Three-dimensional positioning method and device for multi-angle spaceborne SAR target

Abstract

The invention discloses a three-dimensional positioning method and device for a multi-angle spaceborne SAR target. The method comprises the following steps: acquiring a multi-azimuth-angle satellite-borne SAR image sequence of a simulation scene; taking a certain angle image in the image sequence as a main image, and calculating elevation estimation sensitivity of each other angle image based on the main image; determining a plurality of sampling heights according to a target height range of the simulation scene; calculating a correlation coefficient between each target point in the main image and a corresponding matching point in each other angle image at each sampling height; based on the elevation estimation sensitivity and the correlation coefficient at each sampling height, constructing a single-angle elevation estimation probability density function of each target point in the main image and each other angle image; calculating a height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function; and calculating a three-dimensional position estimation result of each target point based on the focusing position and the height estimation value of the target point in the main image. According to the invention, the three-dimensional position of the target can be accurately inverted.

Description

Technical Field

[0001] The present invention relates to the technical field of target positioning, and particularly relates to a three-dimensional positioning method and device for multi-angle spaceborne SAR targets. Background Art

[0002] An SAR image is essentially a projection of the three-dimensional electromagnetic scattering characteristics of a target onto a two-dimensional imaging plane. Its core value lies in realizing continuous perception of strategic targets in the monitored area through all-weather and all-time remote sensing observation capabilities. However, due to the side-looking imaging characteristics of the SAR system, complex three-dimensional targets have problems such as top-bottom inversion, occlusion, and layover in SAR images, resulting in confusion of target spatial positions and distortion of geometric structures, severely restricting the interpretation and precise positioning of targets.

[0003] The spaceborne SAR system with multi-azimuth angle observation forms a curved trajectory during a long-term staring process, which is equivalent to forming discrete sampling in the elevation direction and has the ability to reconstruct the three-dimensional target scene. However, existing three-dimensional multi-angle target three-dimensional positioning methods mostly adopt approximate processing during the positioning process, resulting in an inaccurate elevation estimation model and low positioning accuracy. Summary of the Invention

[0004] The present invention provides a three-dimensional positioning method and device for multi-angle spaceborne SAR targets, which can accurately invert the three-dimensional position of a target through a sequence of multi-angle SAR images. The technical solution is as follows:

[0005] On the one hand, a three-dimensional positioning method for multi-angle spaceborne SAR targets is provided. The method includes:

[0006] Obtain a sequence of multi-azimuth angle spaceborne SAR images of a simulation scene; wherein, each SAR image corresponds to an azimuth angle;

[0007] Take an image at a certain angle in the image sequence as the main image, and calculate the elevation estimation sensitivity of each other-angle image based on the main image;

[0008] Determine a plurality of sampling heights according to the target height range of the simulation scene;

[0009] Calculate the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height;

[0010] Based on the elevation estimation sensitivity and the correlation coefficient at each sampling height, construct a single-angle elevation estimation probability density function for each target point in the main image and each other-angle image;

[0011] Based on each single-angle elevation estimation probability density function, calculate the height estimation value of each target point in the main image;

[0012] Calculate the three-dimensional position estimation result of each target point based on the focusing position and height estimation value of each target point in the main image.

[0013] On the other hand, a three-dimensional positioning device for multi-angle spaceborne SAR targets is provided, and the device includes:

[0014] An acquisition unit for acquiring a multi-azimuth spaceborne SAR image sequence of a simulation scene; wherein, each SAR image corresponds to an azimuth angle;

[0015] A first calculation unit for taking an image at a certain angle in the image sequence as the main image and calculating the elevation estimation sensitivity of each other-angle image based on the main image;

[0016] A sampling height determination unit for determining a plurality of sampling heights according to the target height range of the simulation scene;

[0017] A correlation coefficient calculation unit for calculating the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height;

[0018] A construction unit for constructing a single-angle elevation estimation probability density function of each target point in the main image and each other-angle image based on the elevation estimation sensitivity and the correlation coefficient at each sampling height;

[0019] A second calculation unit for calculating the height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function;

[0020] A third calculation unit for calculating the three-dimensional position estimation result of each target point based on the focusing position and height estimation value of each target point in the main image.

[0021] On the other hand, a computer device is provided, and the computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the steps of the above-mentioned three-dimensional positioning method for multi-angle spaceborne SAR targets.

[0022] On the other hand, a computer-readable storage medium is provided, and a computer program is stored in the storage medium. When the computer program is executed by a processor, the steps of the above-mentioned three-dimensional positioning method for multi-angle spaceborne SAR targets are implemented.

[0023] On the other hand, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned three-dimensional positioning method for multi-angle spaceborne SAR targets are implemented.

[0024] An embodiment of the present invention provides a three-dimensional positioning method for multi-angle spaceborne SAR targets. Through a multi-angle SAR image sequence, first calculate the elevation estimation sensitivity between each angle image and the main image. Then, using multiple sampling heights, calculate the correlation coefficient between each target point and the matching points in other angle images and construct the corresponding elevation estimation probability density function. Then, using each single-angle elevation estimation probability density function, the height estimation value of each target point in the main image can be accurately calculated. Finally, invert the three-dimensional position of the target point according to the height estimation value. The method of the present application has no approximation processing during the positioning process, has a better elevation estimation model, and can achieve higher three-dimensional positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 is a flowchart of a three-dimensional positioning method for multi-angle spaceborne SAR targets provided by an embodiment of the present invention;

[0027] Figure 2 is a structural diagram of a three-dimensional positioning device for multi-angle spaceborne SAR targets provided by an embodiment of the present invention;

[0028] Figure 3 is a hardware architecture diagram of a computer device provided by an embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a simulation scenario provided by an embodiment of the present invention;

[0030] Figure 5 is the reconstruction result of a multi-angle three-dimensional positioning method based on image point projection geometry provided by an embodiment of the present invention;

[0031] Figure 6 is the reconstruction result of a multi-angle three-dimensional positioning method using the method of the present application provided by an embodiment of the present invention;

[0032] Figure 7 is a schematic diagram of the comparison result of the target point elevation estimation error between the multi-angle three-dimensional positioning method based on image point projection geometry and the method proposed in the present application provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] The following describes the specific implementation manners of the above concepts.

[0035] Please refer to Figure 1 , a three-dimensional positioning method for multi-angle spaceborne SAR targets provided by an embodiment of the present invention, the method includes:

[0036] Step 100, obtaining a multi-azimuth spaceborne SAR image sequence of a simulation scene; wherein, each SAR image corresponds to an azimuth angle;

[0037] Step 102, taking an image at a certain angle in the image sequence as the main image, and calculating the elevation estimation sensitivity of each other-angle image based on the main image;

[0038] Step 104, determining a plurality of sampling heights according to the target height range of the simulation scene;

[0039] Step 106, calculating the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height;

[0040] Step 108, constructing a single-angle elevation estimation probability density function of each target point in the main image and each other-angle image based on the elevation estimation sensitivity and the correlation coefficient at each sampling height;

[0041] Step 110, calculating the height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function;

[0042] Step 112, calculating the three-dimensional position estimation result of the target point based on the focusing position and the height estimation value of each target point in the main image.

[0043] In the embodiments of the present invention, through a multi-angle SAR image sequence, first, the elevation estimation sensitivity between each angle image and the main image is calculated. Then, using multiple sampling heights, the correlation coefficient between each target point and the matching points in other angle images is calculated and the corresponding elevation estimation probability density function is constructed. Then, using each single-angle elevation estimation probability density function, the height estimation value of each target point in the main image can be accurately calculated. Finally, according to the height estimation value, the three-dimensional position of the target point is inverted. The method of the present application has no approximation processing during the positioning process, has a better elevation estimation model, and can achieve higher three-dimensional positioning accuracy.

[0044] The following describes Figure 1 the execution manners of the various steps shown.

[0045] First, for step 100:

[0046] The image parameters of each SAR image include: the satellite position vector and velocity vector at the observation center time corresponding to the image, azimuth resolution, range resolution, azimuth sampling interval, range sampling interval, number of azimuth points, and number of range points. In addition, the simulation parameters of the simulation scenario include the image slice length L and the scene height range [h min , h max , h min and h max are the minimum height and the maximum height respectively.

[0047] In addition, the number and angle range of the SAR image sequence are determined according to user needs, and the present application does not make specific limitations.

[0048] Then, for step 102:

[0049] Any angle image can be used as the main image. Preferably, the present application uses the image corresponding to the first azimuth angle as the main image (i.e., the first image in the image sequence), so as to ensure higher calculation accuracy.

[0050] In some embodiments, step 102 includes:

[0051] Step B1, setting the target height unit vector and the intermediate matrix;

[0052] Step B2, based on the satellite position vector and velocity vector at the observation center time corresponding to each angle image, the target height unit vector, and the intermediate matrix, calculating the solutions of the forward positioning equations corresponding to each angle image respectively;

[0053] Step B3, based on the solutions of the forward positioning equations corresponding to each angle image, calculating the focusing positions of the target height unit vector in each angle image respectively;

[0054] Step B4: Calculate the azimuth offset and ground range offset between the focusing position of the target height unit vector in the main image and its focusing positions in the images at other angles.

[0055] Step B5: Calculate the elevation estimation sensitivity of each image at other angles based on the azimuth offset and ground range offset.

[0056] In step B1, the target height unit vector Q is preferably:

[0057] The intermediate matrix A is preferably:

[0058] Of course, the user can also adopt other matrices, and this application is not limited thereto.

[0059] In step B2, the solutions of the forward positioning equations corresponding to each angle image are calculated respectively by the following formulas:

[0060] α k =[V k (2); -V k (1); 0]

[0061]

[0062] In the formula, α k , β k and c k are respectively the first solution, the second solution and the constant coefficient of the forward positioning equation corresponding to the image at angle k; V k and S k are respectively the velocity vector and position vector of the satellite at the observation center time corresponding to the image at angle k, where S k (1), S k (2) and S k (3) are respectively the position components in the x, y, and z directions; V k (1), V k (2) and V k (3) are respectively the velocity components in the x, y, and z directions; k = 1, 2... N look , N look is the number of azimuth angles, that is, the number of the image sequence.

[0063] In step B3, calculate the focusing position of the target height unit vector in each angle image through the following formula:

[0064] P k =c k ·α k +β k

[0065] Wherein, P k is the focusing position of the target height unit vector Q in the image of angle k, P k (1), P k (2) and P k (3) are the components in the x, y, and z directions respectively.

[0066] In step B4, the azimuth offset and the range offset are calculated respectively by the following formulas:

[0067] Δx 1-k = P k (1) - P1(1), Δy 1-k = P k (2) - P1(2)

[0068] Wherein, Δx 1-k and Δy 1-k are respectively the azimuth offset and the range offset between the focusing position of the target height unit vector Q in the image of angle 1 (main image) and the focusing position in the image of angle k.

[0069] In step B5, the elevation estimation sensitivity of each other angle image is calculated by the following formula:

[0070]

[0071] Wherein, ε k is the elevation estimation sensitivity of the image of angle k; ρ kx and ρ ky are respectively the azimuth resolution and the range resolution of the image of angle k, k = 2, 3... N look .

[0072] Then, for step 104, the following formula is used to determine multiple sampling heights:

[0073] h(n) = h min + 0.1·n, n = 1, 2,..., round[10·(h max - h min )]

[0074] Wherein, h(n) is the nth sampling height; round(·) is the rounding operation.

[0075] For step 106, it includes:

[0076] For each sampling height, the following is executed:

[0077] Step C1, set the height of each target point in the main image to be equal to this sampling height, and calculate the sampled three-dimensional position of each target point at this sampling height;

[0078] Step C2: Calculate the focusing position of each target point in each other - angle image when the target point is at its sampled three - dimensional position based on the forward positioning equation.

[0079] Step C3: Extract the first image slice with a preset size around each target point in the main image, and the second image slice with a preset size around the focusing position of each target point in each other - angle image.

[0080] Step C4: Calculate the correlation coefficient between the first image slice and the second image slice corresponding to each target point, and use this correlation coefficient as the correlation coefficient between each target point at this sampling height and its corresponding matching point in each other - angle image.

[0081] In some embodiments, Step C1 includes:

[0082] For each target point in the main image, the following operations are performed:

[0083] Step D1: Calculate the focusing position of the target point in the main image according to the pixel position, the number of azimuth points, the number of range points, the azimuth sampling interval, and the range sampling interval of the target point in the main image.

[0084] Step D2: Based on the focusing position, this sampling height, the satellite position vector and velocity vector at the observation center time corresponding to the main image, calculate the solution of the reverse positioning equation corresponding to the target point.

[0085] Step D3: Based on the solution of the reverse positioning equation, calculate the sampled three - dimensional position of the target point at this sampling height.

[0086] In Step D1, taking the i - th target point as an example, calculate the focusing position of the target point in the main image through the following formula:

[0087]

[0088] where P 1i is the focusing position of target point i in the main image; and are respectively the pixel positions of target point i in the main image; δ x and δ y are respectively the azimuth sampling interval and the range sampling interval of the SAR image; N x and N y are respectively the number of azimuth points and the number of range points of the SAR image.

[0089] In Step D2, let the sampling height of target point i be h n , then calculate the solution of the reverse positioning equation corresponding to target point i through the following formula:

[0090]

[0091]

[0092] In the formula, ξ i and d i are the first solution, the second solution and the constant coefficient of the inverse positioning equation corresponding to the target point i respectively; P 1i (1) and P 1i (2) are the components of P 1i in the x and y directions respectively.

[0093] In step D3, the sampled three-dimensional position of the target point i at this sampling height is calculated by the following formula:

[0094]

[0095] In the formula, T i is the sampled three-dimensional position of the target point i at this sampling height

[0096] In step C2, when calculating the target point i at the sampled three-dimensional position T i , the focusing position of the target point i in the image at angle k is:

[0097] α ki =[V k (2); -V k (1); 0]

[0098]

[0099] P ki =c ki ·α ki +β ki

[0100] In the formula, P ki is the focusing position of the target point i in the image at angle k when it is at the sampled three-dimensional position T i , and are the components in the x and y directions respectively; α ki , β ki and c ki are the first solution, the second solution and the constant coefficient of the forward positioning equation corresponding to the image at angle k when the target point i is at the sampled three-dimensional position T i ; k = 2, 3... N look .

[0101] In step C3, the preset size is 2L + 1, and the target point i is the first image slice At the center, the focusing position of each target point i in the angular image k is the second image slice at the center.

[0102] In step C4, the following formula is used to calculate the correlation coefficient between the first image slice and the second image slice corresponding to the target point i at the sampling height h n as follows:

[0103]

[0104] In the formula, and are the mean values of the first image slice and the second image slice corresponding to the target point i, respectively.

[0105] In some embodiments, for step 108, it includes:

[0106] For each target point in each other angular image and the main image, the following operations are performed:

[0107] S1, based on the correlation coefficients between the target point and its matching points corresponding to the current angular image at each sampling height, plot a discrete correlation coefficient curve, and perform S2;

[0108] S2, solve the maximum peak in the current correlation coefficient curve and the peak height corresponding to the maximum peak, and construct the current correlation coefficient model at the peak height based on the elevation estimation sensitivity of the current angular image, and perform S3;

[0109] S3, remove the maximum peak and the discrete points within a preset range around the peak height from the current correlation coefficient model curve to obtain an updated correlation coefficient curve, and determine whether there is still a maximum peak in the updated correlation coefficient curve. If so, use the updated correlation coefficient curve as the current correlation coefficient curve and return to perform S2; if not, use the current correlation coefficient model as the final correlation coefficient model and perform S4;

[0110] S4, perform normalization processing on the final correlation coefficient model to obtain the single-angle elevation estimation probability density function of the target point and the current angular image.

[0111] In this step, the calculation formula of the final correlation coefficient model of each angular image is:

[0112]

[0113] The single-angle elevation estimation probability density functions of each angular image are respectively:

[0114]

[0115] where the subscript k represents the image corresponding to the azimuth angle k; Coh′ k (h) is the final correlation coefficient model corresponding to the image of angle k; γ km is the correlation coefficient of the m-th maximum peak point, m = 1, 2... M, and M is the total number of peak points; h is the high-order sequence composed of each sampling height; h km is the peak height corresponding to γ km ; ε k is the elevation estimation sensitivity corresponding to the image of angle k; {·} mainlobe is the operation of taking the main lobe region of the sinc function; pdf k (h) is the elevation estimation probability density function corresponding to the image of angle k; h min and h max are the minimum height and the maximum height in the high-order sequence respectively.

[0116] In some embodiments, for step 110, it includes:

[0117] Based on each single-angle elevation estimation probability density function, the multi-angle joint probability density function is constructed by the following formula:

[0118]

[0119] Solve the maximum likelihood solution of the multi-angle joint probability density function, and calculate the height estimation value of each target point in the main image;

[0120] where pdf multi (h) is the multi-angle joint probability density function; pdf k (h) is the elevation estimation probability density function of the image of angle k; N look is the total number of azimuth angles.

[0121] In addition, the height estimation value of the target point i in the main image is calculated by the following formula

[0122]

[0123] In some embodiments, for step 112, the three-dimensional position estimation result of the target point i is calculated by the following formula:

[0124] ζ' i =[-V1(2); V1(1); 0]

[0125]

[0126] T' i =d' i ·ζ i +ξ' i

[0127] In the formula, ζ' i , ξ' i and d' i are respectively the first solution, the second solution and the constant coefficient of the corresponding inverse positioning equation when the estimated value of the height at the target point i is ; T' i is the three-dimensional position estimation result when the estimated value of the height at the target point i is .

[0128] In summary, by calculating the three-dimensional position estimation results of each target point in the main image using the above process, a three-dimensional point cloud map can be generated, and a three-dimensional positioning result map of the simulation scene can be obtained.

[0129] To prove the beneficial effects of the present invention, the inventor uses the following embodiments to compare the estimation results of the method of the present application with the estimation results of the existing method (a multi-angle three-dimensional positioning method based on image point projection geometry). The simulation scene is as Figure 4 shown. It can be seen from the figure that there are 11 target points in each image, which are respectively denoted as P1 to P11. In addition, the simulation parameters are shown in Table 1:

[0130] Table 1 System simulation parameters

[0131]

[0132] In addition, 10-angle SAR images are used, and the image at angle 1 is used as the main image. The observation parameters of SAR images at different angles are shown in Table 2.

[0133] Table 2 Observation parameters of SAR images at different angles

[0134]

[0135] Calculations are respectively performed using the above parameters. The calculation results using the existing method are as Figure 5 shown, and the calculation results using the method of the present application are as Figure 6 shown. Among them, the yellow square represents the true position of the target, and the dot is the algorithm estimation result. Intuitively comparing the three-dimensional reconstruction results of the two algorithms shows that: the height estimation error of the method based on image point projection geometry is relatively large, a large number of points are outside the true position, and the highest height estimated by it is also much higher than the highest height of the true target. In contrast, the estimated target position of the method proposed in the present application is closest to the true target position, and it is basically within the yellow square.

[0136] In addition, Figure 7The elevation estimation error of the method based on image-point projection geometry and the method proposed in this application for the target points. By comparing the elevation estimation errors of each target point, it can be seen that the height estimation accuracy of the method proposed in this application is relatively good, and the error is basically controlled within 2m. However, the height estimation error of the method based on image-point projection geometry has exceeded 30m, resulting in a large positioning error.

[0137] It can be seen from this that the calculation result of the method in this application is closer to the real target position, and the calculation accuracy is relatively high.

[0138] As Figure 2 , Figure 3 shown, the embodiments of the present invention provide a three-dimensional positioning device for multi-angle spaceborne SAR targets. The device embodiments can be implemented by software, or by hardware or a combination of software and hardware. In terms of the hardware level, as Figure 2 shown, it is a hardware architecture diagram of a computing device where the three-dimensional positioning device for multi-angle spaceborne SAR targets provided by the embodiments of the present invention is located. In addition to Figure 2 the processor, memory, network interface, and non-volatile memory shown, the computing device where the device is located in the embodiments usually may also include other hardware, such as a forwarding chip responsible for processing packets, and so on. Taking software implementation as an example, as Figure 3 shown, as a logically meaningful device, it is formed by the CPU of its computing device reading the corresponding computer program in the non-volatile memory into the memory and running.

[0139] Please refer to Figure 3 , the embodiments of this application provide a three-dimensional positioning device for multi-angle spaceborne SAR targets, including:

[0140] An acquisition unit 300, configured to acquire a multi-azimuth spaceborne SAR image sequence of a simulation scene; wherein, each SAR image corresponds to an azimuth angle;

[0141] A first calculation unit 302, configured to use an image at a certain angle in the image sequence as the main image, and calculate the elevation estimation sensitivity of each other-angle image based on the main image;

[0142] A sampling height determination unit 304, configured to determine a plurality of sampling heights according to the target height range of the simulation scene;

[0143] A correlation coefficient calculation unit 306, configured to calculate the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height;

[0144] A construction unit 308, configured to construct a single-angle elevation estimation probability density function of each target point in the main image and each other-angle image based on the elevation estimation sensitivity and the correlation coefficient at each sampling height;

[0145] A second calculation unit 310, configured to calculate a height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function;

[0146] A third calculation unit 312, configured to calculate a three-dimensional position estimation result of each target point based on the focusing position and height estimation value of each target point in the main image.

[0147] In some embodiments, the first calculation unit 302 is configured to perform the following operations:

[0148] Set a target height unit vector and an intermediate matrix;

[0149] Based on the satellite position vector and velocity vector at the observation center time corresponding to each angular image, the target height unit vector, and the intermediate matrix, calculate the solution of the forward positioning equation corresponding to each angular image respectively;

[0150] Based on the solutions of the forward positioning equations corresponding to each angular image, calculate the focusing positions of the target height unit vector in each angular image respectively;

[0151] Calculate the azimuth offset and ground distance offset between the focusing position of the target height unit vector in the main image and its focusing positions in other angular images;

[0152] Based on the azimuth offset and ground distance offset, calculate the elevation estimation sensitivity of each other angular image.

[0153] In some embodiments, the correlation coefficient calculation unit 306 is configured to perform the following operations:

[0154] For each sampling height, perform:

[0155] Set the height of each target point in the main image to be equal to the sampling height, and calculate the sampling three-dimensional position of each target point at the sampling height;

[0156] Based on the forward positioning equation, calculate the focusing position of each target point in each other angular image when it is at its sampling three-dimensional position;

[0157] Extract a first image slice with a preset size around each target point in the main image, and a second image slice with a preset size around the focusing position of each target point in each other angular image;

[0158] Calculate the correlation coefficient between the first image slice and the second image slice corresponding to each target point, and use the correlation coefficient as the correlation coefficient between each target point at the sampling height and its corresponding matching point in each other angular image.

[0159] In some embodiments, when the correlation coefficient calculation unit 306 calculates the sampled three-dimensional position of each target point at the sampling height, it is used to perform the following operations:

[0160] For each target point in the main image, the following operations are performed:

[0161] According to the pixel position, azimuth number of points, range number of points, azimuth sampling interval, and ground range sampling interval of the target point in the main image, calculate the focusing position of the target point in the main image;

[0162] Based on the focusing position, the sampling height, the satellite position vector and velocity vector at the observation center time corresponding to the main image, calculate the solution of the inverse positioning equation corresponding to the target point;

[0163] Based on the solution of the inverse positioning equation, calculate the sampled three-dimensional position of the target point at the sampling height.

[0164] In some embodiments, the construction unit 308 is used to perform the following operations:

[0165] For each other angle image and each target point in the main image, the following operations are performed:

[0166] S1. Based on the correlation coefficient between the target point and the matching point corresponding to the current angle image at each sampling height, draw a discrete correlation coefficient curve, and perform S2;

[0167] S2. Solve the maximum peak in the current correlation coefficient curve and the peak height corresponding to the maximum peak, and construct the current correlation coefficient model at the peak height based on the elevation estimation sensitivity of the current angle image, and perform S3;

[0168] S3. Exclude the maximum peak and the discrete points within a preset range around the peak height from the current correlation coefficient model curve to obtain an updated correlation coefficient curve, and determine whether there is still a maximum peak in the updated correlation coefficient curve. If so, use the updated correlation coefficient curve as the current correlation coefficient curve and return to execute S2; if not, use the current correlation coefficient model as the final correlation coefficient model and perform S4;

[0169] S4. Normalize the final correlation coefficient model to obtain the single-angle elevation estimation probability density function of the target point and the current angle image.

[0170] In some embodiments, the calculation formula of the final correlation coefficient model of each angle image is:

[0171]

[0172] The single-angle elevation estimation probability density functions of each angle image are respectively:

[0173]

[0174] In the formula, the subscript k represents the image corresponding to the azimuth angle k; Coh k ′(h) is the final correlation coefficient model corresponding to the image of angle k; γ km is the correlation coefficient of the m-th maximum peak point, m = 1, 2... M, where M is the total number of peak points; h is the height sequence composed of each sampling height; h km is the peak height corresponding to γ km ; ε k is the elevation estimation sensitivity corresponding to the image of angle k; {·} mainlobe is the operation of taking the main lobe region of the sinc function; pdf k (h) is the elevation estimation probability density function corresponding to the image of angle k; h min and h max are respectively the minimum height and the maximum height in the height sequence.

[0175] In some embodiments, the second calculation unit is used to perform the following operations:

[0176] Based on each single-angle elevation estimation probability density function, the following formula is used to construct a multi-angle joint probability density function:

[0177]

[0178] Solve the maximum likelihood solution of the multi-angle joint probability density function, and calculate the height estimation value of each target point in the main image;

[0179] In the formula, pdf multi (h) is the multi-angle joint probability density function; pdf k (h) is the elevation estimation probability density function of the image of angle k; N look is the total number of azimuth angles.

[0180] It should be noted that: for the three-dimensional positioning device of the multi-angle spaceborne SAR target provided in the above embodiments, only the above division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the three-dimensional positioning device of the multi-angle spaceborne SAR target provided in the above embodiments and the embodiments of the three-dimensional positioning method of the multi-angle spaceborne SAR target belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0181] The embodiments of the present application also provide a computer device. Please refer to Figure 3, the computer device includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the three-dimensional positioning method for multi-angle spaceborne SAR targets provided in the above method embodiments.

[0182] An embodiment of the present application also provides a computer-readable storage medium. At least one instruction, at least one program, a code set or an instruction set is stored on the computer-readable storage medium. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the three-dimensional positioning method for multi-angle spaceborne SAR targets provided in the above method embodiments.

[0183] An embodiment of the present application also provides a computer program product. The computer program product includes a computer program. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the three-dimensional positioning method for multi-angle spaceborne SAR targets described in any one of the above embodiments.

[0184] For the convenience of description, when describing the above system or device, it is divided into various modules or units according to functions for separate description. Of course, when implementing the present application, the functions of each unit can be realized in one or more software and / or hardware.

[0185] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments of the present application.

[0186] Finally, it should also be noted that in this text, relational terms such as first, second, third, and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the said element.

[0187] The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A three-dimensional positioning method for multi-angle spaceborne SAR targets, characterized in that, The method includes: Obtaining a sequence of multi-azimuth spaceborne SAR images of a simulation scene; wherein, each SAR image corresponds to an azimuth angle; Taking an image at a certain angle in the image sequence as the main image, and calculating the elevation estimation sensitivity of each other-angle image based on the main image; Determining a plurality of sampling heights according to the target height range of the simulation scene; Calculating the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height; Constructing a single-angle elevation estimation probability density function of each target point in the main image and each other-angle image based on the elevation estimation sensitivity and the correlation coefficient at each sampling height; Calculating the height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function; Calculating the three-dimensional position estimation result of the target point based on the focusing position and the height estimation value of each target point in the main image.

2. The method according to claim 1, wherein The calculating the elevation estimation sensitivity of each other-angle image based on the main image includes: Setting a target height unit vector and an intermediate matrix; Calculating the solution of the forward positioning equation corresponding to each angle image respectively based on the satellite position vector and velocity vector at the observation center moment corresponding to each angle image, the target height unit vector, and the intermediate matrix; Calculating the focusing position of the target height unit vector in each angle image respectively based on the solution of the forward positioning equation corresponding to each angle image; Calculating the azimuth offset and the ground range offset between the focusing position of the target height unit vector in the main image and its focusing positions in other angle images; Calculating the elevation estimation sensitivity of each other-angle image based on the azimuth offset and the ground range offset.

3. The method according to claim 1, characterized in that The calculating the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height includes: For each sampling height, perform: Making the height of each target point in the main image equal to the sampling height, and calculating the sampled three-dimensional position of each target point at the sampling height; Calculating the focusing position of each target point in each other-angle image when the target point is at its sampled three-dimensional position based on the forward positioning equation; Extracting a first image slice with a preset size around each target point in the main image, and a second image slice with the preset size around the focusing position of each target point in each other-angle image; Calculating the correlation coefficient between the first image slice and the second image slice corresponding to each target point, and using this correlation coefficient as the correlation coefficient between each target point and its corresponding matching point in each other-angle image at the sampling height.

4. The method according to claim 3, wherein The calculating the sampled three-dimensional position of each target point at the sampling height includes: For each target point in the main image, perform: Calculating the focusing position of the target point in the main image according to the pixel position, the number of azimuth points, the number of range points, the azimuth sampling interval, and the ground range sampling interval of the target point in the main image; Calculate the solution of the reverse positioning equation corresponding to the target point based on the focusing position, the sampling height, the satellite position vector and velocity vector at the observation center moment corresponding to the main image; Calculate the sampled three-dimensional position of the target point at the sampling height based on the solution of the reverse positioning equation.

5. The method according to claim 1, characterized in that, The constructing of the single-angle elevation estimation probability density function of each target point in the main image and each other-angle image based on the elevation estimation sensitivity and the correlation coefficient at each sampling height includes: For each other-angle image and each target point in the main image, perform: S1. Draw a discrete correlation coefficient curve based on the correlation coefficient between the target point and the matching point corresponding to the current angle image at each sampling height, and perform S2; S2. Solve the maximum peak in the current correlation coefficient curve and the peak height corresponding to the maximum peak, and construct the current correlation coefficient model at the peak height based on the elevation estimation sensitivity of the current angle image, and perform S3; S3. Remove the maximum peak and the discrete points within a preset range around the peak height from the current correlation coefficient model curve to obtain an updated correlation coefficient curve, and determine whether there is still a maximum peak in the updated correlation coefficient curve. If so, use the updated correlation coefficient curve as the current correlation coefficient curve and return to perform S2; if not, use the current correlation coefficient model as the final correlation coefficient model and perform S4; S4. Normalize the final correlation coefficient model to obtain the single-angle elevation estimation probability density function of the target point and the current angle image.

6. The method according to claim 5, wherein The calculation formula of the final correlation coefficient model of each angle image is: The single-angle elevation estimation probability density functions of each angle image are respectively: where the subscript k represents the image corresponding to the azimuth angle k; Coh k ′(h) is the final correlation coefficient model corresponding to the image of angle k; γ km is the correlation coefficient of the m-th maximum peak point, m = 1, 2... M, where M is the total number of peak points; h is the height sequence composed of each sampling height; h km is the peak height corresponding to γ km ; ε k is the elevation estimation sensitivity corresponding to the image of angle k; {·} mainlobe is the operation of taking the main lobe region of the sinc function; pdf k (h) is the elevation estimation probability density function corresponding to the image of angle k; h min and h max are the minimum height and the maximum height in the height sequence, respectively.

7. The method according to claim 5, characterized in that, The calculating of the height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function includes: Construct a multi-angle joint probability density function by the following formula based on each single-angle elevation estimation probability density function: Solve the maximum likelihood solution of the multi-angle joint probability density function and calculate the height estimation value of each target point in the main image; where pdf multi (h) is the multi - angle joint probability density function; pdf k (h) is the probability density function of the elevation estimation of the k - th angle image; N look is the total number of azimuth angles.

8. A three-dimensional positioning device for multi-angle spaceborne SAR targets, characterized in that, The device includes: An acquisition unit, configured to acquire a multi-azimuth spaceborne SAR image sequence of a simulation scenario; wherein, each SAR image corresponds to an azimuth angle; A first calculation unit, configured to use a certain angle image in the image sequence as the main image and calculate the elevation estimation sensitivity of each other-angle image based on the main image; A sampling height determination unit, configured to determine a plurality of sampling heights according to the target height range of the simulation scenario; A correlation coefficient calculation unit, configured to calculate the correlation coefficient between each target point in the main image and its corresponding matching point in each other-angle image at each sampling height; A construction unit, configured to construct the single-angle elevation estimation probability density function of each target point in the main image and each other-angle image based on the elevation estimation sensitivity and the correlation coefficient at each sampling height; A second calculation unit, configured to calculate a height estimation value of each target point in the main image based on each single-angle elevation estimation probability density function; A third calculation unit, configured to calculate a three-dimensional position estimation result of each target point based on the focusing position and the height estimation value of each target point in the main image.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used for storing a computer program, and the processor is used for executing the computer program stored on the memory to implement the steps of the method according to any one of claims 1-7 above.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1-7 are implemented.

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