Thz sar motion compensation method based on minimum tsallis entropy algorithm

Abstract

The present application relates to a terahertz SAR motion compensation method based on a minimum Tsallis entropy algorithm, and belongs to the technical field of radar.The present application aims to solve the problems of defocusing of terahertz SAR imaging results caused by the existence of platform high-frequency vibration errors in the process of terahertz SAR imaging, and the increasing complexity of parameter estimation of platform vibration errors with the increasing complexity of platform vibration.The specific steps are as follows: 1, obtaining echo signals after distance migration correction;2, obtaining SAR imaging results;3, setting a range of terahertz SAR vibration error parameters;4, obtaining a set of vibration error parameter estimates that minimize the Tsallis entropy of the SAR image after vibration error compensation;5, constructing a vibration error compensation function to obtain new compensated echo signals, and performing Fourier transform on the new compensated echo signals along the azimuth direction to obtain SAR imaging results.The present application belongs to the technical field of radar.

Description

Technical Field

[0001] This invention belongs to the field of radar technology and relates to terahertz SAR motion compensation based on the minimum Tsallis entropy algorithm. Background Technology

[0002] In terahertz SAR imaging, due to its short wavelength, the high-frequency vibration error of the platform can no longer be ignored, leading to severe defocusing in the terahertz SAR imaging results. The Tsallis entropy of an image is an important indicator describing the imaging results and can be used to describe the degree of defocusing in terahertz SAR imaging. Tsallis entropy can be considered a generalized version of Shannon entropy and contrast; introducing Tsallis entropy can better describe the terahertz SAR imaging system. The greater the degree of image defocusing, the larger the Tsallis entropy value; conversely, as the degree of image defocusing decreases, the image becomes clearer, and the Tsallis entropy value decreases. In terahertz SAR motion compensation, as the complexity of platform vibration increases, the dimensionality of vibration error parameter estimation also increases, significantly increasing the complexity of accurate vibration error estimation. Therefore, how to accurately and quickly compensate for high-frequency vibration errors of the platform during terahertz SAR imaging is a pressing issue. Summary of the Invention

[0003] The purpose of this invention is to solve the problems of defocusing of terahertz SAR imaging results caused by the presence of high-frequency vibration errors of the platform during existing terahertz SAR imaging processes, and the increasing complexity of parameter estimation of platform vibration errors as the complexity of platform vibration increases. Therefore, this invention proposes a terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm.

[0004] The specific steps of the terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm are as follows:

[0005] Step 1: First, perform range compression on the terahertz SAR echo data to obtain the range-compressed echo signal s1(n,m). Then, perform range migration correction on the range-compressed echo signal s1(n,m) to obtain the range-migration corrected echo signal.

[0006] Step 2: Compensate the echo signal after range migration correction to obtain the compensated echo signal s(n,m). Perform Fourier transform on the compensated echo signal along the azimuth direction to obtain the SAR imaging result.

[0007] Step 3: Set the range of terahertz SAR vibration error parameters;

[0008] Set the maximum number of iterations I and the initialization information for the number of nectar sources in the artificial bee colony algorithm;

[0009] Step 4: Optimize the minimum Tsallis entropy algorithm using the artificial bee colony algorithm, and calculate the Tsallis entropy of the SAR image after vibration error compensation in each iteration to obtain a set of vibration error parameter estimates that minimize the Tsallis entropy of the SAR image after vibration error compensation.

[0010] Step 5: Estimated values ​​of vibration error parameters Constructing a vibration error compensation function

[0011] Vibration error compensation function Multiply the compensated echo signal s(n,m) obtained in step two to obtain a new compensated echo signal. Perform a Fourier transform on the new compensated echo signal along the azimuth direction to obtain the focused SAR imaging result.

[0012] The beneficial effects of this invention are as follows:

[0013] This invention addresses vibration errors present in terahertz SAR imaging by employing a minimum Tsallis entropy algorithm for compensation. Tsallis entropy is a crucial indicator for evaluating the degree of defocusing in SAR images. When affected by high-frequency vibration errors from the terahertz SAR platform, the resulting SAR image is defocused, and the Tsallis entropy value is relatively high. As the focus of the SAR image increases, the Tsallis entropy value gradually decreases; therefore, when the Tsallis entropy reaches its minimum value, a focused SAR image can be obtained. The minimum Tsallis entropy algorithm can compensate for platform vibration errors that cause severe defocusing during terahertz SAR imaging. Furthermore, optimizing the minimum Tsallis entropy algorithm using an artificial bee colony algorithm improves the algorithm's running speed while maintaining the accuracy of vibration error parameter estimation, resulting in well-focused SAR imaging results. Attached Figure Description

[0014] Figure 1 This is a flowchart of the present invention;

[0015] Figure 2 A model diagram of a target with a single scattering point;

[0016] Figure 3 The image shows the imaging result of a single scattering point target before vibration error compensation under 10dB conditions.

[0017] Figure 4 The image shows the imaging result of a single scattering point target after vibration error compensation under 10dB conditions.

[0018] Figure 5 For multi-scattering point target models;

[0019] Figure 6 The image shows the imaging results of a multi-scattering point target before vibration error compensation under 10dB conditions.

[0020] Figure 7 The image shows the imaging results of a multi-scattering point target after vibration error compensation under 10dB conditions.

[0021] Figure 8 The image shows the imaging result of a single scattering point target before vibration error compensation under 0dB conditions.

[0022] Figure 9 The image shows the imaging result of a single scattering point target after vibration error compensation under 0dB conditions.

[0023] Figure 10 The image shows the imaging results of a multi-scattering point target before vibration error compensation under 0dB conditions;

[0024] Figure 11 The image shows the imaging results of a multi-scattering point target after vibration error compensation under 0dB conditions. Detailed Implementation

[0025] Specific Implementation Method 1: The specific steps of the terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm in this implementation method are as follows:

[0026] Step 1: First, perform range compression on the terahertz SAR echo data to obtain the range-compressed echo signal s1(n,m). Then, perform range migration correction on the range-compressed echo signal s1(n,m) to obtain the range-migration corrected echo signal.

[0027] Step 2: Compensate the echo signal after range migration correction to obtain the compensated echo signal s(n,m). Perform Fourier transform on the compensated echo signal along the azimuth direction to obtain the SAR imaging result.

[0028] Step 3: Set the range of terahertz SAR vibration error parameters;

[0029] Set the maximum number of iterations I and the number of nectar sources in the artificial bee colony algorithm, etc.

[0030] Step 4: Optimize the minimum Tsallis entropy algorithm using the artificial bee colony algorithm, and calculate the Tsallis entropy of the SAR image after vibration error compensation in each iteration to obtain a set of vibration error parameter estimates that minimize the Tsallis entropy of the SAR image after vibration error compensation.

[0031] Step 5: Estimated values ​​of vibration error parameters Constructing a vibration error compensation function

[0032] Vibration error compensation function Multiply the compensated echo signal s(n,m) obtained in step two to obtain a new compensated echo signal. Perform a Fourier transform on the new compensated echo signal along the azimuth direction to obtain the focused SAR imaging result.

[0033] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that: in step one, the terahertz SAR echo data is first subjected to range compression to obtain the range-compressed echo signal s1(n,m), and then the range-compressed echo signal s1(n,m) is subjected to range migration correction to obtain the range-corrected echo signal; the specific process is as follows:

[0034] Step 11: The high-frequency vibration error of the terahertz SAR platform is modeled as simple harmonic motion, expressed as: Δr =

[0035] p1 sin(2πp2m+p3)+...+p r-1 sin(2πp r m+p r+1 )+...+p R-2 sin(2πp R-1 m+p R ) (1)

[0037] Where m represents the discrete slow time, m = 0, 1, ..., M-1, and M is the number of discrete slow time points; p r Let R be the vibration error parameter of the r-th platform, where r = 1, 2, ..., R;

[0038] The high-frequency vibration is defined as the absolute value of the product of the vibration frequency and the terahertz SAR synthetic aperture time being greater than or equal to 1.

[0039] Steps 1 and 2: Assume there is a point target N in the imaging scene, and the coordinates of point target N are (x... N ,y N ,0);

[0040] Range compression processing is performed on the terahertz SAR echo signal containing high-frequency vibration errors of the platform to obtain the range-compressed echo signal, which is represented as:

[0041]

[0042]

[0043] Where, σ pLet B be the complex amplitude of the terahertz SAR echo signal after range compression, and let B be the bandwidth of the terahertz SAR signal; n be the discrete fast time, n = 0, 1, ..., N-1, where N is the number of discrete fast time points; R(m) be the distance from the antenna phase center to the point target; and C be the speed of light. s1(n,m) is the range-compressed terahertz SAR echo signal phase; s1(n,m) is the range-compressed echo signal; V is the platform velocity; λ is the terahertz SAR signal wavelength; R N Δr is the shortest slant range between the terahertz SAR platform and the point target; Δr is the high-frequency vibration error of the terahertz SAR platform; k a The frequency is the Doppler modulation frequency; j is the imaginary unit, j 2 =-1;

[0044] Range migration correction is performed on the echo signal s1(n,m) after range compression in equation (2) to obtain the echo signal after range migration correction, which is expressed as:

[0045]

[0046] Wherein, s2(n,m) is the echo signal after distance migration correction.

[0047] The other steps and parameters are the same as in Specific Implementation Method 1.

[0048] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that: in step two, the echo signal after range migration correction is compensated to obtain the compensated echo signal s(n,m). A Fourier transform is then performed on the compensated echo signal along the azimuth direction to obtain the SAR imaging result; the specific process is as follows:

[0049] Construct the compensation function H as shown in the following equation. r ;

[0050] H r =exp[-j(πk)] a m 2 (5)

[0051] Compensate the echo signal s2(n,m) in equation (4) along the azimuth direction to obtain the compensated echo signal s(n,m) as shown in the following equation;

[0052]

[0053] Where s(n,m) is the compensated echo signal, To compensate for the phase of the processed echo signal obtained in step one,

[0054] A SAR image is obtained by performing a Fourier transform on the compensated echo signal along the azimuth direction.

[0055]

[0056] Where h is the Doppler frequency index.

[0057] Other steps and parameters are the same as in specific implementation method one or two.

[0058] Specific Implementation Method Four: This implementation method differs from one of Specific Implementation Methods One to Three in that: in step three, the range of terahertz SAR vibration error parameters is set.

[0059] The specific process is as follows:

[0060] The upper limit of the range of vibration error parameters for terahertz SAR is U, and the lower limit is L;

[0061]

[0062] Where r = 1, 2, ..., R, R is the number of platform vibration error parameters, p_U r p_L represents the upper limit of the vibration error parameter range for the r-th platform. r This represents the lower limit of the vibration error parameter range for the r-th platform.

[0063] Vibration error parameters include the amplitude, frequency, and phase of the vibration.

[0064] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0065] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One through Four in that: in step four, the artificial bee colony algorithm is used to optimize the minimum Tsallis entropy algorithm, and the Tsallis entropy of the SAR image after vibration error compensation is calculated in each iteration to obtain a set of vibration error parameter estimates that minimize the Tsallis entropy of the SAR image after vibration error compensation.

[0066] The specific process is as follows:

[0067] Step 41:

[0068] Initialize the number of iterations i = 1; the artificial bee colony algorithm uses hired bees and observation bees to find the optimal solution within the range of terahertz SAR vibration error parameters set in step three, based on the minimum Tsallis entropy criterion, and at the same time uses scout bees to update the solution vector;

[0069] The solution vector obtained in the i-th iteration is used as the estimated value of the vibration error parameter estimated in the i-th iteration, which is represented as {p1_est}.i ,p2_est i ,...,p r _est i ,...,p R _est i}, using the vibration error parameter estimate {p1_est i ,p2_est i ,...,p r _est i ,...,p R _est i Construct the vibration error compensation function S r ;

[0070]

[0071]

[0072] in, p is the estimated vibration error value of the terahertz SAR platform. r _est i Let r be the estimated value of the vibration error parameter of the r-th platform obtained in the i-th iteration, where r = 1, 2, ..., R;

[0073] The vibration error compensation function S r Multiplying the compensated echo signal s(n,m) obtained in step two yields a new compensated echo signal. A Fourier transform is then performed on the new compensated echo signal along the azimuth direction to obtain the SAR image after vibration error compensation in the i-th iteration. The Tsallis entropy T of the SAR image after vibration error compensation in the i-th iteration is then calculated. i (n,h);

[0074] Step 42: Let i = i + 1, repeat Step 41 until the maximum number of iterations I is reached, obtain the Tsallis entropy of I vibration error compensated SAR images, and take the vibration error parameter estimate corresponding to the minimum Tsallis entropy value of the vibration error compensated SAR image.

[0075] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0076] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One through Five in that: in step four-one, the Tsallis entropy T of the SAR image after vibration error compensation in the i-th iteration is calculated. i (n,h); the expression is:

[0077]

[0078] Where q is any real number, and S is an intermediate variable. g i (n,h) is the SAR image after vibration error compensation in the i-th iteration.

[0079] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0080] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Methods One through Six in that the estimated value of the vibration error parameter in step four-two is... The expression is:

[0081]

[0082] Where, p r _est i This represents the estimated vibration error parameter value of the r-th platform obtained in the i-th iteration. Let R be the estimated value of the vibration error parameter of the r-th platform, where r = 1, 2, ..., R.

[0083] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0084] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods One through Seven in that: in step five, the vibration error parameter estimation value is used... Constructing a vibration error compensation function The specific process is as follows:

[0085] Vibration error compensation function The expression is:

[0086]

[0087]

[0088] in, The final estimated value of the terahertz SAR platform vibration error is obtained. Let R be the estimated value of the vibration error parameter of the r-th platform, where r = 1, 2, ..., R.

[0089] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0090] The beneficial effects of the present invention are verified using the following embodiments:

[0091] Example 1:

[0092] In this implementation example, a terahertz SAR simulation system containing platform vibration errors is constructed, and its system parameters are shown in the table below.

[0093] Table 1. Parameters of the Terahertz SAR Simulation System

[0094]

[0095] As shown in the table above, the simulated SAR system operates in the terahertz band and performs imaging under a signal-to-noise ratio of 10 dB. Example 1 describes imaging a single-point target; the target model is as follows. Figure 2 As shown. After obtaining terahertz SAR echo data containing high-frequency platform vibration errors, the minimum Tsallis entropy algorithm optimized by the artificial bee colony algorithm proposed in this invention is used to estimate the platform vibration errors. After iteration, the vibration error parameter estimates corresponding to the minimum Tsallis entropy are obtained, and a compensation function is constructed to estimate the vibration errors, finally obtaining a clearly visible point target imaging result. The imaging result before vibration error compensation is shown in the figure. Figure 3 As shown, the imaging results after vibration error compensation are as follows: Figure 4 As shown in Table 2, the Tsallis entropy before and after platform vibration error compensation is as follows. It can be seen that after vibration error compensation using the algorithm proposed in this invention, point targets can be clearly imaged, and the defocusing caused by platform vibration error is also suppressed.

[0096] Table 2. Tsallis entropy before and after compensation for single-point target imaging under a signal-to-noise ratio of 10dB.

[0097]

[0098] Example 2

[0099] In this implementation example, a terahertz SAR simulation system containing platform vibration error is constructed, and its system parameters are shown in Table 1.

[0100] Similarly, the simulation system in this embodiment operates in the terahertz band with a signal-to-noise ratio of 10 dB. In this embodiment, the terahertz SAR simulation system images multiple targets, and the multi-point target model is as follows: Figure 5 As shown. Based on the obtained terahertz SAR echo data, the SAR imaging results before vibration error compensation can be obtained, as shown. Figure 6 As shown, the result is severely defocused, and the targets in the scene are not clearly visible, which is due to the vibration error of the terahertz SAR platform. Therefore, the minimum Tsallis entropy algorithm optimized by the artificial bee colony algorithm proposed in this invention is used for compensation. By setting the initial parameters such as the range of terahertz SAR vibration error parameters, the maximum number of iterations, and the number of nectar sources, the vibration error is iteratively estimated. After the iteration, the vibration error parameter estimate that minimizes the Tsallis entropy of the SAR imaging result is selected, and a compensation function is constructed for compensation. Finally, a focused SAR image can be obtained, as shown. Figure 7As shown. According to Figure 6 and Figure 7 The comparison shows that, after compensation by the algorithm of this invention, the defocus along the azimuth direction caused by the vibration error of the terahertz SAR platform can be suppressed, and a clear SAR imaging result is finally obtained. The comparison results of the Tsallis entropy of the SAR images before and after compensation are shown in Table 3. As can be seen from Table 3, after platform vibration error compensation, the Tsallis entropy is reduced and the image clarity is improved.

[0101] Table 3. Tsallis entropy before and after compensation for multi-point target imaging under a signal-to-noise ratio of 10dB.

[0102]

[0103] Example 3

[0104] In this implementation example, a terahertz SAR simulation system containing platform vibration error is constructed, and its system parameters are shown in Table 1.

[0105] Similarly, the simulation system in this embodiment operates in the terahertz band with a signal-to-noise ratio of 0 dB. In this embodiment, the terahertz SAR simulation system images a single point target, and the single point target model is as follows: Figure 2 As shown. Figure 8 As shown, the imaging result before platform vibration error compensation is severely defocused along the azimuth direction. After estimating the error parameters using the platform high-frequency vibration error compensation algorithm proposed in this invention, a vibration error compensation function is constructed for compensation, resulting in the following... Figure 9 The imaging results are shown. (By...) Figure 8 , Figure 9 The comparison shows that after compensation using the algorithm proposed in this invention, defocusing along the azimuth direction is suppressed, and point targets are clearly visible. Table 4 shows the comparison results of Tsallis entropy of SAR images before and after compensation. It can be seen that the Tsallis entropy of the compensated image is reduced, and the image clarity is improved.

[0106] Table 4. Tsallis entropy before and after compensation for single-point target imaging under a signal-to-noise ratio of 0dB.

[0107]

[0108] Example 4

[0109] In this implementation example, a terahertz SAR simulation system containing platform vibration error is constructed, and its system parameters are shown in Table 1.

[0110] Similarly, the simulation system in this embodiment operates in the terahertz band with a signal-to-noise ratio of 0 dB. In this embodiment, the terahertz SAR simulation system images multiple targets, and the multi-point target model is as follows: Figure 5As shown. Due to the influence of high-frequency vibration errors of the terahertz SAR platform, the terahertz SAR imaging results before compensation are defocused, as shown. Figure 10 As shown. The minimum Tsallis entropy algorithm optimized by the artificial bee colony algorithm proposed in this invention is used to estimate vibration error parameters and compensate for the vibration error. The compensated SAR imaging results are shown below. Figure 11 As shown. Comparison Figure 10 and Figure 11 It is easy to see that after estimation by the algorithm proposed in this invention, the defocusing caused by vibration error is suppressed, and multiple targets are clearly visible. The Tsallis entropy of the images before and after compensation is shown in Table 5. It can be seen that as the Tsallis entropy decreases, the focus of the SAR image is also improved.

[0111] Table 5. Tsallis entropy before and after compensation for multi-point target imaging under a signal-to-noise ratio of 0dB.

[0112]

[0113] This invention may have other embodiments. Without departing from the spirit and essence of this invention, those skilled in the art can make various corresponding changes and modifications according to this invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm, characterized in that: The specific steps of the method are as follows: Step 1: Perform range compression on the terahertz SAR echo data to obtain the range-compressed echo signal. Then, the echo signal after distance compression processing Range migration correction is performed to obtain the range migration corrected echo signal; Step 2: Compensate the echo signal after distance migration correction to obtain the compensated echo signal. The Fourier transform of the compensated echo signal along the azimuth direction is performed to obtain the SAR imaging result; Step 3: Set the range of terahertz SAR vibration error parameters; Set the maximum number of iterations. And the initialization information for the number of nectar sources in the artificial bee colony algorithm; Step 4: Optimize the minimum Tsallis entropy algorithm using the artificial bee colony algorithm, and calculate the Tsallis entropy of the SAR image after vibration error compensation in each iteration to obtain a set of vibration error parameter estimates that minimize the Tsallis entropy of the SAR image after vibration error compensation. ; Step 5: Estimated values ​​of vibration error parameters Construct a vibration error compensation function ; Vibration error compensation function The compensated echo signal obtained in step two Multiply the two signals to obtain a new compensated echo signal. Perform a Fourier transform on the new compensated echo signal along the azimuth direction to obtain the focused SAR imaging result. In step four, the minimum Tsallis entropy algorithm is optimized using the artificial bee colony algorithm, and the Tsallis entropy of the SAR image after vibration error compensation is calculated in each iteration to obtain a set of vibration error parameter estimates that minimize the Tsallis entropy of the SAR image after vibration error compensation. ; The specific process is as follows: Step 41: Initialize the number of iterations The artificial bee colony algorithm, based on the minimum Tsallis entropy criterion, uses hired bees and observation bees to find the optimal solution within the range of terahertz SAR vibration error parameters set in step three, while using scout bees to update the solution vector. No. The solution vector obtained in the nth iteration is used as the... The estimated vibration error parameters obtained from the nth iteration, the nth The estimated vibration error parameters obtained from the next iteration are expressed as follows: Using vibration error parameter estimates Constructing a vibration error compensation function ; （9） （10） in, This is the estimated vibration error value for the terahertz SAR platform. For the first The result of the iteration is the first Estimated vibration error parameters for each platform ; Vibration error compensation function The compensated echo signal obtained in step two Multiply to obtain a new compensated echo signal. Perform a Fourier transform on the new compensated echo signal along the azimuth direction to obtain the first... SAR image after vibration error compensation in the next iteration; calculate the SAR image after the next iteration. Tsallis entropy of SAR image after next iteration vibration error compensation ; Step 42: Let Repeat step 41 until the maximum number of iterations is reached. ,get The Tsallis entropy of the SAR image after vibration error compensation is taken as the estimated value of the vibration error parameter corresponding to the minimum Tsallis entropy value of the SAR image after vibration error compensation. ; In step four, the calculation of the first Tsallis entropy of SAR image after next iteration vibration error compensation The expression is: （10） in, It is any real number. As an intermediate variable, , For the first SAR image after vibration error compensation in the second iteration; The vibration error parameter estimation value in step four-two The expression is: （11） in, For the first The result of the iteration is the first Estimated vibration error parameters for each platform For the first The final estimated values ​​of the vibration error parameters of each platform ; In step five, the vibration error parameter estimation value is used. Construct a vibration error compensation function The specific process is as follows: Vibration error compensation function The expression is: （12） （13） in, The final estimated value of the terahertz SAR platform vibration error is obtained. For the first The final estimated values ​​of the vibration error parameters of each platform .

2. The terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm according to claim 1, characterized in that: In step one, the terahertz SAR echo data is first subjected to range compression to obtain the range-compressed echo signal. Then, the echo signal after distance compression processing Range migration correction is performed to obtain the range-migration-corrected echo signal; the specific process is as follows: Step 11: The high-frequency vibration error of the terahertz SAR platform is modeled as simple harmonic motion, expressed as: （1） in, For discrete slow time, , The number of discrete slow time points; For the first Vibration error parameters of each platform ; The high-frequency vibration is defined as the absolute value of the product of the vibration frequency and the terahertz SAR synthetic aperture time being greater than or equal to 1. Steps 1 and 2: Assume there is a point target in the imaging scene. Target The coordinates are ; Range compression processing is performed on the terahertz SAR echo signal containing high-frequency vibration errors of the platform to obtain the range-compressed echo signal, which is represented as: （2） （3） in, The complex amplitude of the terahertz SAR echo signal after range compression. This refers to the bandwidth of the terahertz SAR signal. For discrete fast time, , The number of discrete fast time points; This is the distance from the antenna phase center to the point target. At the speed of light, The phase of the terahertz SAR echo signal after range compression; The echo signal is processed by distance compression. The platform's movement speed; The wavelength of the terahertz SAR signal; This represents the shortest slant distance between the terahertz SAR platform and the point target. This refers to the high-frequency vibration error of the terahertz SAR platform. To tune the frequency for Doppler; The imaginary unit, ; The echo signal after distance compression in equation (2) After performing range migration correction, the range-migration-corrected echo signal is obtained, which is represented as: （4） in, This is the echo signal after distance migration correction.

3. The terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm according to claim 2, characterized in that: In step two, the echo signal after distance migration correction is compensated to obtain the compensated echo signal. The SAR imaging results are obtained by performing a Fourier transform on the compensated echo signal along the azimuth direction; the specific process is as follows: Construct the compensation function as shown in the following equation. ; （5） Echo signal in equation (4) along the azimuth direction After compensation, the compensated echo signal is obtained as shown in the following formula. ; （6） in, To compensate for the back echo signal, To compensate for the phase of the processed echo signal obtained in step one, ; A SAR image is obtained by performing a Fourier transform on the compensated echo signal along the azimuth direction. （7） in, It is the Doppler frequency index.

4. The terahertz SAR motion compensation method based on the minimum Tsallis entropy algorithm according to claim 3, characterized in that: In step three, the range of terahertz SAR vibration error parameters is set. The specific process is as follows: The upper limit of the range of terahertz SAR vibration error parameters is The lower limit is ; （8） in, , The number of vibration error parameters of the platform. For the first The upper limit of the vibration error parameter range for each platform. For the first The lower limit of the vibration error parameter range for each platform.

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