Vehicle-mounted sar self-focusing imaging method based on slant range history and target position estimation

Abstract

The present disclosure provides a vehicle-mounted SAR self-focusing imaging method based on combined estimation of slant range history and target position. First, based on vehicle-mounted SAR echo data, Doppler information is obtained and the average speed of the vehicle platform is estimated; according to Doppler information imaging, a defocused two-dimensional imaging result is obtained; a strong scattering point in the two-dimensional imaging result is selected as a target, and the slant range history of the target is estimated. According to the slant range history and the average speed of the vehicle platform, a distance-Doppler positioning method is used to obtain a coarse estimation of the target position; a joint estimation cost function is established, which includes the slant range history, the coarse estimation of the target position and the estimation of the vehicle platform position, and the vehicle platform trajectory and the local target position are jointly estimated to obtain the vehicle platform track. Two-dimensional imaging is performed using the vehicle platform track to obtain a focused vehicle-mounted SAR image. According to the echo signal received by the radar, the platform motion trajectory is inverted and self-focusing imaging is realized, without relying on a high-precision inertial navigation system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, and particularly relates to a vehicle-mounted SAR self-focusing imaging method based on combined estimation of slant range history and target position. BACKGROUND

[0002] The vehicle-mounted synthetic aperture radar (SAR) can be applied to the field of pavement micro-target detection, etc. The millimeter wave band synthetic aperture radar is installed on a vehicle, and performs two-dimensional high-resolution imaging in the forward direction or the forward oblique strip during the vehicle running, and adopts the constant false alarm rate (CFAR) detection technology to detect the micro-targets in the imaging results.

[0003] The traditional airborne SAR system adopts high-precision integrated navigation equipment to obtain the yaw, pitch, roll angle and speed information of the moving platform, and uses the information to calculate the Doppler center of the airborne SAR echo signal, and then performs two-dimensional high-resolution imaging. Due to the influence of the comprehensive factors such as the high-frequency jolt of the vehicle, the instability of the running direction and the running speed during the running of the vehicle-mounted SAR, the running 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-mounted SAR, and is specifically embodied as imaging defocusing caused by Doppler estimation error. The use of high-data-rate (more than 100Hz) high-precision integrated navigation equipment can alleviate such problems, but the cost of the equipment is high. SUMMARY

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

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

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

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

[0008] Step 2: Slant range history rough estimation: obtaining a defocused two-dimensional imaging result f1 according to the Doppler information imaging; selecting a strong scattering point in the two-dimensional imaging result f1 as a target, and estimating the slant range history of the target

[0009] Step 3: Coarse estimation of target position: according to the slant range history of the target the Doppler information, and the average speed of the vehicle platform a coarse estimation of the target position is obtained by using the range-Doppler positioning method

[0010] Step 4: joint fine estimation of the slant range history and the target position: a joint fine estimation of the slant range history and the target position is obtained by using the gradient descent method a joint estimation cost function of the coarse estimation of the target position and the estimation of the position of the vehicle platform is used to jointly estimate the trajectory of the vehicle platform and the local target position, the cost function represents the difference between the coarse estimation of the target position and the actual target position, when the difference is the smallest, it is the solution of the cost function, and then the trajectory of the vehicle platform is obtained

[0011] Step 5: based on the echo data and the trajectory of the vehicle platform 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 is estimated based on the echo data of the vehicle-mounted SAR as follows:

[0013] the Doppler center frequency of the vehicle-mounted SAR echo data is estimated by using the time domain correlation method, the azimuth frequency modulation is estimated by using the squint MD method, and the average speed of the vehicle platform is calculated according to the Doppler center frequency and the azimuth frequency modulation.

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

[0015] Preferably, in step 3, the coarse estimation of the target position is obtained in the following manner:

[0016] Step 301: according to the slant range history the relative slant angle θ of the synthetic aperture center and the target position is calculated i ;

[0017] Step 302: according to the average speed of the vehicle platform the relative slant angle θ of the target i the azimuth angle f of the target relative to the radar is determined a the echo delay t of the target is determined d the distance between the target and the radar is determined

[0018]

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

[0020] Step 303: Utilize the target's azimuth angle f relative to the radar a and distance Determine the target location (rough estimate)

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

[0022]

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

[0024] Using gradient descent to solve the above optimization problem, the gradient of the cost function between the vehicle platform's trajectory and the target position can be expressed as:

[0025]

[0026] In the formula, These represent the gradients of the cost functions for the vehicle platform's x-axis trajectory, y-axis trajectory, z-axis trajectory, and target position, respectively.

[0027] Each iteration calculates the cost function and its gradient obtained through (I) and (II), and updates the triaxial 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 uses the range-Doppler RD algorithm; or the vehicle platform track is projected onto a grid and the radar imaging back projection BP algorithm is used for imaging processing.

[0029] Beneficial effects:

[0030] (1) This method estimates motion error based on the echo of the vehicle-mounted SAR, and uses two steps, coarse estimation and fine estimation, to invert the accurate motion trajectory and achieve autofocusing of 2D SAR imaging. Applying this algorithm can significantly improve the 2D SAR imaging quality in cases where there is no inertial navigation system (INS) or the navigation system has low 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 algorithm can be jointly estimated by using the slant range history and the target position. The algorithm has a limited traversal range and is suitable for application scenarios such as vehicle-mounted SAR.

[0032] (3) This process does not rely on a high-precision inertial navigation system, which simplifies the architecture of the vehicle-mounted SAR system and thus expands its application scope. Attached Figure Description

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

[0034] Figure 2 This is a schematic diagram of the imaging experimental observation configuration.

[0035] Figure 3 This is a comparison of the imaging results before and after self-focusing with angular reflection.

[0036] Figure 4 This is a flowchart of the present invention. Detailed Implementation

[0037] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] During operation, vehicle-mounted SAR systems are affected by a combination of factors, including high-frequency bumps on the vehicle platform and instability in travel direction and speed. This results in high-frequency motion errors and Doppler parameter errors in the SAR signal, which in turn affect SAR imaging quality and easily cause defocusing. For vehicle-mounted systems, since the slant range and slant angle of the vehicle platform itself have relatively small variations and a relatively fixed angle range, the algorithm for joint estimation using slant range history and target position has a limited traversal range and is suitable for applications like vehicle-mounted SAR. Therefore, this invention estimates Doppler parameters and motion errors and compensates for SAR signals based on vehicle trajectory information jointly estimated from slant range history and target position, which can significantly improve imaging quality.

[0039] The geometry of the vehicle-mounted SAR imaging system is in a right-handed coordinate system, with the platform's motion direction as the x-axis and the vertically upward direction as the z-axis. Assume point P is a point on a building with coordinates (x0, y0, z0). The radar transmits a linear frequency modulated (LFM) 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 represented in the two-dimensional time domain as:

[0040]

[0041] In the formula, t a The slow time (i.e., the azimuth echo signal accumulation time), t rLet A0 be the echo signal amplitude, c be the speed of light, and B be the distance to the echo signal. r λ 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 autofocusing imaging method of the present invention includes the following steps:

[0043] Step 1: Based on the echo data from the vehicle-mounted SAR, acquire Doppler information and estimate the average velocity of the vehicle platform.

[0044] The Doppler center frequency f of the echo dc azimuth frequency f dr The vehicle platform's velocity V has the following relationship:

[0045]

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

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

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

[0049] In this step, the Doppler center frequency f can be obtained using the time-domain correlation method. dc The estimated value is used to obtain the azimuth frequency f through the MD (MapDrift, visual misalignment) algorithm. dr Estimated value.

[0050] Step 102: Obtain the Doppler center frequency f dc azimuth frequency f dr Substituting into equation (3), a rough estimate of the vehicle platform's motion speed is calculated.

[0051]

[0052] Step 2: Rough estimation of slant distance history.

[0053] In this step, the Doppler information obtained in step 1 is used for imaging, and the resulting two-dimensional imaging result f1 is a defocused image. Strong scattering points in the two-dimensional imaging result f1 are selected as targets, and the slant range history of the targets is estimated.

[0054] In this step, phase estimation is required based on the strong scattering points in each region of the image. This involves identifying the peak values ​​within each range cell, calculating the signal-to-clutter ratio (SCR) of all peaks, and selecting a certain proportion of peaks with high SCRs within each range cell as the strong scattering points to be estimated. This is because at each range... Because the distances are different, some strong scattering points are selected in each distance cell, ensuring that these points are distributed as evenly as possible across different distance segments of the image to guarantee the accuracy of the estimation. These strong scattering points are used as the target.

[0055] After selecting a strong scattering point, existing phase estimation methods can be used to estimate the phase of the i-th strong scattering point of the target. Substitute into equation (4) to calculate the estimated value of the slant distance history. The relationship between the two is as follows:

[0056]

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

[0058] This step will be implemented using the following plan:

[0059] Step 301: Based on the slant distance history Calculate the relative oblique 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 above the ground.

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

[0063]

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

[0065] Step 303: Utilize the target's azimuth angle f relative to the radar a and distance Determine the target location (rough estimate)

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

[0067] This step calculates the slant range history of the local target. (Relationship between slant range and azimuth time) and rough estimate of target position An optimization method is used to obtain a precise trajectory. During this process, the target position is coarsely estimated. The residual error will lead to deviations in trajectory estimation, therefore joint estimation of the trajectory and local target position is required. This estimation problem is an optimization problem that minimizes the cost function, i.e., the cost function represents the difference between the coarse target position and the actual target position; the solution to the cost function is when this difference is minimized. To simplify the derivation, the cost function for this optimization process is the antenna phase center P in the vehicle platform. APC (t a ) and rough target location Distance and local target slant range history The difference is calculated, and the cost function expression for each objective is summed, i.e.

[0068]

[0069] In the formula, N is the number of strong scattering points; P APC (t a () represents the phase center position of the radar antenna on the vehicle platform, i.e., the three-dimensional coordinates of the vehicle platform. For the coordinate estimation of the i-th target's strong scattering point, This is the slant range history of the i-th target estimated in the previous step.

[0070] Gradient descent is an extremum-finding method that searches for a local minimum of a function by iteratively moving towards points at a predetermined step distance in the opposite direction of the gradient corresponding to the current point.

[0071] In this invention, gradient descent is used to solve the optimization problem. The gradient of the three-axis trajectory with respect to the target position cost function can be expressed as follows:

[0072]

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

[0074] Each iteration calculates the cost function and its gradient obtained through (6) and (7), and updates the three-axis trajectory of the vehicle platform and the target position according to the gradient descent principle, so as to obtain the most accurate vehicle platform trajectory and target position estimate under the cost function, thereby realizing trajectory estimation without relying on high-precision navigation equipment.

[0075] Step 5: Perform autofocusing 2D SAR imaging using the vehicle platform trajectory.

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

[0077] Implementation Cases

[0078] In this experiment, SAR was installed on a tracked unmanned vehicle, such as... Figure 1 As shown in Table 1.

[0079] Table 1. Experimental Parameters of Vehicle-Mounted SAR

[0080]

[0081]

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

[0083] Using the ideal trajectory generated by the estimated velocity and the precise trajectory separately for BP imaging is equivalent to autofocusing the image. The observation geometry of the imaging experiment is as follows: Figure 2 As shown. Figure 3 This image shows a comparison of the imaging results before and after autofocus. The comparison clearly shows that the image quality is significantly improved after autofocus.

[0084] The specific embodiments described above only illustrate the design principles of the present invention. The shapes and names of the components in this description may differ and are not limited. Therefore, those skilled in the art can modify or make equivalent substitutions to the technical solutions described in the foregoing embodiments; and these modifications and substitutions do not depart from the inventive spirit 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 combined estimation of slant range history and target position, characterized in that, The method comprises the steps of: Step 1: Obtain Doppler information and estimate the average velocity of the vehicle platform based on the SAR echo data onboard the vehicle ; Step 2: Rough estimation of slant range history: obtain a defocused two-dimensional imaging result according to the Doppler information imaging ; select a strong scattering point in the two-dimensional imaging result as a target, and estimate the slant range history of the target ; Step 3: Coarse target location: from slant range history of target , the Doppler information, and the average speed of the vehicle platform , a coarse target location is obtained using a range-Doppler approach ; Step 4: Combined fine estimation of slant range history and target position: a joint estimation cost function is established including the slant range history, the coarse estimation of target position and the estimation of vehicle platform position, and a gradient descent method is used to jointly estimate the trajectory of vehicle platform and the local target position, the cost function represents the difference between the coarse estimation of target position and the actual target position, and the solution of the cost function is when the difference is the smallest, and then the trajectory of vehicle platform is obtained. , the coarse estimation of target position and the estimation of vehicle platform position, and a gradient descent method is used to jointly estimate the trajectory of vehicle platform and the local target position, the cost function represents the difference between the coarse estimation of target position and the actual target position, and the solution of the cost function is when the difference is the smallest, and then the trajectory of vehicle platform is obtained. In 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 of claim 1, wherein, In step 1, the average speed of the vehicle platform is estimated based on the vehicle-mounted SAR echo data as: The time-domain correlation method is used to estimate the Doppler center frequency of the vehicle-mounted SAR echo data, the squint MD method is used to estimate the azimuth frequency modulation, and the average speed of the vehicle platform is calculated according to the Doppler center frequency and the azimuth frequency modulation.

3. The method of claim 1, wherein, In step 2, the strong scattering points in the two-dimensional imaging result are selected as targets In the case of selecting the strong scattering points in the two-dimensional imaging result as the targets, the peak value in each distance unit is found, the signal-to-clutter ratio of all the peak values is calculated, and the peak value with a high signal-to-clutter ratio in a set proportion in each distance unit is selected as the strong scattering point.

4. The method of claim 1, wherein, In step 3, the target position is coarsely estimated by: Step 301 : determining the slant range history calculating the relative slant angle of the synthetic aperture center to the target location ; Step 302: determining the average speed of the vehicle platform , the relative slant angle of the target determining the azimuth angle of the target relative to the radar , the delay of the target echo determining the distance between the target and the radar ; where c is the speed of light, is the signal wavelength; Step 303: determining a coarse estimate of the target position using the azimuth of the target relative to the radar and the range .​ 5. The method of claim 1, wherein, In step 4, the cost function is established as: （I） Where N is the number of strong scattering points; This refers to the position of the radar antenna phase center on the vehicle platform, i.e., the position of the vehicle platform. For the first i The location of a strong scattering point; The gradient descent method is used to solve the cost function, and the track of the vehicle platform and the gradient of the cost function of the target position are represented as: (I) wherein respectively represent a vehicle platform x axis track, y axis track, z axis track, and a gradient of a cost function of a target position; In each iteration, the cost function and its gradient obtained by (I) and (II) are calculated, and the three-axis track and the target position are updated according to the gradient descent principle to obtain the most accurate track of the vehicle platform and the target position estimation under the cost function.

6. The method of claim 1, wherein, In step 5, the distance-Doppler RD algorithm is used for two-dimensional imaging processing; or the vehicle platform track is projected into a grid, and the radar imaging back projection BP algorithm is used for imaging processing.

Images