SAR intermittent sampling interference energy concentration method based on adaptive Kaier window function optimization

Abstract

The invention relates to an SAR intermittent sampling interference energy concentration method based on adaptive Kaiser window function optimization, and belongs to the field of radar signal interference. The method comprises the following steps: constructing a Kaiser window parameter objective function by taking a sidelobe performance index (PSL, ISL) as a constraint; constructing a parameter mapping relation according to the duty ratio and the period of the sampling signal; window function parameters are dynamically updated through a gradient descent method, and an adaptive Kaiser window is obtained; and performing energy centralized processing on the sampled SAR false target signal truncation position by using the window function. The self-adaptive Kaiser window can dynamically match sampling signal features and balance main lobe width and sidelobe suppression performance, the problem of performance degradation of a traditional fixed window function under a low duty ratio or a short sampling period is solved, the energy concentration ratio of a false target is remarkably improved and is close to real target energy distribution, the deception jamming effect on the SAR is effectively enhanced, and the deception jamming rate of the SAR is improved. And a new technical path is provided for the SAR interference technology.

Description

Technical Field

[0001] This invention belongs to the field of radar signal jamming technology, specifically relating to a method for SAR intermittent sampling jamming energy concentration based on adaptive Kasier window function optimization, which is applicable to deceptive jamming scenarios against synthetic aperture radar (SAR). Background Technology

[0002] Synthetic Aperture Radar (SAR) is a microwave coherent imaging radar capable of generating high-resolution radar images. Its performance is unaffected by flight altitude and weather conditions, possessing excellent day and night imaging capabilities and strong penetration power. Active jamming against SAR is mainly divided into suppression and deception. Among these, intermittent sampling forwarding jamming (ISBN) has become a popular deception jamming method due to its advantages such as strong real-time performance, low parameter dependence, and flexible variation in the number and spacing of false targets. Since ISBN was applied to SAR countermeasures, its performance analysis and improvement methods have been a research hotspot.

[0003] Current research on intermittent sampling forwarding interference has focused on expanding interference patterns, such as combining intermittent sampling with modulation methods like time modulation, noise modulation, phase modulation, and amplitude modulation, or combining intermittent sampling with two-dimensional frequency shifting or blind frequency shifting. However, there is limited research on the energy concentration problem. Especially after parameter modulation, the bandwidth of intermittent sampling forwarding interference is smaller than that of the original signal, which can easily lead to excessively high sidelobes due to bandwidth mismatch. This results in energy discontinuity, causing a significant difference between the energy of the false target and the real target, thus reducing the interference effect.

[0004] In traditional signal processing, fixed window functions (such as rectangular windows, Hanning windows, and Blackman windows) are often used for windowing to suppress sidelobes. However, SAR intermittently sampled signals exhibit significant intermittency in the time domain, and fixed window functions cannot be dynamically adjusted according to signal characteristics such as sampling width and frequency. Once a fixed window function is selected, its energy concentration effect deteriorates rapidly with changes in sampling parameters, especially in scenarios with low duty cycles or short sampling periods, where spectral leakage and sidelobe effects are more pronounced, making it difficult to meet interference performance requirements. Therefore, to overcome these difficulties, it is necessary to design a more superior adaptive window function to optimize the spectral characteristics of the interference signal and concentrate the interference energy more effectively. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method for concentrating SAR intermittent sampling interference energy based on adaptive Kaiser window function optimization, addressing the aforementioned shortcomings of existing technologies. By constructing an adaptive adjustment mechanism, the window function can dynamically match the duty cycle and period of the sampled signal, optimize spectral characteristics, improve the energy concentration of the interference signal, reduce spectral leakage and sidelobe effects, and enhance the deception interference effect.

[0006] The technical solution adopted by this invention to solve the above problems is as follows: a method for SAR intermittent sampling interference energy concentration based on adaptive Kaiser window function optimization. This method constructs a Kaiser window parameter objective function with sidelobe performance indicators as constraints, establishes a mapping relationship between window function parameters based on sampled signal characteristics, dynamically updates the parameters using gradient descent to obtain the optimal window shape, and ultimately achieves interference signal energy concentration. The specific technical solution includes the following steps: S1: Construct the objective function for the Kaiser window parameters Using the sidelobe performance metrics of SAR multi-segment false target signals after intermittent sampling as constraints, the sidelobe performance metrics include Peak Sidelobe Level (PSL) and Integral Sidelobe Energy Ratio (ISL). PSL describes the amplitude ratio of the highest sidelobe to the main lobe in the frequency domain, while ISL describes the proportion of sidelobe energy to total energy. The combination of the two can comprehensively reflect the sidelobe suppression performance of the window function.

[0007] The objective function is defined as: ; in, β The shape parameters of the Kaiser window determine the main lobe width and side lobe suppression capability of the window function; the weight parameters... α ∈[0,1], the priority of PSL and ISL in optimization can be adjusted according to the actual interference requirements.

[0008] S2: Constructing the mapping relationship for Kaiser optimization window functions Kaiser window β The parameters need to be adapted to the characteristics of the sampled signal, based on the duty cycle of the sampled signal. d and sampling period T j Build β The mapping relationship is expressed as: ; in β min and β max Preset β Lower and upper limits of values, p , q To adjust the factor, T 0 is the reference period. This mapping relationship enables: when the duty cycle... d Reduce or sample period T j When shortened, β As the value increases, the window function's ability to suppress spectral leakage is enhanced; when the duty cycle... d Increase or sampling period T j When extended, β The value tends to decrease, avoiding excessive widening of the main lobe.

[0009] S3: Constructing an adaptive Kaiser window function based on gradient descent

[0010] Due to the objective function J ( β Since no analytical closed-form solution exists, numerical optimization is performed using the gradient descent method, and the gradient is estimated using finite difference ... β Dynamic parameter updates. The gradient estimation formula is: ; in, d For small perturbation values ​​(such as d =1e-5), used to approximate the gradient direction of the objective function. Through iterative updates... β parameter until the objective function J ( β The convergence occurs, and the optimal value is obtained. β The value corresponds to the Kaiser window type.

[0011] S4: Energy Concentration Processing Based on Adaptive Kaiser Window Function Specifically, it includes the following sub-steps: S41: Construct the target SAR signal. Based on the SAR operating parameters (such as carrier frequency, frequency modulation slope, pulse repetition period, etc.), construct a radar echo signal model of the ground target. The signal is a linear frequency modulated pulse signal.

[0012] S42: Perform intermittent sampling processing on the target SAR signal. A rectangular envelope pulse train is used to intermittently sample the target SAR signal in the range and azimuth directions to obtain multiple discrete SAR interference signals.

[0013] S43: Obtain the objective function. Perform a Fourier transform on the sampled SAR interference signal to extract spectral information and determine the main lobe center position. f main and main lobe width W Calculate PSL and ISL indices and construct the objective function. J ( β ).

[0014] S44: Calculation β The adaptive adjustment information is combined with the duty cycle of the sampled signal. d Sampling period T j and the weighting factors of the objective function α Through the mapping relationship in step S2, we obtain β exist β min and β maxThe range of variation between them.

[0015] S45: Iteratively update the optimal window shape. Based on the range of change in step S44, iteratively update the β parameter using gradient descent until the objective function converges, thus obtaining the optimal Kaiser window shape.

[0016] S46: Energy Concentration Processing. The optimal Kaiser window shape is applied to the sampled SAR interference signal, and windowing is performed on the signal truncation points to achieve energy concentration of the false target signal.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) Excellent dynamic optimization capability. The Kaiser window is adjusted in real time using gradient descent. β The parameters can dynamically balance the main lobe width and side lobe suppression performance based on the duty cycle and period of the sampled signal, making it more adaptable than traditional fixed window functions (rectangular window, Hanning window, etc.).

[0018] (2) Alleviating performance degradation. It effectively solves the problems of increased sidelobes and severe spectral leakage of traditional fixed window functions under low duty cycles or short sampling periods, significantly improving the energy concentration of false targets and making it close to the energy distribution of real targets.

[0019] (3) The interference effect is significantly enhanced. The optimized interference signal has a lower proportion of sidelobe energy and a more concentrated main lobe energy, which can generate false targets with high similarity to real targets, greatly improving the deception interference effect on SAR and providing a new technical path for SAR interference technology. Attached Figure Description

[0020] Figure 1. Flowchart of the Kaiser window function optimization based on intermittent sampling signal; Figure 2. Schematic diagram of the SAR intermittent sampling interference energy concentration method based on adaptive Kaiser window function optimization; Figure 3. Comparison of interference signal energy concentration results between adaptive Kaiser window function and control window function, where: a) Comparison chart of window type changes under different duty cycles; b) Sampling period is T r Comparison of energy concentration at / 4; c) Sampling period is T r Comparison chart of energy concentration at / 8; d) Sampling period is T r Comparison chart of energy concentration at 16:00. Detailed Implementation

[0021] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, this invention provides a method for SAR intermittent sampling interference energy concentration based on adaptive Kasier window function optimization, wherein the specific steps of intermittent signal adaptive Kasier window function optimization include: Step S1: Construct the Kasier window parameter objective function by using the sidelobe performance index of the multi-segment false target signal of the intermittently sampled synthetic aperture radar (SAR) as a constraint.

[0024] The Kaiser window has adjustable parameters. β Thus, it becomes an ideal window function, and its expression is: ; in I 0(·) denotes the zeroth-order modified Bessel function. N The length of the window function. β The parameters determine the shape of the window function. When β When the window size is large, the sidelobe suppression effect of the window function is enhanced, but the main lobe width also increases accordingly; when β When the value is small, the main lobe is narrow, but the side lobe suppression capability is insufficient. In applications with intermittent sampling and forwarding interference, the sampled signal is only used within a certain duty cycle. d Valid within the period, and sampling period T j It also affects the time-domain continuity of the signal, so in practical design, it is necessary to dynamically adjust according to these two factors. β This allows the window function to suppress spectral leakage caused by sampling discontinuities while also preventing excessive diffusion of the main lobe energy.

[0025] Therefore, the following objective functions are constructed to describe the key indicators of the window function's sidelobe performance in the frequency domain: Peak Sidelobe Level (PSL) and Integral Sidelobe Energy Ratio (ISL). PSL is defined as: ; in Y ( k () represents the magnitude sequence of the window function after Fourier transform. k 0 corresponds to the center of the main lobe.

[0026] The sidelobe integral is a metric for measuring the energy of sidelobes in a signal's spectrum. Sidelobes refer to spectral components outside the main lobe; high sidelobes lead to spectral leakage and reduced interference effectiveness. Let the signal spectrum be... Its length is N Define the width of the main lobe as... W The range of the main lobe is ,in f main This is the location of the maximum value of the spectrum. (Side lobe integral) P It can be represented as: ; Total Energy It can be represented as: ; Percentage of sidelobe energy to total energy In other words, ISL can be represented as: ; To balance both aspects, construct the objective function: ; Among them, weight parameters α ∈[0,1], used to adjust the importance of PSL and ISL in optimization.

[0027] Step S2: Construct a mapping relationship based on the duty cycle and period of the sampled signal to obtain a Kasier optimized window function suitable for energy concentration of SAR false target signals.

[0028] Clearly, the objective function is about β The value of depends on the sidelobe characteristics of the window function after FFT, and these characteristics are related to the discontinuity of the sampled signal in the time domain. The duty cycle of intermittent sampling. d and cycle T j The ratio of the effective portion to the "gap" in the signal is determined: when the duty cycle is low or the sampling period is short, the signal discontinuity is more pronounced, easily causing spectral leakage and leading to increased sidelobes; conversely, when the duty cycle is high or the period is long, the signal is more continuous, and the sidelobe problem is relatively mild. Therefore, this paper can determine the optimal... β It can be considered as a function of duty cycle and sampling period, that is, a mapping relationship can be designed: ; in d This refers to the duty cycle of intermittent sampling. T j The period for intermittent sampling, β min and β max Preset β Lower and upper limits of values, p , q To adjust the factor,T 0 is the reference period. This formula can initially determine... β While assigning a reasonable initial value, it reflects the impact of decreasing duty cycle or shortening sampling period. β The value tends to be larger, thereby enhancing the window function's ability to suppress spectral leakage.

[0029] Step S3: Dynamically update the closed-form solution of the Kasier window function using numerical optimization methods to construct an adaptive Kasier window function based on gradient descent.

[0030] Because in practice, the objective function J ( β Often, there is no closed-form solution, therefore numerical optimization methods are needed to obtain the optimal solution. β This paper employs gradient descent, using finite difference estimation to update parameters. Specifically, let: ; in d This is a small perturbation value.

[0031] Step S4: Based on the adaptive Kasier window function, perform energy concentration at the truncation points of the sampled multi-segment SAR false target signals, as follows: Figure 2 As shown.

[0032] Step 1: Construct the target SAR signal.

[0033] The SAR transmits frequency-modulated pulse signals perpendicular to the direction of motion of the sensor and receives the reflected echo data. During the motion, it transmits and receives pulses at a fixed frequency. Let the multiple linear frequency-modulated pulse signals transmitted by the SAR be... s ( t r ),Right now: ; in, t Sampling time, t r For distance to fast time, n It is an integer. PRT The pulse repetition period, f 0 represents the carrier frequency. T p The pulse width. K r For the range-direction frequency modulation slope, rect( t / T p ) is a rectangle function. j Let be the imaginary unit. Then the radar echo signal of any single point target on the ground is: ; In the formula, s The radar cross section (RCS) is the area of ​​a point target. oh This represents the radar antenna pattern weighting coefficient for the target at that point; t n Indicates the launch number n The propagation delay between the target and the radar at each pulse point R n Here, λ is the slant range from the point target to the radar, and λ is the wavelength. In summary, the echo signal model for a single point target is: ; Generally, the speed of a SAR platform differs greatly from the speed of light, so we can assume that SAR follows a "stop-go-stop" pattern. Therefore, the one-dimensional echo signal can be stored in two-dimensional form, and then the carrier can be removed through orthogonal demodulation to obtain: ; In the formula, t a The direction is slowed down by time; t a for t a The function, T L For the time to synthesize the pore size, R ( t a )for t a The slant range from the target to the radar at a given time point. It can be viewed as a linear frequency modulated pulse signal in the azimuth direction.

[0034] Step 2: Perform intermittent sampling processing on the target SAR signal.

[0035] The significant advantage of intermittent sampling lies in its ability to avoid sampling the entire pulse when processing signals with long time spans, thereby effectively reducing forwarding delay. Assuming a range-oriented intermittently sampled pulse signal... and azimuth intermittent sampling pulse signal These are rectangular envelope pulse trains in the following forms: ; In the formula, The distance is the intermittent sampling pulse width. The width of the intermittent sampling pulse in the azimuth direction; For the distance-oriented intermittent sampling period, The azimuth intermittent sampling period is [period]. n r The distance is a multiple of the intermittent sampling period.n a This represents the multiple of the azimuth intermittent sampling period; * indicates convolution. When the jammer intercepts the SAR signal, the echo signal only travels half the propagation path. Therefore, the received signal of the jammer can be obtained as follows: ; The jammer's transmitted signal is: ; In equation (8), This represents the jammer's forwarding delay, which includes several components: the time required to sample the first short pulse, the system's own processing delay, and the signal propagation delay between the jammer and the target. Therefore, This can be viewed as the total delay between the relayed signal and the actual echo signal. The range-direction matched filter is set as follows: ; When the interference signal reaches the SAR receiving platform, it experiences another propagation delay. After frequency mixing and carrier removal, as shown in the RD algorithm flow, range pulse compression processing is performed first, resulting in: ; in, The range-direction intermittent sampling frequency, The distance-oriented intermittent sampling amplitude weighting coefficient, Let be the order of the distance to the pseudo-target. To compensate for the fast arrival time of the interference echo at the SAR receiver, where Slower time with direction The distance changes with the distance migration, and therefore distance migration correction is required. After correction, it is transformed into a time-slowing mode with respect to the azimuth. Let the irrelevant time constant be denoted as .but After distance migration correction, it can be transformed into: ; Let the azimuth matched filter be: ; in, K a The azimuth frequency modulation slope is given. After the signal is processed by azimuth matched filtering, it becomes: ; In the formula, f a For range frequency, f sa The range-direction intermittent sampling frequency, b naThis represents the amplitude weighting coefficient for intermittent sampling in the azimuth direction. As shown in the above formula, the amplitude of the false target is mainly affected by the amplitude weighting coefficient and the intermittent sampling period.

[0036] Step 3: Obtain the signal spectrum information from Step 2, and define the width of the main lobe as... W The range of the main lobe is ,in f main It is the location of the maximum value of the spectrum, obtained in the Kasier window. β The objective function with respect to the peak sidelobe level (PSL) and the sidelobe energy integral ratio (ISL) is constructed as in step S1.

[0037] Step 4: Based on the information such as the duty cycle and sampling period of the sampled signal in Step 2, and the weighting factors of the objective function in Step 3, calculate... β The relationship between the values ​​and the upper and lower limits is used to obtain the adaptive adjustment information of the Kasier window based on the sampled signal. The construction process is as described in step S2.

[0038] Step 5: Iteratively update the change relationship obtained in Step 4 using gradient descent to calculate the optimal Kasier window shape. The construction process is the same as in Step S3.

[0039] Step 6: Apply the optimal Kasier window obtained in Step 5 to the SAR interference signal obtained in Step 2, and analyze the effect of the adaptive Kasier window function on the concentration of SAR intermittent sampling interference energy.

[0040] The final result obtained after applying the adaptive Kaiser window to the intermittent sampling forwarding interference of SAR is as follows: ; From formula (25), we can further analyze the results of signal processing after windowing and the effect of windowing on indicators such as energy concentration. Since windowing is only performed in the time domain, the Doppler-related processing can be ignored in formula (25), and only the signal and its energy in the time dimension are analyzed; the result of Doppler-related signal processing is simplified to a function. D ( t a , f a Then formula (25) can be simplified to: ; In formula (26), exp(- j 2π f 0 t 0) represents the overall delay of radar signal reception, which has no effect on energy concentration; I ( fThe frequency domain transformation of the adaptive filter is given. Furthermore, during energy analysis, the relative amplitude and overall delay of the pulse echo do not affect the overall echo energy concentration analysis. Therefore, assuming the radar signal has no delay or amplitude terms, we only consider the first intermittent sampling transmission of a single pulse to analyze the signal energy concentration. Removing the relevant terms, we obtain the following formula: ; For the analysis of a single intermittent sampling forwarding signal, according to formula (27), the final result of time-domain pulse compression can be equivalent to adding a window function to the pulse compression for time-domain processing. Therefore, its energy concentration is only affected by the parameters of the pulse compression itself and the shape of the window function. When no processing is performed, the window function term in formula (27) does not exist, and the overall signal processing result is equivalent to processing the sinc function with a triangular window obtained by convolving a rectangular window. Its sidelobe suppression effect is equivalent to the formula in... item.

[0041] A comparative experiment was conducted by adding windows to the distance dimension of the interference signal using various window types, including rectangular window, Hanning window, Blackman window, and Gaussian window.

[0042] Table 1 SAR signal construction parameters ; To measure the difference in energy concentration before and after windowing, two indicators are used for evaluation: energy concentration and the aforementioned ISL.

[0043] Energy concentration is an indicator that measures the degree to which signal energy is concentrated within a specific region. High energy concentration means that signal energy is mainly concentrated within the target area, thereby improving the signal detection and recognition capabilities. The formula for calculating energy concentration is as follows: Let the signal matrix be E Its size is M × N Define a local region whose center is ( ). i 0, j 0), radius is r Energy in a local area It can be represented as: ; Total Energy It can be represented as: ; Energy Concentration CIt can be represented as: ; High energy concentration indicates that the signal energy is mainly concentrated within the target area, which helps improve the signal detection and recognition capabilities. Combining this with the previous description of ISL, low sidelobe integral indicates that there is less sidelobe energy in the signal spectrum, which helps improve the signal's spectral resolution and interference effectiveness. By calculating energy concentration and sidelobe integral, the signal quality and interference effect can be evaluated.

[0044] Table 2. Evaluation results of energy concentration.

[0045] ; To verify the effectiveness of the adaptive window function against intermittent sampling-forwarding interference under different conditions, the intermittent sampling period was selected as 1 / 4 of the transmitted pulse, and the duty cycle was set to 0.3, 0.5, and 0.7. The changes in the window shape under different duty cycles were observed, and the results were compared with those of the Hamming window under the same duty cycle. The results are as follows. Figure 3 As shown.

[0046] It can be seen that if the Hamming window is selected, the window shape is also determined when the intermittent sampling period is determined, and it does not change with the change of duty cycle; while if the adaptive window Kaiser window function is selected, it can be seen that the window function changes with the change of duty cycle. This change can adapt to the flexible changes of intermittent sampling forwarding interference in actual applications, thereby enhancing the effect of intermittent sampling forwarding interference.

[0047] To observe the effect of the adaptive Kaiser window function on intermittent sampling forwarding interference, the duty cycle of the intermittent sampling forwarding interference was selected from 0.1 to 1.0, and the sampling period was selected as T. r / 4,T r / 8,T r / 16, Similarly, energy concentration is chosen as the indicator for measurement, and the change graph is shown below. Figure 3 .

[0048] In summary, this invention optimizes intermittent sampling and forwarding interference by constructing a Kaiser window, designs an adaptive adjustment mechanism, and adjusts the signal in real time using the gradient descent method. The parameters dynamically balance the main lobe width and side lobe suppression performance based on the duty cycle and sampling period. Compared with traditional window types, it has superior dynamic optimization capabilities, effectively alleviating the performance degradation problem of traditional fixed window functions under low duty cycles or short sampling periods. This makes the energy of false targets generated by intermittent sampling more concentrated, approximating real targets and enhancing the deception and jamming effect on SAR. This invention provides new ideas and methods for future SAR jamming technology.

[0049] In addition to the above embodiments, the present invention also includes other embodiments. All technical solutions formed by equivalent transformation or equivalent substitution should fall within the protection scope of the claims of the present invention.

Claims

1. A signal sampling energy concentration method based on Kasier window optimization, characterized in that, Includes the following steps: S1: Using the sidelobe performance index of the SAR multi-segment false target signal after intermittent sampling as a constraint, construct the Kaiser window parameter objective function; S2: Based on the duty cycle and period of the sampled signal, a mapping relationship is constructed to obtain the Kaiser optimized window function suitable for the energy concentration of SAR false target signals; S3: Dynamically update the parameters of the Kaiser window function using numerical optimization methods to construct an adaptive Kaiser window function based on gradient descent; S4: Based on the adaptive Kaiser window function, perform energy concentration processing on the truncated parts of the sampled multi-segment SAR false target signals.

2. The signal sampling energy concentration method based on Kasier window optimization according to claim 1, characterized in that: In step S1, the sidelobe performance indicators include peak sidelobe level and sidelobe energy integral ratio, and the objective function is defined as: ； in, β The shape parameters and weight parameters of the Kaiser window. α ∈[0,1], used to adjust the importance of peak sidelobe level and sidelobe energy integral ratio in optimization.

3. The signal sampling energy concentration method based on Kasier window optimization according to claim 1, characterized in that: In step S2, the mapping relationship is expressed as follows: ； in, β min and β max They are respectively β The preset lower and upper limits of values, p , q To adjust the factor, d The duty cycle of the sampled signal. T j The sampling period is T 0 is the reference period.

4. The signal sampling energy concentration method based on Kasier window optimization according to claim 1, characterized in that: In step S3, the numerical optimization method is gradient descent, which updates parameters by estimating the gradient using finite difference, specifically as follows: ； in, δ For small perturbation values, ∇ J ( β ) is the objective function J ( β )about β The gradient.

5. The signal sampling energy concentration method based on Kasier window optimization according to claim 1, characterized in that: Step S4 specifically includes the following sub-steps: S41: Construct the target SAR signal, wherein the target SAR signal is the echo signal of the linear frequency modulated pulse signal transmitted by the SAR after being reflected by the ground target; S42: Perform intermittent sampling processing on the target SAR signal in the range and azimuth directions to obtain SAR interference signal; S43: Perform a Fourier transform on the SAR interference signal to obtain the spectral information, and define the main lobe width as... W The range of the main lobe is ( f main - W , f main + W ),in f main The objective function in step S1 is obtained based on the spectrum information, where the maximum value of the spectrum is located. S44: Based on the duty cycle and sampling period of the sampled signal in step S42, and the weighting factor of the objective function in step S43. α Calculate using the mapping relationship in step S2 β exist β min and β max By analyzing the relationship between these changes, we can obtain the adaptive adjustment information for the Kaiser window. S45: The change relationship is iteratively updated using the gradient descent method in step S3 to calculate the optimal Kaiser window type; S46: Apply the optimal Kaiser window shape to the SAR interference signal obtained in step S42 to achieve energy concentration of the false target signal.

6. The signal sampling energy concentration method based on Kasier window optimization according to claim 5, characterized in that: In step S41, the expression for the target SAR signal is: ； In the formula, σ The radar cross section of the point target. ω This represents the radar antenna pattern weighting coefficient for the target at that point. t a For direction, slow time, t r For distance to fast time, T p The pulse width. K r This represents the range-direction frequency modulation slope. j The imaginary unit; τ a for t a The function, T L For the time to synthesize the pore size, R ( t a )for t a The slant range from the target to the radar at a given time point. It can be viewed as a linear frequency modulated pulse signal in the azimuth direction.

7. The signal sampling energy concentration method based on Kasier window optimization according to claim 5, characterized in that: In step S42, the range-direction intermittent sampling pulse signal and the azimuth-direction intermittent sampling pulse signal used in the intermittent sampling processing are both rectangular envelope pulse trains, with the following expressions: ； in, The distance is the intermittent sampling pulse width. The width of the intermittent sampling pulse in the azimuth direction; For the distance-oriented intermittent sampling period, The azimuth interval sampling period; * indicates convolution. δ (・) is the impulse function. Indicates direction n One cycle, Indicates distance direction n One cycle.

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