Multi-channel sar azimuth distributed deception jamming method under doppler ambiguity imaging

Abstract

The application discloses a kind of multi-channel SAR azimuth distributed deception jamming methods under the imaging of Doppler ambiguity, and the existence of distance " defocusing " condition and imaging azimuth Doppler ambiguity problem of deception jamming mode under azimuth multi-channel SAR system, on the basis of single-channel SAR deception jamming basic principle, equivalent phase center model is combined, and by the coordinated design of multiple machines, it is transformed into linear constraint optimization problem of interference modulation frequency offset compensation, and the best matching function vector is obtained using adaptive iterative algorithm.The method of the application obtains good distance focusing result in false point target imaging, and simultaneously realizes the Doppler ambiguity-free imaging of interference scene azimuth.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, specifically relating to a multi-channel SAR azimuth distributed deception jamming method. Background Technology

[0002] A high-resolution, wide-swath multi-channel SAR system divides the flight path into multiple sub-apertures with offset phase centers. The reference channel transmits the beam, while multiple channels receive the scattered echoes, forming a "one-transmit, multiple-receive" mode. This mode ensures a high azimuth Doppler bandwidth, thus achieving high azimuth resolution. Multiple equivalent phase centers increase the number of azimuth sampling points obtained within a single pulse repetition time, reducing the PRF limitation and increasing the range swath width. The working mechanism of multi-channel high-resolution, wide-swath SAR imaging is as follows: Figure 1 As shown.

[0003] For traditional SAR, with the continuous development of jamming technology, the Digital Radio Frequency Storage Jammer (DRFM) has become the mainstream hardware device for deception jamming transmitters. For deception jamming methods in single-channel SAR systems, the difference in slant range history distribution between real and false point targets is used to perform time delay and frequency domain modulation to achieve modulation and forwarding, thereby jamming target imaging. Its basic principle and process are as follows: Figure 2 As shown. However, for multi-channel SAR systems, the interference modulated echo received by each channel suffers from azimuth Doppler ambiguity. Furthermore, the interference signal modulated and relayed to the reference channel is simultaneously received by other channels, leading to range mismatch in subsequent imaging. How to utilize DRFM to deceive and jam false targets in azimuth multi-channel SAR imaging is an urgent problem to be solved. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a multi-channel SAR azimuth-distributed deception jamming method for de-Doppler blurring imaging. Addressing the range-direction "defocusing" and azimuth Doppler blurring issues in deception jamming modes of multi-channel SAR systems, this invention, based on the fundamental principles of single-channel SAR deception jamming and incorporating an equivalent phase center model, transforms the problem into a linearly constrained optimal problem of interference modulation frequency offset compensation through multi-machine collaborative design. An adaptive iterative algorithm is then used to obtain the optimal matching function vector. This invention achieves good range-direction focusing results in imaging false point targets and simultaneously realizes azimuth-free imaging of the jammed scene.

[0005] The technical solution adopted by this invention to solve its technical problem includes the following steps:

[0006] Step 1: Construct three jamming sites in the azimuth direction. The relationship between these sites and the slant range history of each channel of the SAR platform is expressed as: R i,j (t aThe relationship between spurious points and the slant range history of each channel of the SAR platform is expressed as: R i '(t a The difference in slant range history between the false point and the jammer is expressed as ΔR. ij The actual modulated echo received by the jammer on each channel is as follows:

[0007]

[0008] Where i is the channel number, j is the jammer number, and t is the jammer number. a Indicates location and time. For range-direction frequency;

[0009] The frequency deviation vector of the j-th jammer is represented as B. j Set the compensation factor of the j-th jammer to Δe. j The compensation vector A can be represented as: A = [△e1 △e2 △e3] T The deviation vector D after receiving the j-th interference compensation in each channel j Represented as: D j =C·A·B j Combining the components of each jammer, the compensated deviation matrix D is expressed as: D = [D1 D2 D3] T Where C = [1 1 1];

[0010] Step 2: Construct the linear constraint transformation relation:

[0011] stC·A·B=D j D = [D1 D2 D3]

[0012]

[0013] Where B represents the frequency deviation vector of the three jammers;

[0014] Step 3: At the initial feasible point D (k) At this point, perform a first-order Taylor polynomial expansion of f(D):

[0015]

[0016] in, Represents the partial derivative of a vector;

[0017] Step 4: Transform Step 2 into minimizing the expansion within the feasible region A:

[0018]

[0019] stD∈A

[0020] Step 5: Given an initial feasible point D (1) If the allowable error ε > 0, set k = 1;

[0021] Step 6: Solve Step 4 to obtain the optimal solution Y. (k) ,judge: If true, the input quantity D is obtained. (k) and the optimal solution Y (k) Proceed to step 9; otherwise, continue with step 7 below.

[0022] Step 7: From D (k) Along Y (k) -D (k) Direction in connection D (k) and Y (k) The search is performed over the region to obtain the iteration step size λ. k :

[0023] minf(D (k) +λ(Y (k) -D (k) ))

[0024] st0≤λ≤1

[0025] Step 8: Determine the step size λ of the iteration. k , making D (k+1) =D (k) +λ k (Y (k) -D (k) Let k increment by 1, then proceed to step 6;

[0026] Step 9: The false target azimuth echo after frequency compensation for the i-th receiving channel in the range cell is... The time-domain and frequency-domain forms of the pre-filter for each channel are h, respectively. i (t a ) and H n (f a Construct a matrix form H(f) that matches the number of channels. a );f a Indicates the azimuth Doppler frequency; where, This is the channel distance frequency offset compensation amount;

[0027] The pre-filtered spatial domain H(f) a The vector is transformed into a spatial steering vector α, and the coefficient matrix P(f) is reconstructed. a The weighted coefficient vector is composed of: W = P(f) a )=Qα -1 By weighting the azimuth echoes of false targets in the spatial domain of each channel using a weighted coefficient vector composed of reconstruction functions, a beam echo without ambiguity components is obtained. Where Q is Q = [1 … 1]1×i ;

[0028] Step 10: Stitch together the unambiguous components corresponding to each spatial angle to obtain the Doppler spectrum of the equivalent single-channel false target echo, and then perform subsequent SAR imaging operations.

[0029] Preferably, step 1 is performed as follows:

[0030] The relationship between each jammer and spoof point and the slant range history of each channel of the SAR platform (R) i,j (t a ) and R i '(t a Specifically, they are expressed as follows:

[0031]

[0032]

[0033] Where R0 is the shortest slant range under zero Doppler, d is the channel spacing, v is the SAR platform movement speed, j is the distance between jammers, x and y are the azimuth position and perpendicular distance to the azimuth of the false point target, respectively; r represents the jammer spacing.

[0034] The difference in slant range history between the false point and the jammer, ΔR ij Represented as:

[0035]

[0036] Each channel received the actual modulated echo from the jammer. Specifically, it is expressed as follows:

[0037]

[0038] Among them, f c Where c is the carrier frequency and c is the speed of light;

[0039] The frequency deviation vector B of the j-th jammer j Specifically, it is expressed as follows:

[0040]

[0041] The deviation vector D after receiving the j-th interference compensation in each channel j Represented as:

[0042]

[0043] Each channel is an independent self-generated and self-retrieved wave with an equivalent phase center. After reconstruction, linear azimuth compression is performed to obtain an imaging model that has a mapping relationship with the model before azimuth pulse matched filtering. The optimal azimuth cooperative interference imaging is transformed into finding the optimal solution for linear constraints on azimuth interference modulation frequency offset compensation. The optimal channel imaging and the optimal modulation compensation are consistent.

[0044] Preferably, step 9 specifically comprises:

[0045] The false target azimuth echo after frequency compensation for the i-th receiving channel in the range cell is specifically as follows:

[0046]

[0047] in, σ is the channel distance frequency offset compensation amount. m Let a be the backscattering coefficient. a (t a ) represents the azimuth modulation envelope term, λ is the wavelength, and Δx represents the channel position vector, expressed as:

[0048] Δx=[△x1 … △x5]=[D0 D0 / 2 0 -D0 / 2 -D0]

[0049] The echo form of a single channel is represented as:

[0050] The time-domain and frequency-domain forms of the pre-filter for each channel h i (t a ) and H n (f a They are respectively:

[0051]

[0052] Where δ is the impulse function;

[0053] Construct a matrix form H(f) that matches the number of channels. a ) is represented as:

[0054]

[0055] Among them, (f a -k·PRF) represents the k-th ambiguity component of the channel. The relationship between the false target azimuth echo containing the ambiguity component and the true component echo is expressed as: Expanded to:

[0056]

[0057] The pre-filtered spatial domain H(f) a Transform into spatial guiding vector α:

[0058]

[0059] Reconstruct the coefficient matrix P(f) a The weighted coefficient vector is composed of: W = P(f) a )=Αα -1 By weighting the azimuth echo of the false target in each channel using a weighted coefficient vector composed of reconstruction functions, a beam echo without ambiguity components is obtained:

[0060]

[0061] The beneficial effects of this invention are as follows:

[0062] This invention can effectively solve the problems of range-direction "defocus" and azimuth Doppler blur in the deception interference mode of multi-channel SAR systems, so as to achieve good focusing in the range direction and no Doppler blur component in the azimuth direction for imaging false points generated by deception interference. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the working mode of azimuth multi-channel SAR imaging.

[0064] Figure 2 This is a flowchart illustrating the basic principle of SAR active deception jamming.

[0065] Figure 3 The following is a comparison of the imaging before and after processing by the multi-channel SAR azimuth distributed deception jamming algorithm in this embodiment of the invention: (a) Imaging of the i=2 false point target without processing (without frequency offset compensation); (b) Imaging of the i=3 false point target (without frequency offset compensation); (c) Range sampling cross-section of the i=2 false point target; (d) Range sampling cross-section of the i=3 false point target; (e) Imaging of the real point target (upsampling); (f) Imaging of the false point target after frequency offset compensation (upsampling); (g) Range sampling cross-section of the real point target; (h) Sampling cross-section of the false point target after frequency offset compensation; (i) Iterative results of adaptive optimal output.

[0066] Figure 4 The following are the results of the deception interference imaging after the azimuth reconstruction algorithm of this invention: (a) Real part of the channel SAR echo; (b) Channel azimuth echo sampling; (c) Extraction of azimuth Doppler blur components (k=4); (d) Results after deblurring; (e) Reconstructed image without Doppler blur. Detailed Implementation

[0067] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0068] To address the range-direction defocusing and azimuth Doppler blurring issues in deception jamming modes of multi-channel SAR systems, this paper proposes a distributed azimuth jamming algorithm for multi-channel SAR under de-Doppler blurring imaging, based on the fundamental principles of single-channel SAR deception jamming and incorporating an equivalent phase center model. This algorithm achieves good range-direction focusing results in imaging false point targets and simultaneously realizes Doppler blur-free imaging in the azimuth direction of the jammed scene.

[0069] A distributed azimuth deception jamming algorithm for multi-channel SAR under Doppler blurring imaging is proposed. Through multi-machine collaborative design, it is transformed into a linear constrained optimal problem of interference modulation frequency offset compensation. An adaptive iterative algorithm is then used to obtain the optimal matching function vector. The algorithm includes the following steps:

[0070] Step 1: Construct three jamming sites in the azimuth direction. The relationship between these sites and the slant range history of each channel of the SAR platform is expressed as: R i,j (t a The relationship between spurious points and the slant range history of each channel of the SAR platform is expressed as: R i '(t a The difference in slant range history between the false point and the jammer is expressed as ΔR. ij The actual modulated echo received by the jammer on each channel is as follows: Where i is the channel number, j is the jammer number, and t is the jammer number. a Indicates location and time. For range-direction frequency.

[0071] The frequency deviation vector of the j-th jammer is represented as B. j Set the compensation factor of the j-th jammer to Δe. j The compensation vector A can be represented as: A = [△e1 △e2 △e3] T The deviation vector D after receiving the j-th interference compensation in each channel j Represented as: D j =C·A·B j Combining the components of each jammer, the compensated deviation matrix D is expressed as: D = [D1D2D3] T .

[0072] Where C = [1 1 1].

[0073] Step 2, construct the linear constraint transformation relation:

[0074]

[0075] Step 3, at the initial feasible point D (k) At this point, perform a first-order Taylor polynomial expansion of f(D):

[0076]

[0077] in, This represents the partial derivative of a vector.

[0078] Step 4, transform step 3 into minimizing the expansion within the feasible region A:

[0079]

[0080] Step 5, given the initial feasible point D (1) If the allowable error ε>0, set k=1.

[0081] Step 6: Solve step 5 to obtain the optimal solution Y. (k) ,judge: If true, the input quantity D is obtained. (k) and the optimal solution Y (k) Proceed to step 9; otherwise, continue with the following steps.

[0082] Step 7, from D (k) Along Y (k) -D (k) Direction in connection D (k) and Y (k) The search is performed over the region to obtain the iteration step size λ. k :

[0083]

[0084] Step 8: Determine the iteration step size λ k , making D (k+1) =D (k) +λ k (Y (k) -D (k) Let k increase by 1, and go to step 6.

[0085] Step 9, the false target azimuth echo after frequency compensation for the i-th receiving channel under the range cell is... The time-domain and frequency-domain forms of the pre-filter for each channel are h, respectively. i (t a ) and H n (f a Construct a matrix form H(f) that matches the number of channels. a ).

[0086] in, This is the channel distance frequency offset compensation amount.

[0087] The pre-filtered spatial domain H(f) a The vector is transformed into a spatial steering vector α, and the coefficient matrix P(f) is reconstructed. a The weighted coefficient vector is composed of: W = P(f)a )=Qα -1 By weighting the azimuth echoes of false targets in the spatial domain of each channel using a weighted coefficient vector composed of reconstruction functions, a beam echo without ambiguity components is obtained.

[0088] Where A is A = [1 … 1] 1×i .

[0089] Step 10: Stitch together the unambiguous components corresponding to each spatial angle to obtain the Doppler spectrum of the equivalent single-channel false target echo, and then perform subsequent SAR imaging operations.

[0090] Step 1 is performed as follows:

[0091] The relationship between each jammer and spoof point and the slant range history of each channel of the SAR platform (R) i,j (t a ) and R i '(t a Specifically, they are expressed as follows:

[0092]

[0093]

[0094] Where R0 is the shortest slant range under zero Doppler, d is the channel spacing, v is the SAR platform movement speed, j is the distance between jammers, and x and y are the azimuth position and the distance perpendicular to the azimuth of the false point target, respectively.

[0095] The difference in slant range history between the false point and the jammer, ΔR ij Represented as:

[0096]

[0097] Each channel received the actual modulated echo from the jammer. Specifically, it is expressed as:

[0098]

[0099] Among them, f c denoted as carrier frequency, and c as the speed of light.

[0100] The frequency deviation vector B of the j-th jammer j Specifically, it is expressed as follows:

[0101]

[0102] The deviation vector D after receiving the j-th interference compensation in each channel j Represented as:

[0103]

[0104] Each channel is an independent, self-generated and self-retrieved wave with an equivalent phase center. After reconstruction, linear azimuth compression is performed to obtain an imaging model that maps to the model before azimuth pulse matched filtering. The optimal azimuth cooperative interference imaging is transformed into finding the optimal solution for linear constraints on azimuth interference modulation frequency offset compensation. The optimal channel imaging and the optimal modulation compensation are consistent.

[0105] Step 9 is performed as follows:

[0106] The false target azimuth echo after frequency compensation for the i-th receiving channel in the range cell is specifically as follows:

[0107]

[0108] in, σ is the channel distance frequency offset compensation amount. m Let a be the backscattering coefficient. a (t a ) represents the azimuth modulation envelope term, λ is the wavelength, and Δx represents the channel position vector, expressed as:

[0109] Δx=[Δx1 … Δx5]=[D0 D0 / 2 0 -D0 / 2 -D0].

[0110] The echo form of a single channel is represented as:

[0111] The time-domain and frequency-domain forms of the pre-filter for each channel h i (t a ) and H n (f a They are respectively:

[0112]

[0113] Where δ is the impulse function.

[0114] Construct a matrix form H(f) that matches the number of channels. a ) is represented as:

[0115]

[0116] Among them, (f a -k·PRF) represents the k-th ambiguity component of the channel. The relationship between the false target azimuth echo containing the ambiguity component and the true component echo is expressed as: Expanded to:

[0117]

[0118] The pre-filtered spatial domain H(f)a Transform into spatial guiding vector α:

[0119]

[0120] Reconstruct the coefficient matrix P(f) a The weighted coefficient vector is composed of: W = P(f) a )=Αα -1 By weighting the azimuth echo of the false target in each channel using a weighted coefficient vector composed of reconstruction functions, a beam echo without ambiguity components is obtained:

[0121] Thus, a multi-channel SAR azimuth distributed deception jamming algorithm under de-Doppler blur imaging has been basically completed.

[0122] Example:

[0123] Figure 3 and Figure 4 This is a diagram showing the simulation data processing results of the present invention;

[0124] The effectiveness of the present invention will be further verified by simulation experimental data.

[0125] (I) Simulation Experiment

[0126] 1. Measured parameters

[0127] To verify the effectiveness of the method of the present invention, the measured data parameters in Table 1 are given here.

[0128] Table 1 Simulation Data Parameters

[0129] SAR platform speed 7000m / s Channel spacing 10m Pulse Repetition Frequency (PRF) <![CDATA[1.8×10 3 Hz]]> Interference machine location 300m Orientation step duration <![CDATA[1.75×10 -3 s]]> Doppler center frequency 2500Hz Platform speed 5000m / s Aperture center time point 0s Interference source azimuth coefficient 500m Number of jammers 3 <![CDATA[Doppler bandwidth B a > <![CDATA[7.5×10 3 Hz]]>

[0130] 2. Experiment Content

[0131] Figure 3 The diagram illustrates the focusing imaging results of false point targets in the range direction before and after processing with the multi-channel SAR azimuth distributed deception jamming algorithm proposed in this invention, under different channel numbers. As can be seen from the figure, after the method of this invention collaboratively compensates for frequency offset using multiple jammers, the false point targets exhibit better imaging in the range direction, effectively suppressing the blurring component in the range direction, achieving range compression matching, and avoiding the problem of defocusing. Figure 4 The diagram illustrates the image results of deception interference after processing with the azimuth reconstruction algorithm. A clear image of the false interference scene after azimuth de-ambiguity removal is obtained. Therefore, the method of this invention can effectively solve the problems of range-direction "defocus" and azimuth Doppler blur in deception interference modes under azimuth multi-channel SAR systems.

[0132] In conclusion, the simulation data experiments have verified the correctness, effectiveness, and reliability of the present invention.

Claims

1. A method for distributed azimuth deception interference in multi-channel SAR under Doppler blur resolution imaging, characterized in that, Includes the following steps: Step 1: Construct three jamming sites in the azimuth direction. The relationship between these sites and the slant range history of each channel of the SAR platform is expressed as: R i,j (t a The relationship between spurious points and the slant range history of each channel of the SAR platform is expressed as: R i '(t a The difference in slant range history between the false point and the jammer is expressed as ΔR. ij The actual modulated echo received by the jammer on each channel is as follows: Where i is the channel number, j is the jammer number, and t is the jammer number. a Indicates location and time. For range-direction frequency; The frequency deviation vector of the j-th jammer is represented as B. j Set the compensation factor of the j-th jammer to Δe. j The compensation vector A can be represented as: A = [△e1 △e2 △e3] T The deviation vector D after receiving the j-th interference compensation in each channel j Represented as: D j =C·A·B j Combining the components of each jammer, the compensated deviation matrix D is expressed as: D = [D1 D2 D3] T Where C = [1 1 1]; Step 2: Construct the linear constraint transformation relation: s.t.C·A·B=D j ,D=[D1 D2 D3] f(D)=||D·C T ||1 Where B represents the frequency deviation vector of the three jammers; Step 3: At the initial feasible point D (k) At this point, perform a first-order Taylor polynomial expansion of f(D): in, Represents the partial derivative of a vector; Step 4: Transform Step 2 into minimizing the expansion within the feasible region A: stD∈A Step 5: Given an initial feasible point D (1) If the allowable error ε > 0, set k = 1; Step 6: Solve Step 4 to obtain the optimal solution Y. (k) ,judge: If true, the input quantity D is obtained. (k) and the optimal solution Y (k) Proceed to step 9; otherwise, continue with step 7 below. Step 7: From D (k) Along Y (k) -D (k) Direction in connection D (k) and Y (k) The search is performed over the region to obtain the iteration step size λ. k : min f(D (k) +λ(Y (k) -D (k) )) st0≤λ≤1 Step 8: Determine the step size λ of the iteration. k , making D (k+1) =D (k) +λ k (Y (k) -D (k) Let k increment by 1, then proceed to step 6; Step 9: The false target azimuth echo after frequency compensation for the i-th receiving channel in the range cell is... The time-domain and frequency-domain forms of the pre-filter for each channel are h, respectively. i (t a ) and H n (f a Construct a matrix form H(fa) that matches the number of channels; f a Indicates the azimuth Doppler frequency; where, This is the channel distance frequency offset compensation amount; The pre-filtered spatial domain H(f) a The vector is transformed into a spatial steering vector α, and the coefficient matrix P(f) is reconstructed. a The weighted coefficient vector is composed of: W = P(f) a )=Qα -1 By weighting the azimuth echoes of false targets in the spatial domain of each channel using a weighted coefficient vector composed of reconstruction functions, a beam echo without ambiguity components is obtained. Where Q is Q = [1 … 1] 1×i ; Step 10: Stitch together the unambiguous components corresponding to each spatial angle to obtain the Doppler spectrum of the equivalent single-channel false target echo, and then perform subsequent SAR imaging operations.

2. The method for distributed azimuth deception interference of multi-channel SAR under Doppler blur resolution imaging according to claim 1, characterized in that, The specific steps for step 1 are as follows: The relationship between each jammer and spoof point and the slant range history of each channel of the SAR platform (R) i,j (t a ) and R i '(t a Specifically, they are expressed as follows: Where R0 is the shortest slant range under zero Doppler, d is the channel spacing, v is the SAR platform movement speed, j is the distance between jammers, x and y are the azimuth position and perpendicular distance to the azimuth of the false point target, respectively; r represents the jammer spacing. The difference in slant range history between the false point and the jammer, ΔR ij Represented as: Each channel received the actual modulated echo from the jammer. Specifically, it is expressed as follows: Among them, f c Where c is the carrier frequency and c is the speed of light; The frequency deviation vector B of the j-th jammer j Specifically, it is expressed as follows: The deviation vector D after receiving the j-th interference compensation in each channel j Represented as: Each channel is an independent self-generated and self-retrieved wave with an equivalent phase center. After reconstruction, linear azimuth compression is performed to obtain an imaging model that has a mapping relationship with the model before azimuth pulse matched filtering. The optimal azimuth cooperative interference imaging is transformed into finding the optimal solution for linear constraints on azimuth interference modulation frequency offset compensation. The optimal channel imaging and the optimal modulation compensation are consistent.

3. The method for distributed azimuth deception interference of multi-channel SAR under de-Doppler blur imaging according to claim 2, characterized in that, Step 9 specifically involves: The false target azimuth echo after frequency compensation for the i-th receiving channel in the range cell is specifically as follows: in, σ is the channel distance frequency offset compensation amount. m Let a be the backscattering coefficient. a (t a ) represents the azimuth modulation envelope term, λ is the wavelength, and Δx represents the channel position vector, expressed as: Δx=[△x1 … △x5]=[D0 D0 / 2 0 -D0 / 2 -D0] The echo form of a single channel is represented as: The time-domain and frequency-domain forms of the pre-filter for each channel h i (t a ) and H n (f a They are respectively: Where δ is the impulse function; Construct a matrix form H(f) that matches the number of channels. a ) is represented as: Among them, (f a -k·PRF) represents the k-th ambiguity component of the channel. The relationship between the false target azimuth echo containing the ambiguity component and the true component echo is expressed as: Expanded to: The pre-filtered spatial domain H(f) a Transform into spatial guiding vector α: Reconstruct the coefficient matrix P(f) a The weighted coefficient vector is composed of: W = P(f) a )=Αα -1 By weighting the azimuth echo of the false target in each channel using a weighted coefficient vector composed of reconstruction functions, a beam echo without ambiguity components is obtained:

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