An unmanned aerial vehicle ISAR instantaneous imaging method based on differential evolution optimization SPWVD

Abstract

The application discloses a kind of based on differential evolution optimization SPWVD's unmanned plane ISAR instantaneous imaging method, it includes the following steps: the received echo signal is carried out distance compression, envelope alignment and phase compensation and obtains the data after processing;The data after processing is replaced by the fast Fourier transform (FFT) in the azimuth direction in distance Doppler algorithm by the time-frequency analysis method such as smooth pseudo-winer distribution (SPWVD), and the initial distance-instantaneous Doppler imaging is obtained, and then the two smooth windows of SPWVD are optimized by differential evolution (DE) algorithm, and imaging again.The inverse synthetic aperture radar algorithm of the present application is improved by introducing the optimization process of differential evolution algorithm SPWVD, and the imaging quality of the mobile target inverse synthetic aperture radar can be effectively improved, so that the target imaging result is clearer, and the entropy value of the imaging diagram is smaller.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar imaging, and particularly relates to a UAV instantaneous ISAR imaging method based on differential evolution optimized SPWVD. BACKGROUND

[0002] In order to adapt to the ISAR imaging requirements of complex maneuvering targets, in recent years, in view of the defocusing and smearing problems of the traditional RD imaging algorithm in processing non-uniform rotating targets, researchers have proposed various RID imaging algorithms. These methods aim to improve the imaging quality of maneuvering targets, especially in the ISAR imaging application of high-speed, variable acceleration vehicles such as UAVs.

[0003] The traditional RD imaging algorithm relies on Fourier transform, which assumes that the target performs uniform or approximately uniform rotation. However, in actual operating environments, the motion pattern of the UAV is often complex and unpredictable, which leads to the change of Doppler frequency in the echo signal over time, and further affects the quality of imaging. In order to solve this problem, two main types of RID imaging algorithms are proposed: parameterized method and non-parametric time-frequency analysis method. The former estimates the instantaneous Doppler frequency by estimating the scatterer parameters, but its computational cost is high and is easily affected by model mismatch; the latter uses techniques such as STFT, WVD and its improved version such as SPWVD to provide more flexible time-frequency representation, but these methods either have insufficient resolution or have cross-term interference problems, which affect the clarity of the final ISAR image. SUMMARY

[0004] In view of the existing technical problems, the present application provides a UAV instantaneous ISAR imaging method based on differential evolution optimized SPWVD, which can obtain better imaging effect when imaging non-stationary moving targets.

[0005] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows:

[0006] A UAV ISAR instantaneous imaging method based on differential evolution optimized SPWVD is provided, which comprises the following steps:

[0007] S1, compressing the received echo signal in distance, aligning the envelope and compensating the phase to obtain the processed data;

[0008] S2, using the improved range Doppler algorithm to the data obtained in step 1 imaging, the improved range Doppler algorithm in which the SPWVD time-frequency analysis method instead of fast Fourier transform in the azimuth direction;

[0009] S3, by difference optimization method to optimize the two smooth windows in SPWVD and obtain the optimization results;

[0010] S4, using the optimized smooth window to the signal again imaging.

[0011] Further, the time-frequency analysis method of SPWVD of step 2 is:

[0012]

[0013] Wherein, SPWVD s (t,f) represents the smooth pseudo-wigner distribution, t represents time, f represents frequency, And The function is the instantaneous correlation function of the signal s(t), h(τ) and g(u) are the smooth window, the window in SPWVD is optimized by using difference optimization algorithm.

[0014] Further, the substep of step S3 is as follows:

[0015] S3-1, initialization parameters, and randomly generate a window population, set the space population number as NP, the solution space L of the smooth window h(τ) and g(u) after the Gth iteration G :

[0016]

[0017] Wherein, X i,G represents the population individual, represents the ith individual in the Gth iteration corresponding to h(τ), represents the ith individual in the Gth iteration corresponding to g(u), and G represents the iteration number;

[0018] S3-2, randomly select three individuals X r1,G , X r2,G , X r3,G from the current window population and perform difference processing, set the mutation factor as F v , then obtain the mutant individual V i,G ;

[0019] S3-3, each individual selected from the population and the mutant individual are crossed with a probability CR, and the required individual for experiment is obtained Wherein:

[0020]

[0021] wherein rand j (0,1) represents a random number between 0 and 1, j rand is a random component, ensuring that at least one dimension of the crossbred experimental individual is provided by the variation individual;

[0022] S3-4, the current individual and the experimental individual are respectively brought into the cost function calculation, and the one with smaller entropy value is selected as the next generation individual, wherein the entropy value function E(I) is:

[0023]

[0024] wherein ln represents a natural logarithm, I(i,j) represents a two-dimensional image matrix, and |·| 2 represents a square sum thereof;

[0025] The cost function f(ρ) is:

[0026] f(ρ)=E(I)

[0027] wherein ρ represents an independent variable in the cost function, and I represents a two-dimensional image matrix;

[0028] The process of selecting the next generation individual is as follows:

[0029]

[0030] S3-5, when the iteration number G max or the population optimal solution reaches a predetermined error precision, the process is ended and the optimal result is output.

[0031] Further, the calculation method of the variation individual in step 3-2 is:

[0032] V i,G =X r1,G +F v (X r2,G -X r3,G )

[0033] wherein F v () represents a variation coefficient in the variation process.

[0034] The unmanned aerial vehicle instantaneous inverse synthetic aperture radar imaging method based on differential evolution optimization SPWVD has the advantages that the method can optimize a smoothing window, reduce the influence of the smoothing window on the time-frequency analysis effect, and thus improve the ISAR imaging quality. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a specific flowchart of the unmanned aerial vehicle instantaneous inverse synthetic aperture radar imaging method based on differential evolution optimization SPWVD.

[0036] Figure 2 A model diagram used in the simulation experiment;

[0037] Figure 3 An imaging result diagram of the STFT-RID algorithm;

[0038] Figure 4 An imaging result diagram of the SPWVD-RID algorithm;

[0039] Figure 5 An imaging result diagram of the algorithm provided by the application. DETAILED DESCRIPTION

[0040] The specific embodiments of the application are described below to facilitate the understanding of the application by those skilled in the art, but it should be clear that the application is not limited to the scope of the specific embodiments, and for those skilled in the art, as long as various changes are within the spirit and scope of the application defined and determined by the appended claims, all applications utilizing the concept of the application are included in the protection.

[0041] The application provides an unmanned aerial vehicle ISAR instantaneous imaging method based on differential evolution optimization SPWVD. The method not only inherits the ability of SPWVD to suppress cross terms, but also further optimizes the design of the smoothing window by introducing a differential evolution algorithm, thereby minimizing the influence of cross terms while maintaining high resolution. The goal of this study is to explore a method that can effectively reduce cross term interference while maintaining or even improving ISAR image resolution, providing a new solution for realizing high-quality unmanned aerial vehicle ISAR instantaneous imaging.

[0042] As shown in Figure 1 A method for unmanned aerial vehicle instantaneous inverse synthetic aperture radar imaging based on differential evolution optimization SPWVD includes the following steps:

[0043] S1, compress the received echo signal in distance, align the envelope and compensate the phase to obtain the processed data;

[0044] S2, the processed data is analyzed by the time-frequency analysis method of SPWVD instead of the FFT in the azimuth direction of the range-doppler algorithm to obtain the SPWVD-RID preliminary imaging;

[0045] S3, the two smoothing windows in SPWVD are optimized by a differential optimization algorithm to obtain the optimization result;

[0046] S4, the signal is imaged again using the optimized smoothing window.

[0047] Further, the sub-steps of step S3 are as follows:

[0048] S3-1, the processed data is analyzed by SPWVD time-frequency analysis method instead of fast Fourier transform (FFT) in the azimuth direction:

[0049]

[0050] Wherein And The function is the instantaneous correlation function of the signal s(t), and h(τ) and g(u) are smoothing windows. The differential optimization algorithm is used to optimize the window in SPWVD.

[0051] S3-2, initialize parameters, and randomly generate a window population Set the population size as NP, and the solution space after the Gth iteration of the smoothing windows h(τ) and g(u) is represented as follows:

[0052]

[0053] S3-3, randomly select three individuals X r1,G , X r2,G , X r3,G from the current window population and perform differential processing Set the mutation factor as F v , then the mutated individual can be obtained:

[0054] V i,G = X r1,G + F v (X r2,G -X r3,G )

[0055] S3-4, in order to increase the diversity of the above population, each individual selected from the population is crossed with the mutated individual with a probability CR to obtain the experimental individual Wherein:

[0056]

[0057] j rand is a random component, which ensures that at least one dimension of the experimental individual after crossing is provided by the mutated individual.

[0058] S3-5, the current individual and the experimental individual are respectively brought into the cost function calculation, and the one with smaller entropy value and better effect is selected as the next generation individual, wherein the entropy value function is:

[0059]

[0060] Therefore, the cost function is converted to:

[0061] f(ρ)=E(I)

[0062] The process of selecting the next generation individual is as follows:

[0063]

[0064] S3-6, the iteration number G is reached max , or the population optimal solution reaches a predetermined error precision, the algorithm ends and the optimal result is output.

[0065] In an embodiment of the application, the target model is simulated by Matlab under the same simulation conditions using the STFT-RID algorithm, the SPWVD-RID algorithm, and the UAV instantaneous inverse synthetic aperture radar imaging method based on differential evolution optimization SPWVD.

[0066] The following simulation conditions are set: the center frequency of the frequency modulation signal is 35GHz, and the bandwidth is 10GHz.

[0067] The simulation target is a quadrotor UAV based on target scattering point simulation, and the rotors are spinning motion, and the wings are variable speed motion with the UAV main body, and the STFT-RID algorithm (corresponding Figure 3 ), the SPWVD-RID algorithm (corresponding Figure 4 ) and the SPWVD-distance-instantaneous Doppler (RID) inverse synthetic aperture radar imaging algorithm improved by the differential evolution algorithm (DE) (corresponding Figure 5 ) are used for simulation. The imaging result of the STFT-RID algorithm is shown in Figure 3 , the time-frequency analysis method of short-time Fourier transform in STFT-RID is easily affected, and is greatly affected by noise, and the imaging is blurred. The imaging result of the SPWVD-RID algorithm is shown in Figure 4 , due to the influence of the window function on the time-frequency analysis effect, the imaging is relatively blurred. The imaging result of the SPWVD-distance-instantaneous Doppler (RID) algorithm improved by the differential evolution algorithm (DE) is shown in Figure 5 , since the differential evolution algorithm is added to optimize the smoothing window, the contour is more clear than the imaging results of the first two methods, and the imaging is clearer.

[0068] Table 1 is a comparison of the results of different algorithms under the same simulation conditions. The image entropy is used to measure the quality of the imaging result.

[0069] Table 1

[0070]

[0071] As can be seen from the data in Table 1, the entropy value of the imaging result of the SPWVD-distance-instantaneous Doppler (RID) algorithm improved based on the differential evolution algorithm (DE) is lower than that of the other two methods. When the image entropy is smaller, the image quality is better. Therefore, we can infer that the imaging effect of the SPWVD-distance-instantaneous Doppler (RID) imaging algorithm improved based on the differential evolution algorithm (DE) is better than that of the other two methods under the same test environment, and the influence of noise can be well reduced, so that better imaging effect can be obtained.

[0072] In conclusion, the SPWVD-distance-instantaneous Doppler (RID) imaging algorithm improved based on the differential evolution algorithm (DE) reduces the influence of the smoothing window on the imaging effect of the time-frequency analysis method, and improves the imaging quality.

Claims

1. A method for ISAR instantaneous imaging of a UAV based on differential evolution optimization of SPWVD, the method comprising the following steps: S1, performing range compression, envelope alignment and phase compensation on the received echo signal to obtain processed data; S2, imaging the data obtained in step 1 using an improved range Doppler algorithm, wherein a time-frequency analysis method of SPWVD is used instead of fast Fourier transform in the azimuth direction in the improved range Doppler algorithm; S3, optimizing the two smoothing windows in SPWVD by a differential optimization method to obtain an optimized result; S3-1, initialize parameters, and randomly generate a window population, set the number of spatial populations to NP, and set the solution space L after the Gth iteration of the smoothing windows h(τ) and g(u) G is: wherein X i,G represents a population individual, represents the ith individual in the Gth iteration corresponding to h(τ), represents the ith individual in the Gth iteration corresponding to g(u), and G represents the number of iterations; S3-2, randomly select 3 individuals X from the current window population r1,G , X r2,G , X r3,G and perform differential processing, set mutation factor F v , then get the mutated individual V i,G ; S3-3, each individual selected from the population is crossed with the mutated individual with a probability CR to obtain the required individuals for the experiment wherein: where rand j (0,1) represents a random number between 0 and 1, j rand is a random component, ensuring that at least one dimension of the crossed experimental individual is provided by the mutated individual; S3-4, bringing the current individual and the experimental individual into the cost function calculation respectively, and selecting the one with a smaller entropy value as the next generation individual, wherein the entropy value function E(I) is: where ln denotes a natural logarithm, I(m, n) denotes a two-dimensional image matrix, |·| 2 denotes a sum of squares thereof; The cost function f(ρ) is: f(ρ)=E(I) wherein ρ represents an independent variable in the cost function, and I represents a two-dimensional image matrix; The process of selecting the next generation individual is as follows: S3-5, reaching the iteration number G max , or the population optimal solution reaches the predetermined error precision, and the optimal result is output. S4, using the optimized smoothing window to image the signal again.

2. The method of claim 1, wherein the method is based on differential evolution optimization SPWVD for ISAR instantaneous imaging of UAV. The time-frequency analysis method of SPWVD in step S2 is: where SPWVD s (t, f) denotes the smoothed pseudo Wigner-Ville distribution, t denotes time, and f denotes frequency, and The function is the instantaneous correlation function of the signal s(t), h(τ) and g(u) are smoothing windows, and a differential optimization algorithm is used to optimize the windows in the SPWVD.

3. The method of claim 1, wherein the method is based on differential evolution optimization SPWVD for ISAR instantaneous imaging of UAVs. The calculation method of the mutant individual in step S3-2 is: V i,G = X r1,G + F v (X r2,G - X r3,G ) where F v (·) indicates the coefficient of variation in the variation process.

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