Unmanned aerial vehicle ISAR imaging method based on improved PGCPF parameter estimation

Abstract

The invention discloses an unmanned aerial vehicle ISAR imaging method based on improved PGCPF parameter estimation, and relates to the technical field of inverse synthetic aperture radar imaging. The method comprises the following steps: performing distance compression, dechirp compensation and translation compensation on a received echo signal to obtain processed data; reconstructing the processed data through a weighted PGCPF optimization algorithm based on differential evolution to obtain a reconstructed signal; and finally, carrying out two-dimensional ISAR imaging on the reconstructed signal. According to the method, a weight factor is introduced into a PGCPF estimation multi-component linear frequency modulation (LFM) signal method, the precision of estimating coefficients of a secondary phase term and a primary phase term is improved, error accumulation of follow-up estimation is reduced, parameter selection in the estimation process is optimized through a differential evolution algorithm, ISAR image entropy serves as an evaluation function, and the estimation accuracy is improved. And an optimal parameter combination is found so as to obtain an optimal echo signal. And the echo signal is reconstructed by using the estimated multi-component linear frequency modulation signal, so that the imaging effect of the unmanned rotorcraft in non-stationary motion can be effectively improved, and the imaging quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of inverse synthetic aperture radar imaging, and in particular to an unmanned aerial vehicle ISAR imaging method based on improved PGCPF parameter estimation. Background Art

[0002] Inverse Synthetic Aperture Radar (ISAR) is a radar system for two-dimensional imaging of moving targets. It can obtain high-resolution images of targets in all weather conditions, all day and at long distances, and is widely used in the imaging of various aerospace targets. For targets such as drones, due to their own rotor components, the moving rotor components will produce a micro-Doppler effect superimposed on the radar echo of the drone, resulting in a broadening of the echo frequency band. In addition, although the movement speed of drones is relatively slow, their movement patterns are often non-stationary, resulting in the Doppler frequency of each scatterer echo signal being usually time-varying, and the radar echo phase term developing towards a higher-order form. This makes the ISAR images obtained by the traditional Range-Doppler (RD) algorithm defocused and smeared, making them difficult to identify.

[0003] For targets with non-stationary motion, the ISAR echo signal of the same distance unit can be modeled into a multi-component linear frequency modulation signal form. The present invention uses the product generalized cubic phase function (PGCPF) to estimate and extract this multi-component LFM (Linear Frequency Modulated, LFM) signal. However, since the PGCPF estimation method may produce error accumulation due to the insufficient accuracy of estimating the coefficients of high-order phase terms, this means that with subsequent estimates or with the addition of more data points, the initial small errors may gradually accumulate, resulting in a large deviation in the final estimation result. In order to reduce this effect, it is necessary to improve the estimation accuracy of PGCPF. In addition, the estimation accuracy is also affected by some variable parameters, and the selection of these parameters can directly affect the final estimation result and affect the final imaging quality. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the UAV ISAR imaging algorithm based on improved PGCPF parameter estimation provided by the present invention can improve the estimation accuracy of multi-component LFM signals, thereby effectively realizing high-resolution ISAR imaging of rotorcraft UAVs performing non-stationary motion.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a UAV ISAR imaging method based on improved PGCPF parameter estimation, the method comprising the following steps:

[0006] S1, performing distance compression, de-skew compensation and translation compensation on the received echo signal to obtain processed data;

[0007] S2, reconstructing the processed data through a weighted PGCPF optimization algorithm based on differential evolution to obtain a reconstructed signal;

[0008] S3. Perform two-dimensional ISAR imaging on the reconstructed signal.

[0009] Furthermore, the specific method of step S2 is as follows:

[0010] S2-1. The radar echo signal of the non-stationary rotor UAV target is modeled as a multi-component linear frequency modulation signal. The discrete form of the echo of the lth range unit is s l (n) are as follows:

[0011]

[0012] Among them, B k , a k,0 , a k,1 , a k,2 They represent the amplitude, initial phase, center frequency and frequency modulation of the kth scattering point respectively, K represents the total number of discrete points, and n(t) represents the micro-Doppler interference generated by the rotor, which is a sinusoidal frequency modulation signal attached to the echo signal, but the signal energy is less than the main echo energy;

[0013] S2-2. Calculate the multi-component LFM signal representing the echo. The definition expression of PGCPF is:

[0014]

[0015] s * (-n+m)s * (-nm)exp(-jβnm 2 )

[0016] Where LFM represents linear frequency modulation signal, PGCPF represents product-type generalized cubic phase function, m is the number of frequency domain sampling points of PGCPF, s(n) is the input echo signal, N is the length of the signal, β is the frequency index, L is the time offset number, s * (n) represents the conjugate echo signal; initialization parameters, let l = 1, k = 1;

[0017] S2-3, introduce weight factors w1 and w2, and estimate the quadratic phase term coefficient according to the following formula

[0018]

[0019] where β1 = argmax β |PGCPF(w1β)|

[0020] β2=argmax β |PGCPF(w1(β2-m / 2)+w2β)|

[0021] S2-4. Compensate for the secondary phase term and calculate the primary phase term coefficient

[0022] S2-5. Compensate the phase term again and calculate the amplitude of the LFM signal component and the initial phase coefficient

[0023] S2-6, subtracting the LFM signal component from the original echo data, setting k=k+1, returning to S2-3, estimating the next LFM signal component, until k reaches the total number of scattering points, and obtaining the multi-component LFM signal of the distance unit;

[0024] S2-7, set l=l+1, return to S2-3, until the multi-component LFM signal estimation of all range cells is completed, and use the estimated signal to reconstruct the echo signal;

[0025] S2-8, introduce differential evolution algorithm to optimize weight factors w1, w2 and frequency domain resolution m of PGCPF;

[0026] Assume that the population size is NP, and the population space L of the Gth iteration is G as follows:

[0027]

[0028] X i,G represents the set of individuals in the population, Represent the population individuals about w1, w2 and m respectively;

[0029] S2-9. Randomly select 3 individual sets X from the population r1,G ,X r2,G ,X r3,G Perform differential processing and set F v is the mutation factor, then the mutation individual set V i,G for:

[0030] V i,G =X r1,G +F v (X r2,G -X r3,G )

[0031] S2-10. The individuals in the population and the mutant individuals are crossed with probability CR to obtain the experimental individuals. in:

[0032]

[0033] Among them, rand j (0,1) means j takes a random number between 0 and 1;

[0034] S2-11. Bring the current individual and the test individual into the inverse synthetic aperture radar echo to calculate the image entropy value, and select the better one as the next generation individual:

[0035]

[0036] Where E(·) represents the function for calculating the Shannon entropy of the image;

[0037] S2-12, return to step S2-3, and continue to reconstruct the echo signal with the optimized parameters;

[0038] S2-13, until the number of iterations G reaches the maximum value, or the algorithm ends when the convergence accuracy is met, and the inverse synthetic aperture radar image with optimal entropy is obtained.

[0039] Furthermore, the specific methods of steps 2-4 are:

[0040]

[0041] in,

[0042]

[0043] Furthermore, the specific method of steps 2-5 is:

[0044]

[0045] The beneficial effects of the present invention are as follows: the UAV ISAR imaging algorithm based on improved PGCPF parameter estimation can obtain good imaging effects for rotor UAVs performing non-stationary motion, can effectively remove micro-Doppler interference generated by the rotor near the target signal, improve target recognition, and is beneficial to high-resolution imaging of UAV inverse synthetic aperture radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is the specific flow chart of the algorithm proposed in this paper;

[0047] Figure 2 A simulation model of a target rotary-wing UAV;

[0048] Figure 3 This is the imaging result of fast minimum entropy RD;

[0049] Figure 4This is the imaging result of the PGCPF estimation method.

[0050] Figure 5 This is an imaging result diagram of the algorithm provided by the present invention. DETAILED DESCRIPTION

[0051] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0052] like Figure 1 As shown, an ISAR imaging algorithm for UAV based on improved PGCPF parameter estimation includes the following steps:

[0053] S1, performing distance compression, de-skew compensation and translation compensation on the received echo signal to obtain processed data;

[0054] S2, reconstructing the processed data through a weighted PGCPF optimization algorithm based on differential evolution to obtain a reconstructed signal;

[0055] S3. Perform two-dimensional ISAR imaging on the reconstructed signal.

[0056] The sub-steps of step S2 are as follows:

[0057] S2-1. The radar echo signal of the target UAV performing non-stationary motion is modeled as a multi-component linear frequency modulation signal. The discrete form of the echo of the lth range unit is as follows:

[0058]

[0059] Among them B k , a k,0 , a k,1 , a k,2 They represent the amplitude, initial phase, center frequency and modulation frequency of the kth scattering point respectively. n(t) represents the micro-Doppler interference generated by the rotor, which is a sinusoidal frequency-modulated signal attached to the echo signal, but the signal energy is less than the main echo energy.

[0060] S2-2. Use the product-type generalized cubic phase function to estimate the multi-component LFM signal representing the echo. The definition expression of PGCPF is:

[0061]

[0062] s * (-n+m)s* (-nm)exp(-jβnm 2 )

[0063] Where m is the number of frequency domain sampling points of PGCPF, s(n) is the input echo signal, N is the length of the signal, and β is the frequency index. Initialize the parameters and set l=1, k=1.

[0064] S2-3, introduce weight factors w1 and w2, calculate PGCPF respectively, and estimate the quadratic phase term coefficient according to the following formula

[0065] β1=argmax β |PGCPF(w1β)|

[0066] β2=argmax β |PGCPF(w1(β2-m / 2)+w2β)|

[0067]

[0068] S2-4, compensate the secondary phase term, and estimate the primary phase term coefficient according to the following formula:

[0069]

[0070] S2-5. Compensate the phase term again and estimate the amplitude according to the following formula: and the initial phase coefficient

[0071]

[0072] S2-6, subtracting the LFM signal component from the original echo data, setting k=k+1, returning to S2-3, estimating the next LFM signal component, until k reaches the total number of scattering points, and obtaining the multi-component LFM signal of the distance unit;

[0073] S2-7, set l=l+1, return to S2-3, until the multi-component LFM signal estimation of all range cells is completed, and use the estimated signal to reconstruct the echo signal.

[0074] S2-8. Introduce the differential evolution algorithm to optimize the weight factors w1, w2 and the frequency domain resolution m of PGCPF. Assume that the population size is NP. The population space of the Gth iteration is as follows:

[0075]

[0076] S2-9. Randomly select 3 individuals X from the population r1,G ,X r2,G ,Xr3,G Perform differential processing and set F v is the mutation factor, then the mutation individual is:

[0077] V i,G =X r1,G +F v (X r2,G -X r3,G )

[0078] S2-10. The individuals in the population and the mutant individuals are crossed with probability CR to obtain the experimental individuals. in:

[0079]

[0080] S2-11. Bring the current individual and the test individual into the ISAR echo to calculate the image entropy value, and select the better one as the next generation individual:

[0081]

[0082] S2-12. Return to step S2-3 and continue to reconstruct the echo signal with the optimized parameters.

[0083] S2-13, until the number of iterations G reaches the maximum value, or the algorithm ends when the convergence accuracy is met, and the ISAR image with the optimal entropy is obtained.

[0084] In one embodiment of the present invention, in order to verify the performance of the proposed algorithm, we conducted a Matlab simulation experiment under the following conditions: the center frequency of the FM signal is 35 GHz, the bandwidth is 10 GHz, and the pulse repetition frequency is 833 Hz. Figure 2 The nine points in the middle of the simulation model represent the main body of the drone, the four points represent the center of each rotor, and the four points around represent the two rotor blades. The initial speed of the drone is 5m / s and the acceleration is 0.5m / s 2 , the rotor rotation speed is 40πrad / s. The differential evolution algorithm parameters are set as: NP=15, F v =0.4, CR = 0.5. Under the same simulation conditions, the target model was simulated using fast minimum entropy RD imaging, PGCPF estimation method imaging, and UAV ISAR imaging algorithm based on improved PGCPF parameter estimation.

[0085] from Figure 3 It can be seen from the imaging results that the fast minimum entropy RD imaging method will produce blurred stripes for the rotor UAV with non-stationary motion. This is because the rotor generates micro-Doppler interference, and most of the scattering points also have defocusing problems due to non-stationary motion. Figure 4 and Figure 5The imaging results of the PGCPF estimation algorithm and the algorithm proposed in the present invention are as follows. Since the ISAR echo signal is reconstructed, there is no blur band caused by micro-Doppler interference, and the focusing effect of the scattering points is also better. However, since the estimation accuracy of the traditional PGCPF is not as good as the algorithm of the present invention, there are still some noise points caused by micro-Doppler interference in the image, and the focusing effect of the scattering points is also poor.

[0086] Table 1 is a comparison of the results of different algorithms under the same simulation conditions. Image entropy is used to measure the quality of imaging results.

[0087] algorithm Fast minimum entropy algorithm PGCPF estimation algorithm Algorithm of the present invention Entropy 9.2050 5.4492 4.6384

[0088] From the data in Table 1, it can be seen that the imaging result of the algorithm proposed in the present invention is about 0.8108 lower in image entropy than the imaging result of the traditional PGCPF estimation method. When the image entropy is smaller, the focusing effect is better and the image quality is better. Therefore, the imaging effect of the algorithm of the present invention is better than that of the other two algorithms, and can well reduce the defocusing problem of the non-stationary motion of the drone and the influence of the micro-Doppler effect generated by the rotor.

[0089] In summary, the UAV ISAR imaging algorithm based on the improved PGCPF parameter estimation in the present invention can effectively realize high-resolution ISAR imaging of rotorcraft UAVs performing non-stationary motion, and the estimation accuracy is also higher than that of the traditional PGCPF estimation method, and the image focusing effect is better.

Claims

1. A UAV ISAR imaging method based on improved PGCPF parameter estimation, the method comprising the following steps: S1, performing distance compression, de-skew compensation and translation compensation on the received echo signal to obtain processed data; S2, reconstructing the processed data through a weighted PGCPF optimization algorithm based on differential evolution to obtain a reconstructed signal; S3. Perform two-dimensional ISAR imaging on the reconstructed signal.

2. The UAV ISAR imaging method based on improved PGCPF parameter estimation according to claim 1, characterized in that: The specific method of step S2 is as follows: S2-1. The radar echo signal of the non-stationary rotor UAV target is modeled as a multi-component linear frequency modulation signal. The discrete form of the echo of the lth range unit is s l (n) are as follows: Among them, B k , a k,0 , a k,1 , a k,2 They represent the amplitude, initial phase, center frequency and frequency modulation of the kth scattering point respectively, K represents the total number of discrete points, and n(t) represents the micro-Doppler interference generated by the rotor, which is a sinusoidal frequency modulation signal attached to the echo signal, but the signal energy is less than the main echo energy; S2-2. Calculate the multi-component LFM signal representing the echo. The definition expression of PGCPF is: Where LFM represents linear frequency modulation signal, PGCPF represents product-type generalized cubic phase function, m is the number of frequency domain sampling points of PGCPF, s(n) is the input echo signal, N is the length of the signal, β is the frequency index, L is the time offset number, s * (n) represents the conjugate echo signal; initialization parameters, let l = 1, k = 1; S2-3, introduce weight factors w1 and w2, and estimate the quadratic phase term coefficient according to the following formula where β1 = argmax β |PGCPF(w1β)| β2=argmax β |PGCPF(w1(β2-m / 2)+w2β)| S2-4. Compensate for the secondary phase term and calculate the primary phase term coefficient S2-5. Compensate the phase term again and calculate the amplitude of the LFM signal component and the initial phase coefficient S2-6, subtracting the LFM signal component from the original echo data, setting k=k+1, returning to S2-3, estimating the next LFM signal component, until k reaches the total number of scattering points, and obtaining the multi-component LFM signal of the distance unit; S2-7, set l=l+1, return to S2-3, until the multi-component LFM signal estimation of all range cells is completed, and use the estimated signal to reconstruct the echo signal; S2-8, introduce differential evolution algorithm to optimize weight factors w1, w2 and frequency domain resolution m of PGCPF; Assume that the population size is NP, and the population space L of the Gth iteration is G as follows: X i,G represents the set of individuals in the population, Represent the population individuals about w1, w2 and m respectively; S2-9. Randomly select 3 individual sets X from the population r1,G ,X r2,G ,X r3,G Perform differential processing and set F v is the mutation factor, then the mutation individual set V i,G for: V i,G =X r1,G +F v (X r2,G -X r3,G ) S2-10. The individuals in the population and the mutant individuals are crossed with probability CR to obtain the experimental individuals. in: Among them, rand j (0,1) means j takes a random number between 0 and 1; S2-11. Bring the current individual and the test individual into the inverse synthetic aperture radar echo to calculate the image entropy value, and select the better one as the next generation individual: Where E(·) represents the function for calculating the Shannon entropy of the image; S2-12, return to step S2-3, and continue to reconstruct the echo signal with the optimized parameters; S2-13, until the number of iterations G reaches the maximum value, or the algorithm ends when the convergence accuracy is met, and the inverse synthetic aperture radar image with optimal entropy is obtained.

3. The UAV ISAR imaging method based on improved PGCPF parameter estimation as claimed in claim 2, characterized in that: The specific methods for steps 2-4 are: in, 4. The UAV ISAR imaging method based on improved PGCPF parameter estimation as claimed in claim 2, characterized in that: The specific method of steps 2-5 is:

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