Handheld high-resolution millimeter wave synthetic aperture radar imaging method

Abstract

The invention discloses a handheld high-resolution millimeter-wave synthetic aperture radar imaging method. The method comprises the following steps: mixing signals acquired by a handheld high-resolution millimeter-wave radar to obtain intermediate-frequency signals; performing fast Fourier transform processing and residual video phase removal on the intermediate frequency signal; calculating the frequency response corresponding to the distance between the phase center of the radar antenna and the pixel point through linear interpolation; obtaining a preliminary result according to backward projection imaging; calculating position error estimation according to a minimum entropy self-focusing algorithm; and track correction is carried out according to the position error estimation, and then backward projection imaging is carried out again. The minimum entropy self-focusing backward projection algorithm provided by the invention takes image entropy minimization as an optimization target, compensates the position error of the phase center of the radar antenna through a coordinate descent method, does not need to depend on an external sensor, and effectively improves the imaging definition, thereby achieving compact and efficient imaging. The invention discloses a light and independent handheld high-resolution millimeter wave synthetic aperture radar system.

Description

Technical Field

[0001] This invention belongs to the field of radar imaging technology, and particularly relates to a handheld high-resolution millimeter-wave synthetic aperture radar imaging method. Background Technology

[0002] Handheld high-resolution millimeter-wave synthetic aperture radar (HR-mmW-SAR) technology represents a major breakthrough in next-generation portable imaging systems. This technology enables penetrating, high-resolution imaging in complex environments and adverse weather conditions, demonstrating unique advantages in search and rescue, security inspections, and infrastructure assessments. Compared to traditional optical and infrared imaging technologies, HR-mmW-SAR has the following significant characteristics: 1) all-weather operation capability; 2) ability to penetrate non-metallic obstacles; and 3) a compact design for easy handheld operation.

[0003] Back-projection (BP) algorithms are ideal for handheld SAR systems due to their adaptability to arbitrary motion trajectories. However, the practical application of this technology faces two major challenges: First, handheld operation inevitably introduces platform position errors, leading to a significant decrease in image quality; second, existing compensation methods have obvious limitations. While inertial measurement unit (IMU)-based schemes offer high accuracy, they increase system complexity and cost, and struggle to capture subtle hand tremors. Traditional autofocus algorithms (such as phase gradient autofocus) rely on Fourier transform relationships, but the coherent integral characteristics of the BP algorithm prevent direct application of these methods. This technical bottleneck severely restricts the performance of HR-mmW-SAR in imaging applications.

[0004] With the rapid development of millimeter-wave hardware and signal processing technology, developing novel self-focusing methods suitable for the BP algorithm framework has become a current research hotspot. However, the following challenges remain: 1. Reliance on external sensors; 2. Difficulty in adapting to the unique mathematical characteristics of the BP algorithm; 3. Insufficient computational efficiency. Therefore, overcoming these challenges is a pressing issue to be addressed in this field. Summary of the Invention

[0005] Purpose of the Invention: The purpose of this invention is to provide a handheld high-resolution millimeter-wave synthetic aperture radar (MEA-BP) imaging method. This method solves the imaging blurring problem caused by radar position errors introduced by hand tremors.

[0006] Technical solution: The present invention provides a handheld high-resolution millimeter-wave synthetic aperture radar imaging method, comprising the following steps:

[0007] Step 1: Acquire radar signals using a handheld high-resolution millimeter-wave radar system, and mix the radar signals to obtain an intermediate frequency signal;

[0008] Step 2: Perform Fast Fourier Transform and residual video phase removal on the intermediate frequency signal to obtain the processed signal;

[0009] Step 3: Based on the processed signal, calculate the frequency response corresponding to the distance between the radar antenna phase center and the pixel point through linear interpolation;

[0010] Step 4: Based on the frequency response, multiply by the azimuth phase compensation according to the back projection imaging, and then perform coherent summation to obtain the image expression;

[0011] Step 5: Based on the image expression, according to the minimum entropy algorithm, the position error is updated by calculating the image entropy and selecting the minimum error to obtain the optimal error estimation vector;

[0012] Step 6: Correct the motion trajectory of the radar antenna phase center using the optimal error estimation vector, and re-execute the back projection imaging.

[0013] Furthermore, step 1 specifically involves: during radar data acquisition, the handheld high-resolution millimeter-wave radar system transmits along its trajectory for a duration of [duration missing]. The frequency-modulated continuous wave signal, after being reflected by the target, is received by the receiver and mixed with the transmitted signal to generate an intermediate frequency signal:

[0014]

[0015] In the formula, At the speed of light, The carrier wavelength of the signal. For carrier frequency, For continuous slow-time variables, For continuous fast-time variables, and It is the slope of the linear frequency modulated signal. B For signal bandwidth, R This represents the instantaneous slant distance.

[0016] Furthermore, step 2 specifically involves: processing the signal using Fast Fourier Transform and removing the residual video phase, resulting in the following:

[0017]

[0018] Furthermore, step 3 specifically involves calculating the distance between the radar antenna phase center and the pixel using linear interpolation. The corresponding frequency response is:

[0019]

[0020] In the formula, For discrete distance, and They represent and Local range compressed signal, and Indicates distance A continuous index of adjacent discrete distance points.

[0021] Furthermore, step 4 specifically involves multiplying the back projection image by the azimuth phase compensation. Then, perform coherent summation to obtain the image expression:

[0022]

[0023] In the formula, Indicates slow time Sampling as One point, The x-coordinate of the pixel is The ordinate of the pixel is I Represents a specific pixel in the target scene image. The coherent imaging intensity value is the basic unit that constitutes the final radar image.

[0024] Furthermore, in step 5, the minimum entropy algorithm is specifically as follows:

[0025]

[0026] In the formula, The information entropy representing the radar imaging results, These are the pixel values ​​after image normalization; Iterative optimization is performed using the coordinate descent method, updating only one parameter at a time while keeping other parameters unchanged. The iterative process is denoted as... .

[0027] Furthermore, in step 5, the step of calculating the image entropy and selecting the minimum error to update the position error to obtain the optimal error estimation vector specifically includes the following steps:

[0028] Step 5.1: Initialize the position error as follows: That is, it is assumed that the error of all trajectory points is zero at the beginning;

[0029] Step 5.2, in the... In this iteration, the current position error vector is For the nth trajectory point, by introducing an update quantity The updated error vector is represented as:

[0030] ;

[0031] In the formula, Indicates the first j In the nth iteration process, the radar trajectory on the 1st... N Position error of each trajectory point; Step 5.3, update the... Calculate the corresponding image entropy And find the one that minimizes image entropy. The optimization problem is described as follows:

[0032]

[0033] Step 5.4: Repeat steps 5.2 and 5.3 for all trajectory points, updating each element in the error vector one by one until the entire trajectory is updated. Through multiple iterations, the position error is gradually optimized, and finally the optimal error estimation vector is obtained. .

[0034] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0035] The present invention also discloses a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method of the present invention.

[0036] The present invention also discloses a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method of the present invention.

[0037] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0038] (1) This invention uses the minimum entropy self-focusing algorithm to achieve motion error compensation using only echo data, without the need for any external sensors, which significantly reduces hardware cost and size, making it more suitable for portable application scenarios.

[0039] (2) Based on the coherent integral characteristics of the BP algorithm, this invention optimizes the position error point by point by using the coordinate descent method, thereby minimizing the image entropy and realizing efficient autofocus under the BP framework, breaking through the limitations of traditional technology.

[0040] (3) Compared with the existing maximum image intensity back projection method and maximum image sharpness back projection method, the method of the present invention has a higher peak side lobe ratio and integral side lobe ratio.

[0041] (4) This invention can achieve high-resolution imaging by combining with commercial millimeter-wave radar, providing a feasible solution for low-cost portable SAR systems in fields such as search and rescue and security inspection. Attached Figure Description

[0042] Figure 1 This is a schematic diagram illustrating the workflow of a handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to an embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram illustrating the workflow of the minimum entropy self-focusing algorithm in a handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to an embodiment of the present invention.

[0044] Figure 3 The following is a comparison of simulation results of a handheld high-resolution millimeter-wave synthetic aperture radar imaging method with other methods in an embodiment of the present invention: (a) random position error, (b) BP without position error, (c) BP with position error, (d) MPA-BP, (e) MSA-BP, (f) MEA-BP.

[0045] Figure 4 Here are the azimuth profile and distance profile of the center point in the simulation results of the embodiments of the present invention: (a) azimuth profile, (b) distance profile;

[0046] Figure 5 The HR-mmW-SAR system and experimental scenario settings used in the experiment in the embodiments of the present invention are as follows: (a) HR-mmW-SAR system, (b) experimental scenario;

[0047] Figure 6 The following is a comparison of experimental results of a handheld high-resolution millimeter-wave synthetic aperture radar imaging method with other methods in an embodiment of the present invention: (a) BP without autofocus, (b) MPA-BP, (c) MSA-BP, (d) MEA-BP;

[0048] Figure 7 Here are the azimuth profile and range profile of target 1 in the experimental scenario in the embodiments of the present invention: (a) azimuth profile, (b) range profile;

[0049] Figure 8 Here are the azimuth profile and range profile of target 2 in the experimental scenario in the embodiment of the present invention: (a) azimuth profile, (b) range profile. Detailed Implementation

[0050] The technical solution of the present invention will be further described below with reference to the accompanying drawings. The present invention provides a handheld high-resolution millimeter-wave synthetic aperture radar imaging method, the entire workflow of which is as follows: Figure 1 As shown, the steps for imaging a handheld high-resolution millimeter-wave synthetic aperture radar are as follows:

[0051] Step 1: During radar data acquisition, the HR-mmW-SAR system transmits along its trajectory for a duration of [duration missing]. The frequency-modulated continuous wave signal, after being reflected by the target, is received by the receiver and mixed with the transmitted signal to generate an intermediate frequency signal:

[0052]

[0053] In the formula, At the speed of light, The carrier wavelength of the signal. For carrier frequency, For continuous slow-time variables, For continuous fast-time variables, and It is the slope of the linear frequency modulated signal. B For signal bandwidth, R This represents the instantaneous slant distance.

[0054] Step 2: After processing with Fast Fourier Transform and removing residual video phase, the baseband signal is:

[0055]

[0056] Step 3: Calculate the distance between the radar antenna phase center and the pixel using linear interpolation. The corresponding frequency response is:

[0057]

[0058] In the formula, For discrete distance, and They represent and The signal is compressed within a certain range.

[0059] Step 4: Multiply the BP imaging result by azimuth phase compensation. Then, perform coherent summation to obtain the image expression:

[0060]

[0061] In the formula, Indicates slow time Sampling as One point, The x-coordinate of the pixel is The vertical coordinate of the pixel is denoted as y.

[0062] Step 5: According to the minimum entropy algorithm, the position error is updated by calculating the image entropy and selecting the error with the minimum entropy. The entropy function of the BP image is:

[0063]

[0064] This invention employs a coordinate descent method for iterative optimization, updating only one parameter at a time while keeping other parameters unchanged. The iterative process is denoted as... .

[0065] The workflow of step 5 is as follows: Figure 2 As shown, it specifically includes:

[0066] Step 5.1: Initialize the position error as follows: That is, it is assumed that the error of all trajectory points is zero at the beginning.

[0067] Step 5.2, in the... In this iteration, the current position error vector is For the nth trajectory point, by introducing an update quantity The updated error vector is represented as:

[0068]

[0069] Step 5.3, update the... Calculate the corresponding image entropy And find the one that minimizes image entropy. The optimization problem is described as follows:

[0070]

[0071] Step 5.4: Repeat steps 5.2 and 5.3 for all trajectory points, updating each element in the error vector one by one until the entire trajectory has been updated. Through multiple iterations, the position error is gradually optimized, and finally the optimal error estimation vector is obtained. .

[0072] Step 6, Use The trajectory of the radar antenna phase center is corrected, and BP imaging is re-executed.

[0073] In a further embodiment, the main simulation parameters of the present invention in the simulation scenario are shown in Table 1.

[0074] Table 1

[0075]

[0076] Simulation results are as follows Figure 3 As shown, the generated random position error is as follows Figure 3 As shown in (a), the focusing results of simulation data for BP without positional error, BP with positional error, maximum intensity autofocus back-projection (MPA-BP), maximum sharpness autofocus back-projection (MSA-BP), and MEA-BP are as follows: Figure 3 As shown in (b)-(f) of the simulation results. The image entropy, sharpness, and intensity indices of the simulation results are shown in Table 2.

[0077] Table 2

[0078]

[0079] Figure 4 yes Figure 3 (b) A cross-sectional view of the center point in the middle circle. Figure 4 (a) in the diagram is an azimuth profile. Figure 4 (b) in the table is the distance profile. The dashed line represents the BP result without positional error, the dotted line represents the BP result with positional error, the dotted-dash line represents the MPA-BP result, the thin solid line represents the MSA-BP result, and the thick solid line represents the result of the method of this invention. The PSLR and ISLR of the center point are shown in Table 3, where the azimuth profile and the distance profile are distinguished by the prefixes A and R, respectively.

[0080] Table 3

[0081]

[0082] In a further embodiment, the present invention performs imaging in an experimental scenario, using an HR-mmW-SAR system and target settings in the scenario as follows. Figure 5 As shown, Figure 5 (a) in the figure represents the HR-mmW-SAR system. Figure 5 (b) in the table shows the target settings in the experimental scenario. The radar parameter settings are shown in Table 1.

[0083] Figure 6 This is a comparison chart of experimental results between the method of this invention and other methods. Figure 6 In Table 4, (a)-(d) show the results of BP without autofocus, MPA-BP, MSA-BP, and MEA-BP using the method of this invention, respectively. The image entropy, sharpness, and intensity indices of the experimental results are shown in Table 4.

[0084] Table 4

[0085]

[0086] Figure 7 for Figure 5 (b) shows the azimuth profile and range profile of target 1. Figure 7 (a) in the diagram is an azimuth profile. Figure 7 (b) in the diagram is a distance profile. Figure 8 for Figure 5 (b) shows the azimuth profile and range profile of target 2. Figure 8 (a) in the diagram is an azimuth profile. Figure 8 (b) in the figure represents the range profile. The dashed line represents the BP result without autofocus, the dotted line represents the MPA-BP result, the dashed-dot line represents the MSA-BP result, and the solid line represents the MEA-BP result. The PSLR and ISLR of the two targets are shown in Table 5.

[0087] Comparison of the results of this invention with other methods in simulated and experimental scenarios shows that the imaged target point features are clearly visible, while possessing the lowest image entropy and high sharpness. In terms of focusing performance, PSLR and ISLR show excellent results. In summary, this invention can effectively adapt to target position errors, suppress defocus and noise interference, and achieve high-quality motion compensation.

[0088] The specific implementation schemes described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific implementation schemes of the present invention and are not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention.

[0089] Table 5

[0090]

Claims

1. A handheld high-resolution millimeter-wave synthetic aperture radar imaging method, characterized in that, Includes the following steps: Step 1: Acquire radar signals using a handheld high-resolution millimeter-wave radar system, and mix the radar signals to obtain an intermediate frequency signal; Step 2: Perform Fast Fourier Transform and residual video phase removal on the intermediate frequency signal to obtain the processed signal; Step 3: Based on the processed signal, calculate the frequency response corresponding to the distance between the radar antenna phase center and the pixel point through linear interpolation; Step 4: Based on the frequency response, multiply by the azimuth phase compensation according to the back projection imaging, and then perform coherent summation to obtain the image expression; Step 5: Based on the image expression, according to the minimum entropy algorithm, the position error is updated by calculating the image entropy and selecting the minimum error to obtain the optimal error estimation vector; Step 6: Correct the motion trajectory of the radar antenna phase center using the optimal error estimation vector, and re-execute the back projection imaging.

2. The handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to claim 1, characterized in that, Step 1 specifically involves: During radar data acquisition, the handheld high-resolution millimeter-wave radar system transmits along its trajectory for a duration of [duration missing]. The frequency-modulated continuous wave signal, after being reflected by the target, is received by the receiver and mixed with the transmitted signal to generate an intermediate frequency signal: ； In the formula, At the speed of light, The carrier wavelength of the signal. For carrier frequency, For continuous slow-time variables, For continuous fast-time variables, and It is the slope of the linear frequency modulated signal. B For signal bandwidth, R This represents the instantaneous slant distance.

3. The handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to claim 2, characterized in that, Step 2 specifically involves: After Fast Fourier Transform processing and residual video phase removal, the processed signal is as follows: 。 4. The handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to claim 1, characterized in that, Step 3 specifically involves calculating the distance between the radar antenna phase center and the pixel using linear interpolation. The corresponding frequency response is: ； In the formula, For discrete distance, and They represent and Local range compressed signal, and Indicates distance A continuous index of adjacent discrete distance points.

5. The handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to claim 4, characterized in that, Step 4 specifically involves: Based on the back projection imaging, multiplied by the azimuth phase compensation Then, perform coherent summation to obtain the image expression: ； In the formula, Indicates slow time Sampling as One point, The x-coordinate of the pixel is The ordinate of the pixel is I Represents a specific pixel in the target scene image. The coherent imaging intensity value is the basic unit that constitutes the final radar image.

6. The handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to claim 5, characterized in that, In step 5, the minimum entropy algorithm is specifically as follows: ； In the formula, The information entropy representing the radar imaging results, These are the pixel values ​​after image normalization; Iterative optimization is performed using the coordinate descent method, updating only one parameter at a time while keeping other parameters unchanged. The iterative process is denoted as... .

7. The handheld high-resolution millimeter-wave synthetic aperture radar imaging method according to claim 6, characterized in that, In step 5, the step of calculating the image entropy and selecting the minimum error to update the position error to obtain the optimal error estimation vector specifically includes the following steps: Step 5.1: Initialize the position error as follows: That is, assume that the error of all trajectory points is zero at the beginning; Step 5.2, in the... In this iteration, the current position error vector is For the nth trajectory point, by introducing an update quantity The updated error vector is represented as: ； In the formula, Indicates the first j In the nth iteration process, the radar trajectory on the 1st... N Position error of each trajectory point; Step 5.3, update the... Calculate the corresponding image entropy And find the one that minimizes image entropy. The optimization problem is described as follows: ； Step 5.4: Repeat steps 5.2 and 5.3 for all trajectory points, updating each element in the error vector one by one until the entire trajectory is updated. Through multiple iterations, the position error is gradually optimized, and finally the optimal error estimation vector is obtained. .

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method of claim 1.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

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