

[0009] To implement the above solution, this application adopts the following technical solutions:

[0010] A SAR anti-interference method based on multi-parameter optimization of RD imaging, comprising:

[0011] Based on the cost function, the waveform phase parameters and non-matched filter coefficients of SAR are optimized, and the alternating direction multiplier method is used to update the parameter vector so that the current cost function converges to obtain the phase parameters and azimuth filter coefficients.

[0012] The phase parameter is used to modulate the linear frequency modulation signal and used as the SAR transmission signal.

[0013] The filter coefficients are used to modulate the azimuth matched filter function in the range Doppler imaging algorithm. This method reduces the gain of the synthesized beam in the direction of the jammer without overly complicating the detection waveform, thereby improving the probability of intercepting the jammer and the imaging performance.

[0014] Preferably, the cost function is composed of sidelobe energy integration, interference signal energy integration, integrated sidelobe ratio constraint, non-matched filter peak gain loss constraint and constant modulus constraint.

[0015] Preferably, in the SAR anti-interference method based on multi-parameter optimization of RD imaging, the upper bound of the cost function is approximated by the second-order Taylor expansion to replace the cost function, and then the alternating direction multiplier method is used to solve it so that it converges to a predefined threshold or reaches the maximum number of iterations.

[0016] Preferably, in the SAR anti-interference method based on multi-parameter optimization of RD imaging, obtaining the phase parameters and azimuth filter coefficients includes the following steps:

[0017] 1) First, use the random phase sequence to initialize the phase and azimuth non-matched filter coefficients h=[h1,h2,…,h N ] T , then the waveform of the slow-time domain point target SAR echo after pulse compression and RCMC is x=[x1,x2,…,x N ] T ,in Indicates the current PRI phase, R indicates the distance between the point target and the carrier aircraft,

[0018] 2) According to the prior information of the jammer position, set the jamming system response model J = diag{j}, where j = [j1, j2, ..., j N ] T , Interference signal x j =Jx,

[0019] 3) Set the cost function based on the preset performance indicators:

[0020] f(x,h)=α t h H X H DXh+α j h H Ψ H Ψh+α1|dXh-β1x H h| 2 +α2|h H x-β2| 2 , where X is the transmission signal expansion matrix, Ψ is the interference signal expansion matrix, α t ,α j ,α1,α2 are the target constraint levels, β1 is the ideal value of the integral sidelobe ratio, and β2 is the ideal value of the filter gain.

[0021] 4) Use the second-order Taylor expansion to approximate the cost function,

[0022]

[0023] in:

[0024] To obtain the waveform sequence

[0025] Initial phase sequence

[0026] and filter coefficients

[0027] Preferably, the method of utilizing the phase parameter to modulate the linear frequency modulation signal and using the modulated linear frequency modulation signal as the transmission signal of the SAR includes:

[0028] Using phase parameters Modulate the linear frequency modulation signal as the SAR transmission signal The target echo simulation is used to realize the interaction between the transmitted signal and the target scene and obtain the target echo signal.

[0029] Preferably, in the SAR anti-interference method based on multi-parameter optimization of RD imaging,

[0030] The echo signal is de-carriered and converted into a baseband signal, and then range-matched filtering is performed. and dephase encoding to achieve range-directed pulse compression.

[0031] Preferably, in the SAR anti-interference method based on multi-parameter optimization of RD imaging,

[0032] When matching filter in frequency domain, multiply the linear frequency phase factor exp(-j4π(Vsinθ)t of compensation distance by a f r / c), perform range migration correction,

[0033] Then use the filter coefficient h to modulate the azimuth matched filter function The signal is subjected to unmatched filtering to obtain scene imaging.

[0034] Beneficial effects

[0035] Compared to existing approaches, the multi-parameter optimization SAR anti-interference method based on RD imaging proposed in this application achieves both anti-spoofing interference and reduced impact on imaging performance. Experimental results also demonstrate that the proposed optimization cost function converges quickly when the optimization sequence length is limited, improving anti-interference performance. It can be applied to SAR imaging systems based on the RD imaging algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present application will be further described below with reference to the accompanying drawings and embodiments:

[0037] Figure 1 This is a flowchart of the SAR signal processing process according to an embodiment of the present application;

[0038] Figure 2 This is a schematic diagram of the SAR signal processing flow in an embodiment of the present application;

[0039] Figure 3 It is the cost function iteration curve of the embodiment of the present application.

[0040] Figure 4 This is a comparison diagram of the imaging effects under deception interference when the radar transmitting signal is a typical linear frequency modulation signal and the RD imaging algorithm is used for imaging. a is the anti-interference effect of the typical linear frequency modulation signal, b is the anti-interference effect of this embodiment, and the interference area is [89:159,111:181]. DETAILED DESCRIPTION

[0041] Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that, in this application, unless otherwise specified, "a plurality of" means two or more than two, and "and / or" includes any and all combinations of one or more of the associated listed items.

[0043] This application discloses a SAR anti-interference method based on multi-parameter optimization of RD imaging, which can effectively filter out deceptive interference and ensure clear imaging of the target.

[0044] The method includes (see Figure 1 ):

[0045] Based on the cost function, the waveform phase parameters and non-matched filter coefficients of SAR are optimized, and the alternating direction multiplier method is used to update the parameter vector so that the current cost function converges to obtain the phase parameters and azimuth filter coefficients (such as the phase parameters and azimuth filter coefficients are obtained based on the algorithm model).

[0046] The phase parameter is used to modulate the linear frequency modulation signal and used as the SAR transmission signal.

[0047] The filter coefficient is used to modulate the azimuth matched filter function in the range Doppler imaging algorithm, and then anti-interference imaging is performed.

[0048] The cost function includes:

[0049] 1) The waveform of the slow-time domain point target SAR echo after pulse compression and RCMC is x=[x1,x2,…,x N ] T ,in represents the phase difference between the current PRI phase and the initial phase, R represents the distance between the point target and the carrier, assuming that the distance remains unchanged in space, the corresponding slow time domain non-matched filter coefficient vector is h = [h1,h2,…,h N ] T ;

[0050] 2) The interference scene consists of M point targets Indicates the phase difference between the jammer phase and the current transmitted signal phase, △R jni The distance difference between the interference point and the target center point to the carrier aircraft, λ is the wavelength, c is the wave speed, and the waveform after interference is: x p =Px, where P = diag{p1,p2,…,p N}.

[0051] 3) The integral sidelobe ratio constraint is that the energy ratio G of the peak and the sidelobe integral is usually greater than 20dB, that is: dHx-β1h H x=0, where

[0052] 4) Unmatched filter peak gain loss, i.e. h H xx H h-β2 2 =0, where β2 = N;

[0053] 5) The above four items constitute the required cost function, using α t ,α j ,α1,α2 represent the degree of constraint on each item, that is: f(x,h)=α t h H X H DXh+α j h H Ψ H Ψh+α1|dXh-β1x H h| 2 +α2|h H x-β2| 2 .

[0054] To minimize the above cost function, the optimization process includes the following steps:

[0055] 1) First, the above cost function is split and integrated, which can be expressed as f(x,h)=h H Zh+h H z, where, z=-2α2β2x, Z=α i X H DX+α j Ψ H Ψ+α1(X H d H dX-2β1Re(xdX)+(β1 2 +α2)xx H ).

[0056] 2) Assuming that the transmitted waveform x is known, optimize the non-matched filter coefficient h, according to the second-order Taylor expansion f(x k ,h)≤g1(x (k) ,h|h (k) ), the current optimization expression can be transformed into; Can be obtained in, is the maximum approximation of the Hessian matrix of the cost function;

[0057] 3) Then split and integrate the current cost function into f(x,h)=h H Yh+h H y, where, y=-2α2β2h, Y=α t H H DH+α j PH H HP+α1(H H d H dH-2β1Re(hdH)+(β1 2 +α2)hh H ).

[0058] 4) Assuming the unmatched filter coefficient h is known, optimize the transmit waveform x, Λ2 (k) is the maximum approximation of the Hessian matrix of the current cost function, which can be obtained; x (k+1) =exp(jarg(-4(Y (k) x (k) +y (k) )+Λ2 (k) x (k) ))

[0059] 5) Repeat the above steps until the termination condition is met: or the maximum number of iterations is reached, is a predefined threshold.

[0060] The working process of the method proposed in this application:

[0061] Firstly, a jamming model is designed according to the prior information of the enemy jammer's position and substituted into the cost function. The phase and filter coefficient sequences are obtained by using the alternating direction multiplication method and the method of maximizing the cost function. Finally, the phase and filter coefficient sequences are used as parameters and substituted into the radar transmission signal and reception processing process.

[0062] Next, the above method is verified through distributed scenario simulation experiments.

[0063] In this embodiment, the transmission signal is a linear frequency modulation phase coded signal, the interference type is a forwarding false image deception interference, the DRFM of the jammer adopts a sample pulse mode, and the imaging mode is a range Doppler algorithm with azimuth non-matched filtering.

[0064] Specifically, we first verify the effectiveness of the waveform and filter design method through numerical simulation, and then evaluate the anti-deception jamming and imaging performance of the proposed method through imaging simulation of scenes with high-fidelity deception jamming. Figure 2 , which includes the following steps:

[0065] 1) First, according to the current conditions, the transmission signal expansion matrix X and the interference signal expansion matrix Ψ are initialized, the integral sidelobe ratio constraint degree α1, the non-matched filter gain loss constraint degree α2, the sidelobe energy and interference energy constraint degree α t ,α j , the predefined ideal integrated sidelobe ratio β1 is initialized according to the preset conditions;

[0066] 2) Assuming that the transmitted waveform x is known, optimize the non-matched filter coefficient h and obtain the optimized azimuth non-matched filter coefficient:

[0067]

[0068] 3) Initialize the filter coefficient expansion matrix H and the interference modulation expansion matrix P according to the current conditions. Assuming that the non-matched filter coefficient h is known, optimize the transmit waveform x and obtain the optimized modulation phase: x (k+1) =exp(jarg(-(4Y (k) -tr(Y (k) )I N )x (k) -4y (k) );

[0069] 4) Repeat the above steps until the termination condition is met: Or the maximum number of iterations is reached to obtain the phase sequence and filter coefficients;

[0070] 5) Use phase sequence modulated linear frequency modulation signal as radar transmission signal, and generate scene echo through radar target echo simulator and radar deception jammer realized by simulation experiment;

[0071] 6) The scene echo data is processed using the range Doppler imaging algorithm and the azimuth non-matched filter coefficients obtained in this example to obtain the final imaging of the distributed scene.

[0072] Reference Figure 3 and Figure 4 ,In order to more intuitively show the effectiveness of the method, SAR imaging ,experiments are conducted using the parameters solved by this method.

[0073] Figure 3 is the cost function iteration curve of the embodiment of the present application,

[0074] Figure 4 The imaging effect of the embodiment of the present application is compared with that of the radar transmitting signal being a typical linear frequency modulation signal under false image deception interference. The left side a is the anti-interference effect of the typical linear frequency modulation signal, and the right side b is the anti-interference effect of the present embodiment. The interference area is [89:159,111:181]. Figure 4 It can be seen that the cost function proposed in this application can converge quickly, and the obtained parameters can achieve good anti-interference effect during the imaging process.

[0075] The experimental results show that the SAR anti-interference method based on multi-parameter optimization of RD imaging disclosed in this application has a fast convergence speed and good anti-interference performance.

[0076] The above embodiments are intended only to illustrate the technical concepts and features of this application. Their purpose is to enable those familiar with the art to understand the content of this application and implement it accordingly. They are not intended to limit the scope of protection of this application. Any equivalent changes or modifications made in accordance with the spirit of this application shall be included in the scope of protection of this application.

Claims

1. A SAR anti-interference method based on multi-parameter optimization of RD imaging, characterized by: include: Based on the cost function, the waveform phase parameters and non-matched filter coefficients of SAR are optimized, and the alternating direction multiplier method is used to update the parameter vector so that the current cost function converges to obtain the phase parameters and azimuth filter coefficients. The phase parameter is used to modulate the linear frequency modulation signal and used as the SAR transmission signal. The filter coefficient is used to modulate the azimuth matched filter function in the range Doppler imaging algorithm; The cost function is composed of sidelobe energy integral, interference signal energy integral, integrated sidelobe ratio constraint, non-matched filter peak gain loss constraint and constant modulus constraint; The cost function is replaced by the upper bound of the approximate cost function using the second-order Taylor expansion, and then solved using the alternating direction multiplier method to converge to a predefined threshold or reach the maximum number of iterations; The method of using the phase parameter to modulate the linear frequency modulation signal and using the modulated linear frequency modulation signal as the SAR transmission signal includes: Using phase parameters Modulate the linear frequency modulation signal as the SAR transmission signal The target echo simulation is used to realize the interaction between the transmitted signal and the target scene and obtain the target echo signal.

2. The SAR anti-interference method based on multi-parameter optimization of RD imaging according to claim 1, characterized in that: Obtaining the phase parameters and azimuth filter coefficients includes the following steps: 1) First, use the random phase sequence to initialize the phase and azimuth non-matched filter coefficients h=[h1,h2,…,h N ] T , then the waveform of the slow-time domain point target SAR echo after pulse compression and RCMC is x=[x1,x2,…,x N ] T ,in Indicates the current PRI phase, R indicates the distance between the point target and the carrier aircraft, 2) According to the prior information of the jammer position, set the jamming system response model J = diag{j}, where j = [j1, j2, ..., j N ] T , Interference signal x j =Jx, 3) Set the cost function based on the preset performance indicators: f(x,h)=α t h H X H DXh+α j h H Ψ H Ψh+α1|dXh-β1x H h| 2 +α2|h H x-β2| 2 , where X is the transmission signal expansion matrix, Ψ is the interference signal expansion matrix, α t ,α j ,α1,α2 are the target constraint levels, β1 is the ideal value of the integral sidelobe ratio, and β2 is the ideal value of the filter gain. 4) Use the second-order Taylor expansion to approximate the cost function, in: To obtain the waveform sequence Initial phase sequence and filter coefficients 3. The SAR anti-interference method based on multi-parameter optimization of RD imaging according to claim 1, characterized in that: Also includes: The echo signal is de-carriered and converted into a baseband signal, and then range-matched filtering is performed. and dephase encoding to achieve range-directed pulse compression.

4. The SAR anti-interference method based on multi-parameter optimization of RD imaging according to claim 3, characterized in that: When matching filter in frequency domain, multiply the linear frequency phase factor exp(-j4π(Vsinθ)t of compensation distance by a f r / c), perform range migration correction, Then use the filter coefficient h to modulate the azimuth matched filter function The signal is subjected to unmatched filtering to obtain scene imaging.

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