Vehicle-mounted SAR (Synthetic Aperture Radar) self-focusing imaging method based on slant range history and target position estimation

Abstract

The invention provides a vehicle-mounted SAR (Synthetic Aperture Radar) self-focusing imaging method based on joint estimation of a slant range course and a target position. Firstly, based on vehicle-mounted SAR echo data, Doppler information is acquired, and the average speed of a vehicle platform is estimated; according to Doppler information imaging, obtaining a defocused two-dimensional imaging result; and selecting a strong scattering point in the two-dimensional imaging result as a target, and estimating the slant range of the target. According to the slope distance process and the average speed of the vehicle platform, a distance-Doppler positioning method is adopted to obtain rough estimation of the target position; and establishing a joint estimation cost function including a slant range course, target position coarse estimation and vehicle platform position estimation, and performing joint estimation on a vehicle platform track and a local target position to obtain a vehicle platform track. And carrying out two-dimensional imaging by using the vehicle platform track to obtain a focused vehicle-mounted SAR image. According to the echo signal received by the radar, the motion trail of the platform is inverted and self-focusing imaging is realized without depending on a high-precision inertial navigation system.

Description

Technical Field

[0001] The invention relates to the technical field of radar signal processing, and in particular to a vehicle-mounted SAR self-focusing imaging method based on joint estimation of slant range history and target position. Background Art

[0002] Vehicle-mounted synthetic aperture radar (SAR) can be used in areas such as road surface small target detection. The millimeter-wave frequency band synthetic aperture radar is installed on the vehicle to perform two-dimensional high-resolution imaging of the front, side or front oblique strips while the vehicle is moving, and the constant false alarm rate (CFAR) detection technology is used to detect small targets on the imaging results.

[0003] Traditional airborne SAR systems use high-precision integrated navigation and other equipment to obtain the yaw, pitch, roll angle and speed information of the moving platform, and use this information to calculate the Doppler center of the airborne SAR echo signal, and then perform two-dimensional high-resolution imaging. Due to the influence of comprehensive factors such as high-frequency bumps of the vehicle, instability of the direction and speed of travel during the movement of the vehicle-borne SAR, the speed and attitude information obtained by the low data rate (usually 5Hz) high-precision integrated navigation equipment has large errors, which affects the imaging effect of the vehicle-borne SAR, specifically manifested as imaging defocus caused by Doppler estimation errors. The use of high-data rate (greater than 100Hz) high-precision integrated navigation equipment can alleviate such problems, but the equipment cost is relatively high. Summary of the invention

[0004] In view of this, the present invention provides a vehicle-mounted SAR self-focusing imaging method based on the joint estimation of slant range history and target position, which can solve the Doppler center estimation problem of vehicle-mounted SAR in the two-dimensional imaging process. The method inverts the platform motion trajectory and realizes self-focusing imaging based on the echo signal received by the radar. This process does not need to rely on a high-precision inertial navigation system, simplifies the vehicle-mounted SAR system architecture, and thus can expand its application range.

[0005] In order to solve the above technical problems, the present invention is implemented as follows.

[0006] A vehicle-mounted SAR self-focusing imaging method based on joint estimation of slant range history and target position, comprising:

[0007] Step 1: Based on the echo data of the vehicle-mounted SAR, obtain Doppler information and estimate the average speed of the vehicle platform

[0008] Step 2: Rough estimation of slant range history: Based on the Doppler information imaging, a defocused two-dimensional imaging result f1 is obtained; a strong scattering point in the two-dimensional imaging result f1 is selected as the target, and the slant range history of the target is estimated.

[0009] Step 3: Rough estimate of target position: based on the target's slant range history The Doppler information and the average speed of the vehicle platform Use the range-Doppler positioning method to obtain a rough estimate of the target position

[0010] Step 4: Slope range history-target position joint precise estimation: Establish a slope range history The cost function for the joint estimation of the rough target position and the vehicle platform position is used to jointly estimate the vehicle platform trajectory and the local target position using the gradient descent method. The cost function represents the difference between the rough estimate of the traversed target position and the actual target position. When the difference is the smallest, it is the solution of the cost function, and then the vehicle platform track is obtained.

[0011] Step 5: Based on the echo data and the vehicle platform track obtained in step 4, two-dimensional imaging processing is performed to obtain a focused vehicle-mounted SAR image.

[0012] Preferably, in step 1, the average speed of the vehicle platform estimated based on the echo data of the vehicle-mounted SAR is:

[0013] The time domain correlation method is used to estimate the Doppler frequency center frequency of the vehicle-borne SAR echo data, the visual misalignment MD method is used to estimate the azimuth modulation rate, and the average speed of the vehicle platform is calculated based on the Doppler center frequency and the azimuth modulation rate.

[0014] Preferably, in step 2, when selecting the strong scattering point in the two-dimensional imaging result f1 as the target, the peak value in each distance unit is found, the signal-to-noise ratio of all peak values ​​is calculated, and the peak value with a set proportion of high signal-to-noise ratio is selected as the strong scattering point in each distance unit.

[0015] Preferably, in step 3, a rough estimate of the target position is obtained by:

[0016] Step 301: Based on the slope distance history Calculate the relative slant angle θ between the center of the synthetic aperture and the target position i ;

[0017] Step 302: Based on the average speed of the vehicle platform The relative oblique angle of the target θ i Determine the target's azimuth angle f relative to the radar a , by the target echo delay t d Determine the distance between the target and the radar

[0018]

[0019] Where c is the speed of light and λ is the signal wavelength;

[0020] Step 303: Using the azimuth angle f of the target relative to the radar a and distance Determine a rough estimate of the target location

[0021] Preferably, in step 4, the cost function established is:

[0022]

[0023] Where N is the number of strong scattering points; P APC (t a ) is the phase centre position of the radar antenna on the vehicle platform, i.e. the vehicle platform position; is the position of the i-th strong scattering point;

[0024] The gradient descent method is used to solve the above optimization problem. The gradient of the cost function of the vehicle platform's track and target position can be expressed as:

[0025]

[0026] In the formula, Respectively represent the x-axis track, y-axis track, z-axis track of the vehicle platform, and the gradient of the cost function of the target position;

[0027] Each iteration calculates the cost function and its gradient obtained by (I) and (II), and updates the three-axis trajectory and target position according to the gradient descent principle to obtain the most accurate trajectory and target position estimate of the vehicle platform under the cost function.

[0028] Preferably, in step 5, the two-dimensional imaging processing adopts the range-Doppler RD algorithm to perform the two-dimensional imaging processing; or the vehicle platform track is projected into the grid and the radar imaging back projection BP algorithm is used to perform the imaging processing.

[0029] Beneficial effects:

[0030] (1) This method estimates the motion error based on the echo of the vehicle-mounted SAR, and uses the two steps of coarse estimation and fine estimation to invert the precise motion trajectory to achieve self-focusing of 2D SAR imaging. The application of this algorithm can significantly improve the quality of 2D SAR imaging without an inertial navigation system (INS) or with a low navigation system accuracy and data rate.

[0031] (2) For vehicle-mounted systems, since the slant range and slant angle of the vehicle platform itself change relatively little and the angle range is relatively fixed, the slant range history and target position are used for joint estimation. The algorithm traverses a limited interval and is suitable for application scenarios such as vehicle-mounted SAR.

[0032] (3) This process does not require reliance on a high-precision inertial navigation system, which simplifies the architecture of the vehicle-mounted SAR system and thus expands its scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a physical picture of the vehicle-mounted SAR system.

[0034] Figure 2 Schematic diagram of the observation configuration for the imaging experiment.

[0035] Figure 3 This is a comparison chart of the imaging results before and after the self-focusing angle reversal.

[0036] Figure 4 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0038] When the vehicle-mounted SAR system is moving, it is affected by comprehensive factors such as the high-frequency bumps of the vehicle platform, the instability of the direction and speed of travel, etc., which generates high-frequency motion errors and Doppler parameter errors of the SAR signal, which in turn affects the SAR imaging quality and easily causes defocusing. For the vehicle-mounted system, since the slant range and slant angle of the vehicle platform itself change relatively little and the angle range is relatively fixed, the slant range history and the target position are used for joint estimation. The algorithm traverses a limited interval and is suitable for application scenarios such as vehicle-mounted SAR. Therefore, the present invention estimates the Doppler parameters and motion errors and compensates the SAR signal based on the vehicle trajectory information jointly estimated by the slant range history and the target position, which can significantly improve the imaging quality.

[0039] The geometric configuration of vehicle-mounted SAR imaging is in the right-hand system, with the platform moving in the x-axis direction and the vertical upward direction in the z-axis direction. Assume that point P is a point on the building with coordinates (x0, y0, z0). The radar transmits a linear frequency modulation signal. The instantaneous slant range between the radar and the target is R(t a ,x0,y0,z0). After distance compression, the echo signal can be expressed in the two-dimensional time domain as

[0040]

[0041] Where, t a is the slow time (i.e., the accumulation time of the azimuth echo signal), t ris the fast time (i.e., the time for the distance to the echo signal to accumulate), A0 is the echo signal amplitude, c is the speed of light, and B r is the signal bandwidth and λ is the signal wavelength.

[0042] Based on the above echo signal model, see Figure 4 The vehicle-mounted SAR self-focusing imaging method of the present invention comprises the following steps:

[0043] Step 1: Based on the echo data of the vehicle-mounted SAR, obtain Doppler information and estimate the average speed of the vehicle platform

[0044] The Doppler center frequency f of the echo dc 、Azimuth modulation frequency f dr The relationship with the vehicle platform movement speed V is as follows:

[0045]

[0046] In the formula, θ r,c is the oblique angle of the imaging scene center, R(t a ) is the instantaneous slant distance of the known imaging scene center.

[0047] This step then includes the following sub-steps:

[0048] Step 101: Obtain Doppler information from the echo: Doppler center frequency f dc 、Azimuth modulation frequency f dr ;

[0049] In this step, the Doppler center frequency f can be obtained by the time domain correlation method. dc The estimated value of the azimuth modulation frequency f is obtained through the MD (MapDrift, visual dislocation) algorithm dr Estimated value.

[0050] Step 102: Obtain the Doppler center frequency f dc 、Azimuth modulation frequency f dr Substitute into the following formula (3) to calculate the rough estimate of the vehicle platform movement speed:

[0051]

[0052] Step 2: Rough estimate of slant range history.

[0053] In this step, the Doppler information obtained in step 1 is imaged, and the two-dimensional imaging result f1 is a defocused image. The strong scattering point in the two-dimensional imaging result f1 is selected as the target, and the slant range of the target is estimated.

[0054] In this step, phase estimation is required based on the strong scattering points in each area of ​​the image. The peaks in each distance unit can be found and the signal-to-noise ratio of all peaks can be calculated. A certain proportion of peaks with high signal-to-noise ratios are selected in each distance unit as the strong scattering points to be estimated. Therefore, some strong scattering points are selected in each distance unit so that the selected strong scattering points should be distributed as evenly as possible in different distance segments of the image to ensure the accuracy of the estimation. These strong scattering points are used as targets.

[0055] After selecting the strong scattering point, the existing phase estimation method can be used to estimate the phase of the i-th target strong scattering point. Substitute into the following formula (4) to calculate the estimated value of the slope range history: The relationship between the two is:

[0056]

[0057] Step 3: Rough estimate of target position: Based on the target's slant range history Average speed of vehicle platform The range-Doppler positioning method is used to locate the target strong scattering point based on the correspondence between the range-Doppler domain and the point target position, and a rough estimate of the target position is obtained.

[0058] This step is specifically implemented using the following scheme:

[0059] Step 301: Based on the slope distance history Calculate the relative slant angle θ between the center of the synthetic aperture and the target position i .

[0060]

[0061] Where h is the height of the radar antenna phase center from the ground.

[0062] Step 302: According to formula (5), the average speed of the vehicle platform The relative oblique angle of the target θ i Determine the target's azimuth angle f relative to the radar a ; Delayed by target echo t d Determine the distance between the target and the radar

[0063]

[0064] in, is the average speed of the vehicle platform obtained in step 1.

[0065] Step 303: Using the azimuth angle f of the target relative to the radar a and distance Determine a rough estimate of the target location

[0066] Step 4: Joint precise estimation of slant range and target position.

[0067] This step calculates the slope range history of the local target (the relationship between slant range and azimuth time) and rough estimate of target position The optimization method is used to obtain the accurate trajectory. In this process, the target position is roughly estimated. The residual error will lead to deviations in trajectory estimation, so it is necessary to jointly estimate the trajectory and local target position. This estimation problem is an optimization problem to minimize the cost function, that is, the cost function represents the difference between the traversed rough target position and the actual target position. The solution of the cost function is when the difference is minimized. To simplify the derivation process, the cost function of this optimization process is the antenna phase center P in the vehicle platform. APC (t a ) and the rough target position The distance between the local target and the slant range history The difference between the two targets is obtained by summing up the cost function expressions of each target, that is,

[0068]

[0069] Where N is the number of strong scattering points; P APC (t a ) is the phase center position of the radar antenna on the vehicle platform, that is, the three-dimensional coordinates of the vehicle platform, is the coordinate estimate of the i-th target strong scattering point, is the slant range history of the i-th target estimated by the previous step.

[0070] The gradient descent method is a method for finding extreme values. It searches for the local minimum of a function by iteratively searching for points at a specified step distance in the opposite direction of the gradient corresponding to the current point on the function.

[0071] In the present invention, the gradient descent method is used to solve the optimization problem. The gradient of the cost function of the three-axis trajectory and the target position can be expressed as

[0072]

[0073] In the formula, They represent the gradients of the cost functions of the x-axis track, y-axis track, z-axis track, and target position respectively.

[0074] Each iteration calculates the cost function and its gradient obtained by (6) and (7), and updates the three-axis trajectory of the vehicle platform and the target position according to the gradient descent principle to obtain the most accurate vehicle platform trajectory and target position estimation under the cost function, thereby realizing trajectory estimation that does not rely on high-precision navigation equipment.

[0075] Step 5: Use the vehicle platform trajectory to perform self-focusing two-dimensional SAR imaging.

[0076] This step performs two-dimensional imaging processing based on the echo data and the vehicle platform track estimated in step 4 to obtain a focused vehicle-mounted SAR image. Two-dimensional imaging processing can be performed by frequency domain imaging algorithms such as RD (range-Doppler) algorithm to obtain a well-focused image. Another strategy is to project the precise track into a grid and use BP (radar imaging back projection) algorithm for imaging processing.

[0077] Implementation Cases

[0078] In this experiment, SAR was installed on a tracked unmanned vehicle. Figure 1 The main parameters of the experiment are shown in Table 1

[0079] Table 1 Vehicle SAR experimental parameters

[0080]

[0081]

[0082] According to the parameters in Table 1, the synthetic aperture time is about 4.8s. The unmanned vehicle moves along the x-axis at a speed of 0.6m / s, and moves about 5m in total. The two-dimensional resolution is about 0.2m. In the experiment, the single-channel data of the Multiple Input Multiple Output (MIMO) radar is used to verify the algorithm.

[0083] The ideal trajectory generated by the estimated velocity and the estimated precise trajectory are used for BP imaging, which is equivalent to self-focusing the image. The observation geometry of the imaging experiment is as follows: Figure 2 shown. Figure 3 The following is a comparison of the imaging results of the angle reflection before and after self-focusing. The comparison shows that the image quality is significantly improved after self-focusing.

[0084] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A vehicle-mounted SAR self-focusing imaging method based on joint estimation of slant range history and target position, characterized in that: include: Step 1: Based on the echo data of the vehicle-mounted SAR, obtain Doppler information and estimate the average speed of the vehicle platform Step 2: Rough estimation of slant range history: Based on the Doppler information imaging, a defocused two-dimensional imaging result f1 is obtained; a strong scattering point in the two-dimensional imaging result f1 is selected as the target, and the slant range history of the target is estimated. Step 3: Rough estimate of target position: based on the target's slant range history The Doppler information and the average speed of the vehicle platform Use the range-Doppler positioning method to obtain a rough estimate of the target position Step 4: Slope range history-target position joint precise estimation: Establish a slope range history The cost function for the joint estimation of the rough target position and the vehicle platform position is used to jointly estimate the vehicle platform trajectory and the local target position using the gradient descent method. The cost function represents the difference between the rough estimate of the traversed target position and the actual target position. When the difference is the smallest, it is the solution of the cost function, and then the vehicle platform track is obtained. Step 5: Based on the echo data and the vehicle platform track obtained in step 4, two-dimensional imaging processing is performed to obtain a focused vehicle-mounted SAR image.

2. The method according to claim 1, characterized in that In step 1, the average speed of the vehicle platform estimated based on the echo data of the vehicle-mounted SAR is: The time domain correlation method is used to estimate the Doppler frequency center frequency of the vehicle-borne SAR echo data, the visual misalignment MD method is used to estimate the azimuth modulation rate, and the average speed of the vehicle platform is calculated based on the Doppler center frequency and the azimuth modulation rate.

3. The method according to claim 1, characterized in that In step 2, when the strong scattering point in the two-dimensional imaging result f1 is selected as the target, the peak value in each distance unit is found, the signal-to-noise ratio of all peak values ​​is calculated, and the peak value with a set proportion of high signal-to-noise ratio is selected as the strong scattering point in each distance unit.

4. The method according to claim 1, characterized in that In step 3, the method of obtaining a rough estimate of the target position is: Step 301: Based on the slope distance history Calculate the relative slant angle θ between the center of the synthetic aperture and the target position i ; Step 302: Based on the average speed of the vehicle platform The relative oblique angle of the target θ i Determine the target's azimuth angle f relative to the radar a , by the target echo delay t d Determine the distance between the target and the radar Where c is the speed of light and λ is the signal wavelength; Step 303: Using the azimuth angle f of the target relative to the radar a and distance Determine a rough estimate of the target location 5. The method according to claim 1, characterized in that In step 4, the cost function established is: Where N is the number of strong scattering points; P APC (t a ) is the phase centre position of the radar antenna on the vehicle platform, i.e. the vehicle platform position; is the position of the i-th strong scattering point; The gradient descent method is used to solve the above optimization problem. The gradient of the cost function of the vehicle platform's track and target position can be expressed as: In the formula, Respectively represent the x-axis track, y-axis track, z-axis track of the vehicle platform, and the gradient of the cost function of the target position; Each iteration calculates the cost function and its gradient obtained by (I) and (II), and updates the three-axis trajectory and target position according to the gradient descent principle to obtain the most accurate trajectory and target position estimate of the vehicle platform under the cost function.

6. The method according to claim 1, characterized in that In step 5, the two-dimensional imaging process uses the range-Doppler RD algorithm to perform the two-dimensional imaging process; or the vehicle platform track is projected into the grid and the radar imaging back projection BP algorithm is used to perform the imaging process.

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