A method for resolving multiple strong scattering points in single pulse forward-looking imaging results

Abstract

A method for resolving multiple strong scattering points in a single pulse forward-looking imaging result. The present invention relates to a method for resolving target scattering points in a single pulse forward-looking imaging result. The purpose of the present invention is to solve the problem that the existing single pulse imaging azimuth processing method, such as maximum likelihood estimation, has poor resolution of target strong scattering points in the azimuth within the same distance unit. The process is: 1. Obtain the two-dimensional coordinates and corresponding intensity values ​​of each scattering point; 2. Assuming that there are N' strong scattering points, they are divided into "subsidiary scattering points" of the corresponding strong scattering points according to the minimum "distance"; update the coordinates and intensity values ​​of the strong scattering points; repeat the execution two K times to obtain the position and intensity value of each strong scattering point and each strong scattering point's "subsidiary scattering point"; 3. Take out each strong scattering point and the "subsidiary scattering point" within a certain distance range, remove the remaining points, and use them as the imaging result of N' strong scattering points. The present invention is used in the field of resolving multiple strong scattering points in single pulse forward-looking imaging results.

Description

Technical Field

[0001] The invention relates to a method for resolving target scattering points in a single pulse forward-looking imaging result. Background Art

[0002] The forward imaging area is an important sensing area in many battlefield environments in the military, and it is also extremely important in civilian use. Single-pulse forward imaging is an imaging algorithm that combines ISAR one-dimensional range processing with high resolution and single-pulse angle measurement with high resolution in azimuth. The single-pulse angle measurement technology uses at least two receiving antennas to independently receive echo signals. Through a certain algorithm, the echo signals are separated and compared from multiple antennas to obtain the angle information of the target. However, although the single-pulse technology has the characteristics of high-precision angle measurement, its own characteristics determine that it cannot distinguish multiple targets (i.e., multiple strong scattering points) in a beam. That is, a single-pulse radar can only identify one target within the same range unit and within a beam range, and cannot measure the angle of multiple targets at the same time. The existing azimuth multi-target resolution methods, such as maximum likelihood estimation, sometimes produce unsatisfactory results, and there is still a clustering of echo imaging of multiple strong scattering points of the target, which is difficult to distinguish. Multi-target resolution is of great significance to forward imaging, so it is very important to improve the resolution accuracy. Summary of the invention

[0003] The purpose of the present invention is to solve the problem that the existing single pulse imaging azimuth processing methods, such as maximum likelihood estimation, have poor resolution of strong scattering points of targets in the same distance unit, and to propose a method for resolving multiple strong scattering points in single pulse forward-looking imaging results.

[0004] A method for resolving multiple strong scattering points in a single pulse forward-looking imaging result. The specific process is as follows:

[0005] Step 1: Perform range processing and range migration correction on the single pulse forward-looking imaging radar echo data to obtain a one-dimensional range image; perform azimuth processing on the one-dimensional range image to obtain two-dimensional coordinates and corresponding intensity values ​​of N scattering points; N is a positive integer;

[0006] Step 2: Assuming there are N' strong scattering points, traverse and calculate the "distance" from each scattering point to the strong scattering point, and divide them into "subsidiary scattering points" of the corresponding strong scattering point according to the minimum "distance"; N' is a positive integer, and N'≤N;

[0007] Update the coordinates and intensity values ​​of strong scattering points;

[0008] Repeat step 2 K times to obtain the position and intensity value information of each strong scattering point and its "subordinate scattering point";

[0009] Step 3: Take out each strong scattering point and the "subsidiary scattering points" within a certain distance range, remove the remaining points, and take them as the imaging results of the last N' strong scattering points.

[0010] The beneficial effects of the present invention are:

[0011] The original single-pulse imaging azimuth target resolution is further improved, so that the azimuth resolution of the single-pulse scanning forward-looking imaging after maximum likelihood estimation is increased by about 50%, and its resolution effect is better displayed; by selecting specific distance units for resolution, the target calculation at the position of no interest can be removed, and only the target at the position of interest can be calculated, so as to achieve the purpose of reducing the amount of calculation; by modifying the final attached point deviation range, the resolution effect can be dynamically adjusted to suit different occasions; by adjusting the number of iterations, the calculation amount can be dynamically adjusted according to the resolution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of the present invention;

[0013] Figure 2 It is an iterative flow chart of a strong scattering point and its subsidiary scattering points of the present invention;

[0014] Figure 3 A maximum likelihood single pulse forward-looking imaging flow chart for inputting data to the present invention;

[0015] Figure 4 This is the maximum likelihood estimation flow chart;

[0016] Figure 5 is the antenna beam overlap diagram;

[0017] Figure 6 It is the ratio of difference and echo and the angle detection curve;

[0018] Figure 7 This is the result of traditional single pulse forward scanning imaging;

[0019] Figure 8a This is the maximum likelihood estimation result diagram assuming two strong scattering points at 1000Hz;

[0020] Figure 8b This is the maximum likelihood estimation result diagram assuming 3 strong scattering points at 1000Hz;

[0021] Figure 8c This is the maximum likelihood estimation result diagram assuming 4 strong scattering points at 1000Hz;

[0022] Figure 9a This is the result diagram after adding the present invention assuming two strong scattering points at 1000Hz;

[0023] Figure 9bThis is the result diagram after adding the present invention assuming 3 strong scattering points at 1000Hz;

[0024] Fig.9c This is the result diagram after assuming that 4 strong scattering points are added to the present invention at 1000Hz;

[0025] Fig.10a This is the maximum likelihood estimation result diagram assuming two strong scattering points at 2000Hz;

[0026] Fig.10b This is the maximum likelihood estimation result diagram assuming 3 strong scattering points at 2000Hz;

[0027] Fig.10c This is the maximum likelihood estimation result diagram assuming 4 strong scattering points at 2000Hz;

[0028] Fig.11a This is the result diagram after adding the present invention assuming two strong scattering points at 2000Hz;

[0029] Fig.11b This is the result diagram after assuming that three strong scattering points are added to the present invention at 2000Hz;

[0030] Fig.11c This is the result diagram after assuming that 4 strong scattering points are added to the present invention at 2000Hz;

[0031] Fig.12a This is the maximum likelihood estimation result diagram assuming two strong scattering points at 4000Hz;

[0032] Figure 12b This is the maximum likelihood estimation result diagram assuming 3 strong scattering points at 4000Hz;

[0033] Fig.12c This is the maximum likelihood estimation result diagram assuming 4 strong scattering points at 4000Hz;

[0034] Fig.13a This is the result diagram after adding the present invention assuming two strong scattering points at 4000Hz;

[0035] Fig.13b This is the result diagram after adding the present invention assuming 3 strong scattering points at 4000Hz;

[0036] Fig.13c This is the result diagram after assuming that 4 strong scattering points are added to the present invention at 4000Hz;

[0037] Fig.14a This is the maximum likelihood estimation result diagram assuming two strong scattering points at 8000Hz;

[0038] Fig.14b This is the maximum likelihood estimation result diagram assuming 3 strong scattering points at 8000Hz;

[0039] Fig.14c This is the maximum likelihood estimation result diagram assuming 4 strong scattering points at 8000Hz;

[0040] Fig.15a This is the result diagram after adding the present invention assuming two strong scattering points at 8000Hz;

[0041] Fig.15b This is the result diagram after assuming that three strong scattering points are added to the present invention at 8000Hz;

[0042] Fig.15c This is the result diagram after assuming that 4 strong scattering points are added to the present invention at 8000Hz;

[0043] Fig.16a This is the maximum likelihood estimation result diagram for a scanning range of 10 degrees;

[0044] Fig.16b This is the result diagram after adding the present invention to the scanning range of 10 degrees;

[0045] Fig.17a This is the maximum likelihood estimation result diagram for a scanning range of 4 degrees;

[0046] Fig.17b This is the result diagram after adding the present invention to the scanning range of 4 degrees;

[0047] Fig.18a This is the maximum likelihood estimation result diagram for the scanning range of 2 degrees;

[0048] Fig.18b This is the result diagram after adding the present invention to the scanning range of 2 degrees;

[0049] Fig.19a This is the maximum likelihood estimation result diagram for the scanning range of 1 degree;

[0050] Fig.19b This is the result diagram after adding the present invention to the scanning range of 1 degree. DETAILED DESCRIPTION

[0051] Specific implementation method 1: Combination Figure 1 , 2 , 3, and 4 illustrate this embodiment. The specific process of this embodiment is a method for resolving multiple strong scattering points in a single pulse forward-looking imaging result as follows:

[0052] Step 1: Perform range processing and range migration correction on the single pulse forward-looking imaging radar echo data to obtain a one-dimensional range image; perform azimuth processing on the one-dimensional range image, such as using maximum likelihood estimation or traditional single pulse azimuth angle measurement imaging, to obtain two-dimensional coordinates (px, py) and corresponding intensity values ​​(amplitude) of N scattering points; N is a positive integer;

[0053] Step 2: Figure 2 , assuming that there are actually N' strong scattering points, define "distance", traverse and calculate the "distance" from each scattering point to the strong scattering point, and divide it into "subordinate scattering points" of the corresponding strong scattering point according to the minimum "distance";

[0054] N' is a positive integer, and N'≤N;

[0055] Update the coordinates and intensity values ​​of strong scattering points;

[0056] Repeat step 2 K times to obtain more accurate information on the position and intensity value of each strong scattering point and its "subordinate scattering point";

[0057] Step 3: Take out each strong scattering point and the "subsidiary scattering points" within a certain distance range, remove the remaining points, and take them as the imaging results of the last N' strong scattering points.

[0058] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that, in step 1, the single pulse forward-looking imaging radar echo data is processed in range direction and range migration correction is performed to obtain a one-dimensional range image; the one-dimensional range image is processed in azimuth direction, such as by using maximum likelihood estimation or traditional single pulse azimuth angle measurement imaging, to obtain two-dimensional coordinates (px, py) and corresponding intensity values ​​(amplitude) of N scattering points; N is a positive integer;

[0059] The obtained N scattering points are actually the echoes of N' strong scattering points that are mixed together and cannot be separated after imaging; N' is a positive integer, and N'≤N;

[0060] The specific process is:

[0061] Step 1: The signal source transmits a linear frequency modulated pulse signal, which is received by two radar antennas (the two antennas are arranged in a single pulse angle measurement dual antenna arrangement) after being reflected by the target. The received signal is an echo signal, and the echo signal is processed into a sum beam signal and an azimuth difference beam signal (the sum beam signal is the sum of the two antenna echoes, and the difference beam signal is the difference between the two antenna echoes);

[0062] Step 1 and 2: Perform range processing (de-interpolation or corresponding matched filtering) on ​​the sum beam signal and the azimuth difference beam signal (difference sum echo) respectively, and then perform range migration correction to obtain a one-dimensional range image, such as Figure 3 ;

[0063] Step 13: Perform azimuth processing on the one-dimensional range image (the sum beam signal and the azimuth difference beam signal after range migration correction). The azimuth processing is a normal single pulse angle measurement or some improved azimuth method such as maximum likelihood estimation; Figure 4; Get the two-dimensional coordinates of each scattering point and the corresponding intensity value (get the coordinates of N initial scattering points in the mth distance unit and the intensity value (amplitude) of the corresponding scattering point).

[0064] The other steps and parameters are the same as those in the first embodiment.

[0065] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that in step one or three, the one-dimensional range image is processed in azimuth to obtain the two-dimensional coordinates of each scattering point and the corresponding intensity value; the specific process is:

[0066] For the maximum likelihood estimation in steps one and three, specifically:

[0067] 1) Select the sum beam signal and azimuth difference beam signal of the mth range unit of the one-dimensional range image;

[0068] 2) Construct the observation vector z(m) of the m-th distance unit of the one-dimensional distance image, and select the initial value of the parameter to be estimated g; the observation vector z(m) of the m-th distance unit of the one-dimensional distance image and the initial value of the parameter to be estimated g are:

[0069] z(m)=[s1(m) s2(m) d1(m) d2(m)] T

[0070] g=[α1 η1 … α N η N ] T

[0071] Wherein, m is the range unit number, m = 1, 2, ..., M, M is the total number of range units; s1(m) and d1(m) are the sum beam signal and azimuth difference beam signal values ​​corresponding to the sampling point on the left side of the m-th range unit (the sampling point is a series of points obtained after the echo signal is sampled, and the distance between two sampling points is a range unit; the scattering point is the point on the target that reflects the radar echo), s2(m) and d2(m) are the sum beam signal and azimuth difference beam signal values ​​corresponding to the sampling point on the right side of the m+1-th range unit, T is the transpose; η i is the azimuth difference beam signal value or sum beam signal value of the i-th scattering point; α i is the intermediate variable, Δt i is the time (Δt i Multiply by the speed of light c and divide by 2 to get the relative position), i = 1, 2, ..., N, N is the number of scattering points, T P is the pulse width;

[0072] The maximum likelihood function of the target is

[0073]

[0074] The constraints are

[0075]

[0076] Among them, LL(g) is the maximum likelihood function, f s is the sampling frequency, R is the covariance matrix, k ae is the slope of the angle discrimination curve (a parameter of the antenna. When the antenna is determined, the angle discrimination curve is determined), θ BW is the linear interval width of the azimuth difference beam signal or sum beam signal (difference beam / sum beam);

[0077] 3) Solve for the parameter vector g and get the estimated value That is, we get α i , η i An estimated value of

[0078]

[0079] 4) By α i , η i Calculate the position of the i-th scattering point and the corresponding intensity value; the specific process is:

[0080] The mth distance unit combined with the radar reference distance R ref (The distance between the radar and the reference point, that is, the reference distance for imaging is known) Calculate the x coordinate of the i-th scattering point (relative to the reference point) px i =x m +dx·T p f s α i , declination The y coordinate of the i-th scattering point py i =(R ref +px i )tan(θ i ), intensity value

[0081] In the formula, x m is the x-coordinate of the scattering point on the left side of the m-th distance unit (relative to the reference point), K is the modulation frequency of the transmitted linear frequency modulation signal, and sinc() is the Sinker function.

[0082] The other steps and parameters are the same as those in the first or second embodiment.

[0083] Specific implementation method 4: This implementation method is different from the specific implementation methods 1 to 3 in that in step 2, Figure 2, assuming that there are actually N' strong scattering points, define "distance", traverse and calculate the "distance" from each scattering point to the strong scattering point, and divide it into "subordinate scattering points" of the corresponding strong scattering point according to the minimum "distance";

[0084] N' is a positive integer, and N'≤N;

[0085] Update the coordinates and intensity values ​​of strong scattering points;

[0086] Repeat step 2 K times to obtain more accurate information on the position and intensity value of each strong scattering point and the "subsidiary scattering points" around each strong scattering point;

[0087] The specific process is:

[0088] Step 1) Assume that N scattering points are obtained by imaging the echoes of N' strong scattering points, and define the distance between the i-th scattering point and the j-th strong scattering point

[0089] Among them, (px i ,py i ) and pA i are the two-dimensional coordinates and corresponding intensity values ​​of the i-th scattering point, (x j ,y j ) is the two-dimensional coordinate of the j-th strong scattering point, N'≤N;

[0090] Step 2) randomly selecting N' strong scattering points from the N scattering points as strong scattering points, and recording the coordinates and intensity values ​​of the strong scattering points;

[0091] Step 3) Calculate the distance from the ith scattering point to each strong scattering point. If the distance from the ith scattering point to the jth strong scattering point is the smallest, the ith scattering point is classified as the "subordinate point" of the jth strong scattering point. Traverse the scattering points from 1 to N, and then obtain the "subordinate scattering points" corresponding to each strong scattering point.

[0092] Generally, the imaging distance resolution is high, and the number N' is about 1-4. The number of scattering points N obtained by imaging is determined by the pulse repetition rate, and the order of magnitude is hundreds or even thousands (see Figure 8a , 8b , 8c, 10a, 10b, 10c, 12a, 12b, 12c, 14a, 14b, 14c, the higher the repetition rate, the more scattering points are obtained).

[0093] Step 4) Update the coordinates of strong scattering points:

[0094] The number of "subsidiary scattering points" of the qth strong scattering point is N q , update the coordinates of the strong scattering point to Traverse q from 1-N' and complete the coordinate update of N' strong scattering points;

[0095] In the formula, and Respectively represent the coordinates and intensity values ​​of the ith subsidiary scattering point of the qth strong scattering point; N q represents the number of "subsidiary scattering points" of the qth strong scattering point; mean() represents the mean; (x q ',y q ') represents the updated strong scattering point coordinates;

[0096] Step 5) Repeat step 3) and step 4) K times to obtain more accurate position and intensity value information of each strong scattering point, and position and intensity value information of "subordinate scattering points" of each strong scattering point.

[0097] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0098] Specific implementation mode five: This implementation mode is different from any one of specific implementation modes one to four in that 5≤K≤10.

[0099] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0100] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that in step 3, each strong scattering point and the "subordinate" scattering points within a certain distance range are taken out, and the remaining points are removed as the imaging result of the last N' strong scattering points, specifically:

[0101] Take the coordinate range of the qth strong scattering point (x q '±D x ,y q '±D y ) as the imaging result after echo processing of the qth strong scattering point, remove other points to obtain the imaging result of the qth strong scattering point; traverse q from 1-N' to obtain the imaging results of N' strong scattering points;

[0102] Where D x , D y They are distance ranges respectively and can be flexibly selected according to resolution requirements.

[0103] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0104] The following examples are used to verify the beneficial effects of the present invention:

[0105] Embodiment 1:

[0106] The method for resolving multiple strong scattering points in a single pulse forward-looking imaging result in this embodiment is specifically prepared according to the following steps:

[0107] The radar platform moves towards the target. Assume that the movement direction of the radar platform is the positive direction of the x-axis, the speed Vx = 1000m / s, and the initial distance to the target is 5km. Azimuth beam width 2θ 0.5 =3°, antenna scanning range -0.5°~0.5°, antenna scanning speed 40° / s, rectangular horn aperture antenna, dual antenna beam deviation angle θ s =0.3·2θ 0.5 , angle detection interval (-1°, 1°), angle detection curve slope k ae = 0.2868. The dual antenna beam overlap (E plane) is as follows Figure 5 , the corresponding difference beam / sum beam and the corresponding angle detection curve are as follows Figure 6 The signal carrier frequency is 30GHz, the bandwidth is 300MHz, the pulse width is 1us, the pulse repetition frequency is 2000Hz, the sampling rate is 800MHz, and the signal-to-noise ratio of the sum and difference channels is 30dB.

[0108] (i) Maximum likelihood estimation and imaging simulation results after adding the present invention under different pulse repetition rates and different assumed numbers of strong scattering points;

[0109] The pulse repetition frequency determines the number of echoes accumulated within the scanning range and the amount of echo data used for iteration. The number of assumed strong scattering points determines how many strong scattering points and their attached scattering point sets are included in the obtained imaging results.

[0110] like Figure 7 This is the result of traditional single pulse scanning imaging. Figure 8a , Figure 8b , Figure 8c , Figure 9a , Figure 9b , Fig.9c , Fig.10a , Fig.10b , Fig.10c , Fig.11a , Fig.11b , Fig.11c , Fig.12a , Figure 12b , Fig.12c , Fig.13a , Fig.13b , Fig.13c , Fig.14a , Fig.14b , Fig.14c , Fig.15a , Fig.15b , Fig.15c The maximum likelihood estimation and the single pulse two-dimensional imaging results with the present invention assuming 2, 3, and 4 strong scattering points under pulse repetition frequencies PRF = 1000Hz, 2000Hz, 4000Hz, and 8000Hz, respectively. Figure 7The traditional single pulse forward scanning cannot distinguish the results of close-range targets at all. The results after maximum likelihood estimation can roughly reflect the distribution of the original target scattering points and fit the distribution shape of its scattering points, but the spacing between the scattering points is small, making it difficult to distinguish between each strong scattering point and the corresponding subsidiary scattering point. After the method of the present invention, the imaging results of each point are closer to the original target scattering point, and as the PRF increases, the results are closer and closer to the real point target position. This is because the increase in echo increases the amount of data used for optimization iteration, and the results of the present invention are closer to the real point.

[0111] In addition, after adding the present invention, it can be found that: compared with the assumed number of strong scattering points, when the pulse repetition rate increases to a certain extent, the improvement of the imaging effect is relatively insignificant; and the assumed number of strong scattering points has a greater impact on the imaging effect than the pulse repetition rate. Only when the assumed number is equal to the real number, the imaging error is relatively minimal; when the two are not equal, the imaging results are approximately distributed around the real results. Even so, in order to reduce the amount of calculation in actual application, it can be assumed that the number of strong scattering points in the same distance unit is 2 or 3, so that the scattering points can be roughly separated and the error is relatively small.

[0112] (II) Maximum likelihood estimation and imaging simulation results after adding the present invention under different scanning ranges

[0113] Fig.16a , Fig.16b , Fig.17a , Fig.17b , Fig.18a , Fig.18b , Fig.19a , Fig.19b The maximum likelihood estimation of scanning 10 degrees, 4 degrees, 2 degrees and 1 degree and the imaging results after adding the present invention respectively show that when the scanning angle is greater than or equal to 2 degrees, the estimated scattering point deviation becomes larger and the deviation is almost fixed. This is due to the error of the algorithm itself and the angle detection curve. When the beam scanning angle is large, the echo pulse will enter the receiver at some angles beyond the antenna linear region (-1 degree, 1 degree). Figure 6 As shown, the echoes in these nonlinear regions will have a certain impact on the subsequent maximum likelihood estimation and the results of the present invention, causing estimation deviation. That is, the maximum likelihood method is highly sensitive to non-target echoes or echoes in nonlinear regions, which can easily cause estimation deviation.

[0114] Although the present invention can improve the resolution of scattering points to a certain extent, since the input is the maximum likelihood estimation result, when the maximum likelihood result has a large deviation, the present invention will inevitably have a corresponding deviation.

[0115] In summary, the maximum likelihood estimation combined with the present invention can better distinguish multiple strong scattering points within the same distance unit compared to traditional single pulse imaging when the scanning range is small. When the pulse repetition rate increases, the imaging result can be closer to the actual position of the strong scattering point; when the number of targets is assumed to be equal to the number of actual strong scattering points, the imaging deviation is also minimal. However, when the scanning angle is too large or the noise and clutter are too strong, the maximum likelihood estimation will have a large estimation deviation, and the present invention based on its results will inevitably have corresponding deviations.

[0116] In addition, the present invention can also be applied to other single-pulse azimuth imaging results, and can improve their resolution capabilities to a certain extent.

[0117] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for resolving multiple strong scattering points in a single pulse forward-looking imaging result, characterized in that: The specific process of the method is: Step 1: Perform range processing and range migration correction on the single pulse forward-looking imaging radar echo data to obtain a one-dimensional range image; perform azimuth processing on the one-dimensional range image to obtain two-dimensional coordinates and corresponding intensity values ​​of N scattering points; N is a positive integer; Step 2: Assuming there are N' strong scattering points, traverse and calculate the "distance" from each scattering point to the strong scattering point, and divide them into "subsidiary scattering points" of the corresponding strong scattering point according to the minimum "distance"; N' is a positive integer, and N'≤N; Update the coordinates and intensity values ​​of strong scattering points; Repeat step 2 K times to obtain the position and intensity value information of each strong scattering point and its "subordinate scattering point"; Step 3: Take out each strong scattering point and the "subsidiary scattering points" within a certain distance range, remove the remaining points, and use them as the imaging results of the last N' strong scattering points.

2. The method for resolving multiple strong scattering points in a single pulse forward-looking imaging result according to claim 1, characterized in that: In the step 1, the single pulse forward-looking imaging radar echo data is processed in the range direction and corrected for range migration to obtain a one-dimensional range image; the one-dimensional range image is processed in the azimuth direction to obtain two-dimensional coordinates and corresponding intensity values ​​of N scattering points; N is a positive integer; The specific process is: Step 1: A signal source transmits a linear frequency modulated pulse signal, which is received by two radar antennas after being reflected by a target. The received signal is an echo signal, and the echo signal is processed to obtain a sum beam signal and an azimuth difference beam signal. Step 1 and 2, respectively perform range processing on the sum beam signal and the azimuth difference beam signal, and then perform range migration correction to obtain a one-dimensional range image; Step 13: Perform azimuth processing on the one-dimensional range image to obtain the two-dimensional coordinates of each scattering point and the corresponding intensity value.

3. A method for resolving multiple strong scattering points in a single pulse forward-looking imaging result according to claim 1 or 2, characterized in that: In the step 1-3, the one-dimensional range image is processed in azimuth to obtain the two-dimensional coordinates of each scattering point and the corresponding intensity value; The specific process is: 1) Select the sum beam signal and azimuth difference beam signal of the mth range unit of the one-dimensional range image; 2) Construct the observation vector z(m) of the m-th distance unit of the one-dimensional distance image and select the initial value of the parameter g to be estimated; The observation vector z(m) of the mth distance unit of the one-dimensional distance image and the initial values ​​of the estimated parameter g are: z(m)=[s1(m) s2(m) d1(m) d2(m)] T g=[α1 η1 … α N or N ] T Where m is the range unit number, m = 1, 2, ..., M, M is the total number of range units; s1(m) and d1(m) are the sum beam signal and azimuth difference beam signal values ​​corresponding to the sampling point on the left side of the m-th range unit, s2(m) and d2(m) are the sum beam signal and azimuth difference beam signal values ​​corresponding to the sampling point on the right side of the m+1-th range unit, and T is the transpose. η i is the azimuth difference beam signal value or sum beam signal value of the i-th scattering point; α i is the intermediate variable, Δt i is the time of the i-th scattering point relative to the sampling point m, i = 1, 2, ..., N, N is the number of scattering points, T P is the pulse width; The maximum likelihood function of the target is The constraints are Among them, LL(g) is the maximum likelihood function, f s is the sampling frequency, R is the covariance matrix, k ae is the slope of the angle detection curve, θ BW is the linear interval width of the azimuth difference beam signal or the sum beam signal; 3) Solve for the parameter vector g and get the estimated value That is, we get α i , η i An estimated value of 4) By α i , η i Calculate the position and corresponding intensity value of the i-th scattering point; The specific process is: The mth distance unit combined with the radar reference distance R ref Calculate the x coordinate px of the i-th scattering point i =x m +dx·T p f s α i , declination The y coordinate of the i-th scattering point py i =(R ref +px i )tan(θ i ), intensity value In the formula, x m is the x-coordinate of the scattering point on the left side of the m-th distance unit, K is the modulation frequency of the transmitted linear frequency modulation signal, and sinc() is the Sinker function.

4. The method for resolving multiple strong scattering points in a single pulse forward-looking imaging result according to claim 3, characterized in that: In the step 2, it is assumed that there are N' strong scattering points, and the "distance" from each scattering point to the strong scattering point is calculated, and the points are divided into "subsidiary scattering points" of the corresponding strong scattering point according to the minimum "distance"; N' is a positive integer, and N'≤N; Update the coordinates and intensity values ​​of strong scattering points; Repeat step 2 K times to obtain the position and intensity value information of each strong scattering point and the "subsidiary scattering points" around each strong scattering point; The specific process is: Step 1) Assume that N scattering points are obtained by imaging the echoes of N' strong scattering points, and define the distance between the i-th scattering point and the j-th strong scattering point Among them, (px i ,py i ) and pA i are the two-dimensional coordinates and corresponding intensity values ​​of the i-th scattering point, (x j ,y j ) is the two-dimensional coordinate of the j-th strong scattering point, N'≤N; Step 2) randomly selecting N' strong scattering points from the N scattering points as strong scattering points, and recording the coordinates and intensity values ​​of the strong scattering points; Step 3) Calculate the distance from the ith scattering point to each strong scattering point. If the distance from the ith scattering point to the jth strong scattering point is the smallest, the ith scattering point is classified as the "subordinate point" of the jth strong scattering point. Traverse the scattering points from 1 to N, and the "subordinate scattering points" corresponding to each strong scattering point can be obtained. Step 4) Update the coordinates of strong scattering points: The number of "subsidiary scattering points" of the qth strong scattering point is N q , update the coordinates of the strong scattering point to Traverse q from 1-N' and complete the coordinate update of N' strong scattering points; In the formula, and Respectively represent the coordinates and intensity values ​​of the ith subsidiary scattering point of the qth strong scattering point; N q represents the number of "subsidiary scattering points" of the qth strong scattering point; mean() represents the mean; (x q ',y q ') represents the updated strong scattering point coordinates; Step 5) Repeat step 3) and step 4) K times to obtain the position and intensity value information of each strong scattering point, and the position and intensity value information of the "subordinate scattering point" of each strong scattering point.

5. The method for resolving multiple strong scattering points in a single pulse forward-looking imaging result according to claim 4, characterized in that: Said 5≤K≤10.

6. The method for resolving multiple strong scattering points in a single pulse forward-looking imaging result according to claim 5, characterized in that: In step 3, each strong scattering point and the "subordinate" scattering points within a certain distance range are taken out, and the remaining points are removed as the imaging result of the last N' strong scattering points, which is specifically: Take the coordinate range of the qth strong scattering point (x q '±D x ,y q '±D y ) as the imaging result after echo processing of the qth strong scattering point, remove other points to obtain the imaging result of the qth strong scattering point; traverse q from 1-N' to obtain the imaging results of N' strong scattering points; Where D x , D y They are distance ranges respectively.

Images