Space target joint imaging and attitude inversion method based on spaceborne platform

Abstract

The application relates to a space target joint imaging and attitude inversion method based on a spaceborne platform, and relates to the field of inverse synthetic aperture radar imaging, in particular to a space target joint imaging and attitude inversion method based on a spaceborne platform. The application aims to solve the problems that in the existing space target imaging process of a spaceborne platform, three-dimensional high-order space-variable range migration and phase errors caused by complex relative motion between the target and the platform are difficult to realize high-precision imaging, and the existing attitude estimation algorithm needs to be completely compensated for the rectangular component segmentation and orbit error before the attitude inversion can be realized. The process is as follows: one, a narrowband tracking system of a radar obtains target slant range and a radar line-of-sight unit vector; two, a wedge-shaped transformed signal is obtained; three, spatial Doppler parameter distribution of a special point is obtained; four, an imaging result is obtained; and five, accurate estimation of target attitude parameters is completed through plane coefficient fitting.

Description

Technical Field

[0001] This invention relates to the field of inverse synthetic aperture radar imaging (ISAR), and in particular to a method for joint imaging and attitude inversion of space targets based on a spaceborne platform. Background Technology

[0002] Imaging of space targets has always been a hot topic in the field of ISAR imaging. Due to atmospheric interference and orbital obstruction, traditional ground-based radar and optical systems face inherent limitations in tracking space targets. Spaceborne inverse synthetic aperture radar (ISAR), due to its ability to provide all-weather, long-term observation capabilities, has become a key technology for characterizing space targets. To achieve more comprehensive situational awareness of space targets, high-precision imaging and attitude inversion are required. Existing high-precision imaging algorithms do not analyze the imaging characteristics of space targets, and the mapping relationship between the scattering points of space targets and Doppler parameters needs further analysis. At the same time, attitude inversion algorithms rely on rectangular component segmentation, and in the subsequent plane coefficient estimation, they often ignore orbital errors and the complex Doppler effects brought about by high imaging resolution. This invention proposes a joint high-resolution imaging and attitude inversion algorithm for high-dynamic Doppler space targets by estimating the spatial distribution of Doppler parameters, regionalized uniform motion compensation, and principal component analysis to directly fit the plane coefficients and invert the target attitude. Summary of the Invention

[0003] The purpose of this invention is to solve the problems of distance migration and phase error caused by the complex relative motion between the target and the platform in the process of imaging space targets by existing spaceborne platforms, which makes it difficult to achieve high-precision imaging; and the problem that existing attitude estimation algorithms can only achieve attitude inversion under the premise of rectangular component segmentation and complete orbit error compensation. Therefore, this invention proposes a method for joint imaging and attitude inversion of space targets based on spaceborne platforms.

[0004] The specific process of the method for joint imaging and attitude inversion of space targets based on a spaceborne platform is as follows:

[0005] Step 1: The radar's narrowband tracking system acquires the target slant range and radar line-of-sight unit vector;

[0006] Step 2: Perform translational compensation on the received signal based on the target slant range in Step 1 to obtain the translationally compensated signal. Then, perform a wedge transformation on the translationally compensated signal to obtain the wedge-transformed signal.

[0007] Step 3: Perform time truncation on the wedge-transformed signal obtained in Step 2 to obtain the truncated signal. Perform imaging processing on the truncated signal to obtain coarse focused image 1.

[0008] Using an adaptive template matching method, prominent points are extracted from the coarse focused image 1, and the spatial Doppler parameter distribution of the prominent points is obtained.

[0009] Step 4: Perform interpolation processing on the spatial Doppler parameter distribution results of the prominent points obtained in Step 3 to obtain the complete spatial Doppler parameter distribution results on the image projection plane;

[0010] Based on the complete spatial Doppler parameter distribution results, the real-time phase change of each region in the image is obtained;

[0011] The wedge-transformed signal obtained in step two is processed for imaging to obtain coarse focused image 2. Based on real-time phase change, coarse focused image 2 is divided into sub-regions to obtain the coordinate range of each region in the image domain.

[0012] Based on the coordinate range of each region, the local target signal corresponding to each region is obtained and compensated to obtain the compensated local target signal. Based on the compensated local target signal of each region, the compensated complete target signal is obtained.

[0013] Imaging results are obtained based on the compensated complete target signal;

[0014] Step 5: Using the radar line-of-sight unit vector Taylor expansion results obtained in Step 1, the spatial Doppler parameter distribution results of the prominent points obtained in Step 3, and the imaging results obtained in Step 4, the target attitude parameters are accurately estimated by fitting the plane coefficients.

[0015] Preferably, in step one, the narrowband tracking system of the radar acquires the target slant range and the radar line-of-sight unit vector; the specific process is as follows:

[0016] Based on the radar's narrowband tracking system

[0017] The radar's narrowband tracking system acquires the target slant range R. T (t m ), elevation angle of radar line of sight Azimuth η l (t m )parameter;

[0018] Among them, t m This refers to the azimuth dimension in the time domain.

[0019] t m =mT r T r This represents the pulse repetition period, where M is the number of accumulated pulses, and m = 0, 1, ..., M-1;

[0020] The target slant range is expressed using Taylor expansion as follows:

[0021]

[0022] in,

[0023] T0 is R T (t m The coefficients of the 0th order Taylor series expansion of ).

[0024] T1 is R T (t m The first-order Taylor series expansion coefficients of ).

[0025] T2 is R T (t m The coefficients of the second-order Taylor series expansion of ).

[0026] O T (t m () indicates a higher-order term that can be ignored;

[0027] Radar line-of-sight unit vector i los (t m ) is represented as:

[0028]

[0029] For radar line-of-sight unit vector i los (t m Performing a Taylor expansion, we get:

[0030]

[0031] in,

[0032] i0 is i los (t m The coefficients of the 0th order Taylor series expansion of ).

[0033] i1 is i los (t m The first-order Taylor series expansion coefficients of ).

[0034] i2 is i los (t m The coefficients of the second-order Taylor series expansion of ).

[0035] O los (t m ) indicates a higher-order term that can be ignored.

[0036] Preferably, in step two, the received signal is compensated for translational motion based on the target slant range in step one to obtain a translationally compensated signal, and then a wedge transformation is performed on the translationally compensated signal to obtain a wedge-transformed signal; the specific process is as follows:

[0037] Step Two One:

[0038] The ISAR system transmits a linear frequency modulated (LFM) signal, which is then pulse-compressed to obtain the pulse-compressed target echo spectrum s(f). r ,t m The target echo spectrum s(f) after pulse compression r ,t m The following is represented:

[0039]

[0040] in,

[0041] N represents the number of scattering points on the target;

[0042] σ p This represents the amplitude of the linear frequency modulated signal transmitted by the ISAR system;

[0043] f r Represents the frequency of the distance dimension;

[0044] B represents the bandwidth of the linear frequency modulated signal transmitted by the ISAR system;

[0045] f c Indicates the carrier frequency;

[0046] λ represents the radar wavelength;

[0047] rect(·) represents a rectangular window;

[0048] j represents the imaginary unit, j 2 =-1;

[0049] R p (t m The distance migration of the scattering point p relative to the radar is represented as:

[0050] R p (t m ) = R T (t m )+R Rot,p (t m )

[0051] R Rot,p (t m ) = i los (t m )·p T

[0052] in,

[0053] R Rot,p (t m ) represents the rotational distance migration of the scattering point p relative to the radar;

[0054] • Represents vector product;

[0055] p represents the three-dimensional coordinate vector of the scattering point p of the target;

[0056] The superscript T indicates transpose;

[0057] Step 22: Based on the target slant range R in Step 1 T (t m The Taylor series expansion coefficients of f are used to construct the compensation term H(f). r ,t m );

[0058] Steps two and three: The compensation term H(f) r ,t m ) and the target echo spectrum after pulse compression s(f r ,t m Multiply by , and obtain the translationally compensated signal s'(f) r ,t m );

[0059] Step 24: For the translationally compensated signal s'(f) r ,t m Perform a wedge transform to obtain the wedge-transformed signal s'(f) r ,τ m ).

[0060] Preferably, in step two, the target slant range R from step one is used as the basis. T (t m The Taylor series expansion coefficients of f are used to construct the compensation term H(f). r ,t m The specific process is as follows:

[0061]

[0062] in,

[0063] c represents the speed of light.

[0064] Preferably, in steps two and three, the compensation term H(f) is... r ,t m ) and the target echo spectrum after pulse compression s(f r ,t m Multiply by , and obtain the translationally compensated signal s'(f) r ,t m ); is represented as:

[0065] s'(f r ,t m )=s(f r ,t m )×H(f r ,tm ).

[0066] Preferably, in step two or four, the translationally compensated signal s'(f) r ,t m Perform a wedge transform to obtain the wedge-transformed signal s'(f) r ,τ m );

[0067] in,

[0068] τ m τ represents the time component after the wedge transformation. m =(f r +f c ) / f c t m .

[0069] Preferably, in step three, the wedge-transformed signal obtained in step two is truncated over time to obtain a truncated signal, and the truncated signal is subjected to imaging processing to obtain a coarse focused image 1;

[0070] Using an adaptive template matching method, prominent points are extracted from the coarse focused image 1, and the spatial Doppler parameter distribution of the prominent points is obtained.

[0071] The specific process is as follows:

[0072] Step 31:

[0073] The criterion is that the second-order distance migration within the intercepted time period is less than c / 2B;

[0074] According to the criteria, the signal s'(f) after wedge transformation is... r ,τ m Extract the first time interval and obtain the extracted signal s' p (f r ,τ m );

[0075] Step 32:

[0076] The truncated signal s' p (f r ,τ m Perform inverse Fourier transform in the distance dimension and Fourier transform in the orientation dimension sequentially to obtain the coarse focused image 1S'. p (t r ,f m );

[0077] Among them, t r f represents the time dimension of the distance dimension. m Indicates the frequency of the azimuth dimension;

[0078] Step 33:

[0079] Using an adaptive template matching method to coarsely focused image 1S' p (t r ,f m Extract the prominent points in the data and obtain the spatial Doppler parameter distribution of all prominent points;

[0080] The spatial Doppler parameter distribution of all prominent points is the set of zeroth and first-order Doppler parameters of all extracted prominent points {(D 0,p D 1,p p = 1, 2, ..., N L ;

[0081] N L The number of extracted highlight points;

[0082] D 0,p This is the set of zero-order Doppler parameters for all extracted prominent points;

[0083] D 1,p This is the set of first-order Doppler parameters for all extracted prominent points;

[0084] Steps three and four:

[0085] The azimuth dimension signal corresponding to each prominent point extracted in step three is extracted using an image domain filter; the specific process is as follows:

[0086] The azimuth signal corresponding to the p-th prominent point extracted in step three is extracted using an image domain filter, denoted as s'. p (τ m );

[0087] Step 35:

[0088] Estimate s' using ICPF p (τ m The modulation frequency k 2,p ;

[0089] Based on s' p (τ m The modulation frequency k 2,p Obtain the second-order Doppler parameter D corresponding to the salient point p. 2,p ; indicates as:

[0090]

[0091] Among them, f r,p The fast time frequency corresponding to the distance dimension of the prominent point p is...

[0092] Among them, T lThis indicates the pulse width of the linear frequency modulated signal transmitted by the ISAR system;

[0093] Step 36: Repeat steps 34 to 35 to obtain the second-order Doppler parameters D corresponding to all prominent points. 2,p p = 1, 2, ..., N L ;

[0094] Step 37: Based on the set of zeroth and first-order Doppler parameters of all distinctive points extracted in Step 33 {(D 0,p D 1,p p = 1, 2, ..., N L The second-order Doppler parameters D corresponding to all the prominent points obtained in step 36. 2,p p = 1, 2, ..., N L ; Obtain the spatial Doppler parameter set of all prominent points {(D 0,p D 1,p D 2,p p = 1, 2, ..., N L .

[0095] Preferably, in step four, the spatial Doppler parameter distribution results of the prominent points obtained in step three are interpolated to obtain the complete spatial Doppler parameter distribution results on the image projection plane;

[0096] Based on the complete spatial Doppler parameter distribution results, the real-time phase change of each region in the image is obtained;

[0097] The wedge-transformed signal obtained in step two is processed for imaging to obtain coarse focused image 2. Based on real-time phase change, coarse focused image 2 is divided into sub-regions to obtain the coordinate range of each region in the image domain.

[0098] Based on the coordinate range of each region, the local target signal corresponding to each region is obtained and compensated to obtain the compensated local target signal. Based on the compensated local target signal of each region, the compensated complete target signal is obtained.

[0099] Imaging results are obtained based on the compensated complete target signal;

[0100] The specific process is as follows:

[0101] Step 41:

[0102] The second-order Doppler parameters D corresponding to all prominent points obtained in step three are calculated using the natural neighbor interpolation method based on triangulation. 2,p p = 1, 2, ..., N L Interpolation is performed to obtain the interpolated second-order Doppler parameters D2;

[0103] Step 42: Obtain the real-time phase change based on the interpolated second-order Doppler parameter D2. Represented as:

[0104]

[0105] Step 43: Constructing a coarse-focused image 2S'(t) r ,f m The specific process is as follows:

[0106] The wedge-transformed signal s'(f) obtained in step two r ,τ m Performing IFT in the distance dimension and FT in the orientation dimension sequentially yields the coarse focused image 2S'(t). r ,f m );

[0107] Step 44: When the real-time phase changes When the value is less than π / 4, the coarse-focused image 2S'(t) is divided according to the adaptive partitioning principle. r ,f m Divide it into Q sub-regions;

[0108] The image corresponding to the q-th sub-region is represented as S' q (t r ,f m );

[0109] To S' q (t r ,f m Perform Fourier Transform (FT) in the range dimension and Inverse Fourier Transform (IFT) in the azimuth dimension sequentially to obtain the signal s' corresponding to the q-th sub-region. q (f r ,t m );

[0110] Steps four and five: Construct the compensation term h for the signal corresponding to the q-th sub-region. q (f r ,τ m ); is represented as:

[0111]

[0112] in, The average value of the second-order Doppler parameters in the q-th sub-region;

[0113] Step Four Six:

[0114] The compensation term h of the signal corresponding to the q-th sub-region constructed in steps four and five. q (f r ,τ m The signal s' corresponding to the q-th sub-region obtained in step 4.4q (f r ,t m Multiply by , and obtain the compensated signal for the q-th sub-region;

[0115] Step 47:

[0116] Repeat steps four and five to four and six to obtain the complete target signal after compensation for all sub-regions:

[0117]

[0118] Step Four Eight:

[0119] The complete target signal s after compensation of all sub-regions t (f r ,τ m The target imaging results are obtained by sequentially performing IFT in the range dimension and FT in the azimuth dimension.

[0120] Preferably, in step five, the target attitude parameters are accurately estimated by fitting plane coefficients using the Taylor expansion result of the radar line-of-sight unit vector obtained in step one, the spatial Doppler parameter distribution result of the prominent point obtained in step three, and the imaging result obtained in step four; the specific process is as follows:

[0121] Step 51:

[0122] The Doppler parameter distribution results {(D 0,p D 1,p D 2,p p = 1, 2, ..., N L Generate point cloud data D, where the three-dimensional coordinates of point cloud data D are Di, ... 0,p p = 1, 2, ..., N L D 1,p p = 1, 2, ..., N L D 2,p p = 1, 2, ..., N L ;

[0123] D 0,p D is the x-axis coordinate. 1,p D is the y-axis coordinate. 2,p Z-axis coordinate;

[0124] Step 52: Let the iteration number i = 1;

[0125] Step 53: Use principal component analysis to perform plane fitting on the point cloud data D to obtain the fitting plane and plane coefficients C for the i-th iteration. 0,i C 1,i ,1,C bias,i ;

[0126] in,

[0127] C 0,i D 0,p The plane coefficient;

[0128] C 1,i D 1,p The plane coefficient;

[0129] 1 is D 2,p The plane coefficient;

[0130] C bias,i For plane coefficient constants;

[0131] Step 54: Calculate N obtained in Step 3 L The Euclidean distance d from each prominent point to the fitted plane p p = 1, 2, ..., N L ; indicates as:

[0132]

[0133] Step 55: Calculate the standard deviation σ of the Euclidean distance; expressed as:

[0134]

[0135] in, N is the mean of the Euclidean distance. i The number of highlighted points in the i-th iteration;

[0136] Steps five and six: Keep d p For each prominent point less than σ, let the iteration count i = i + 1, and update the number of prominent points and the point cloud data. Repeat steps 5.3 to 5.5 until the number of remaining prominent points is less than three or the Euclidean distance of each retained prominent point satisfies d. p ≥σ;

[0137] The plane coefficients fitted to all prominent points are obtained and denoted as {(C 0,i C 1,i ,1,C bias,i )}, i=1,2,...,N C ;

[0138] in,

[0139] N C The number of fitted planes;

[0140] Step 57: Calculate the normal vector direction n corresponding to the i-th plane. i :

[0141]

[0142] Step 58:

[0143] In N C From the fitted planes, select the normal vector corresponding to the solar wing plane.

[0144] in,

[0145] The superscript T indicates transpose;

[0146] Step 59:

[0147] The unit vector l of the longer side of the solar array is marked on the high-precision imaging result of the target obtained in step four. 1,proj unit vector l of the shorter side 2,proj ;

[0148] Step 50: Unit vector l based on the long side of the solar array 1,proj unit vector l of the shorter side 2,proj Calculate the three-dimensional vector l1 corresponding to the long side and the three-dimensional vector l2 corresponding to the short side of the solar array;

[0149] At this point, the target attitude estimation result consists of the three-dimensional vector l1 corresponding to the long side of the solar array, the three-dimensional vector l2 corresponding to the short side, and the normal vector. The direction is indicated.

[0150] Preferably, in step fifty, the unit vector l is based on the long side of the solar array. 1,proj unit vector l of the shorter side 2,proj Calculate the three-dimensional vector l1 corresponding to the long side and the three-dimensional vector l2 corresponding to the short side of the solar array; expressed as:

[0151]

[0152] in,

[0153] |·|2 represents the second norm of the vector being solved;

[0154] l 1,proj (1) indicates l 1,proj The first element; l 1,proj (2) indicates l 1,proj The second element; l 2,proj (1) indicates l 2,proj The first element; l 2,proj (2) indicates l 2,proj The second element;

[0155] inv() represents the inverse of a matrix;

[0156] The superscript T indicates transpose.

[0157] The beneficial effects of this invention are as follows:

[0158] This invention proposes a joint imaging and attitude inversion method for space targets based on a spaceborne platform. This invention relates to the field of inverse synthetic aperture radar (ISAR) imaging, and particularly to an ISAR imaging and attitude inversion method for a spaceborne platform. This invention primarily addresses the key issues of high-order three-dimensional spatially varied range migration and phase errors caused by complex relative motion when imaging space targets from a spaceborne platform, as well as the comprehensive utilization of image phase information. The main contents of this invention include: firstly, establishing a mapping model between target Doppler parameters and image domain scattering points, and further estimating and reconstructing the spatial distribution of spatially varied Doppler parameters; then, designing an adaptive region segmentation mechanism to achieve regional consistency compensation for three-dimensional spatiotemporal errors, obtaining high-resolution imaging results; simultaneously, obtaining an explicit correlation between Doppler parameters and satellite attitude based on the characteristics of satellite planar components, and using principal component analysis to directly fit the plane coefficients to invert the target attitude.

[0159] This invention proposes a method for joint imaging and attitude inversion of space targets based on a spaceborne platform. First, a mapping model between the target's Doppler parameters and the scattering points in the image domain is established for the preprocessed signal. Then, the spatial distribution of the spatially varied Doppler parameters is estimated and reconstructed. Next, an adaptive region segmentation mechanism is designed to achieve regional consistency compensation for three-dimensional spatiotemporal errors, significantly improving imaging resolution. At the same time, based on the characteristics of satellite planar components, the explicit correlation between Doppler parameters and satellite attitude is obtained, and the target attitude is inverted by directly fitting the planar coefficients using principal component analysis, avoiding reliance on traditional rectangular component segmentation. Attached Figure Description

[0160] Figure 1 This is a flowchart of the present invention;

[0161] Figure 2 This is a relative orbital diagram;

[0162] Figure 3(a) shows the RD image after translational compensation (attitude 1).

[0163] Figure 3(b) shows the RD image after KT transformation (pose 1);

[0164] Figure 3(c) shows the high-precision imaging results (attitude 1);

[0165] Figure 3(d) shows the RD image after translational compensation (attitude 2);

[0166] Figure 3(e) shows the RD image after KT transformation (attitude 2);

[0167] Figure 3(f) shows the high-precision imaging result (attitude 2);

[0168] Figure 4(a) shows the RD image after translational compensation (Case 1);

[0169] Figure 4(b) RD image after KT transformation (Case 1);

[0170] Figure 4(c) High-precision imaging results (Case 1);

[0171] Figure 4(d) RD image after translational compensation (Case 2);

[0172] Figure 4(e) RD image after KT transformation (Case 2);

[0173] Figure 4(f) High-precision imaging result (Case 2). Detailed Implementation

[0174] Specific Implementation Method 1: The specific process of this implementation method for joint imaging and attitude inversion of space targets based on a spaceborne platform is as follows:

[0175] Step 1: The radar's narrowband tracking system acquires the target slant range and radar line-of-sight unit vector;

[0176] Step 2: Perform translational compensation on the received signal based on the target slant range in Step 1 to obtain the translationally compensated signal. Then, perform a wedge transformation on the translationally compensated signal to obtain the wedge-transformed signal.

[0177] Step 3: Perform time truncation on the wedge-transformed signal obtained in Step 2 to obtain the truncated signal. Perform imaging processing on the truncated signal to obtain coarse focused image 1.

[0178] Using an adaptive template matching method (such as a multi-scale Laplacian of Gaussian (LOG) detector), prominent points are extracted from the coarse focused image 1, and the spatial Doppler parameter distribution of the prominent points is obtained.

[0179] Step 4: Perform interpolation processing on the spatial Doppler parameter distribution results of the prominent points obtained in Step 3 to obtain the complete spatial Doppler parameter distribution results on the image projection plane;

[0180] Based on the complete spatial Doppler parameter distribution results, the real-time phase change of each region in the image is obtained;

[0181] The wedge-transformed signal obtained in step two is processed for imaging to obtain coarse focused image 2. Based on real-time phase change, coarse focused image 2 is divided into sub-regions to obtain the coordinate range of each region in the image domain.

[0182] Based on the coordinate range of each region, the local target signal corresponding to each region is obtained and compensated to obtain the compensated local target signal. Based on the compensated local target signal of each region, the compensated complete target signal is obtained.

[0183] High-precision imaging results are obtained based on the compensated complete target signal;

[0184] Step 5: Using the radar line-of-sight unit vector Taylor expansion results obtained in Step 1, the spatial Doppler parameter distribution results of the prominent points obtained in Step 3, and the high-precision imaging results obtained in Step 4, the target attitude parameters are accurately estimated by fitting the plane coefficients.

[0185] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that, in step one, the radar's narrowband tracking system acquires the target slant range and the radar line-of-sight unit vector.

[0186] The specific process is as follows:

[0187] Based on the radar's narrowband tracking system

[0188] The radar's narrowband tracking system acquires the target slant range R. T (t m ), elevation angle of radar line of sight Azimuth η l (t m ) and other parameters;

[0189] Among them, t m This refers to the azimuth dimension in the time domain.

[0190] t m =mT r T r This represents the pulse repetition period, where M is the number of accumulated pulses, and m = 0, 1, ..., M-1;

[0191] The target slant range is expressed using Taylor expansion as follows:

[0192]

[0193] in,

[0194] T0 is R T (t m The coefficients of the 0th order Taylor series expansion of ).

[0195] T1 is R T (t m The first-order Taylor series expansion coefficients of ).

[0196] T2 is R T (t m The coefficients of the second-order Taylor series expansion of ).

[0197] O T (t m () indicates a higher-order term that can be ignored;

[0198] Radar line-of-sight unit vector ilos (t m ) is represented as:

[0199]

[0200] For radar line-of-sight unit vector i los (t m Performing a Taylor expansion, we get:

[0201]

[0202] in,

[0203] i0 is i los (t m The coefficients of the 0th order Taylor series expansion of ).

[0204] i1 is i los (t m The first-order Taylor series expansion coefficients of ).

[0205] i2 is i los (t m The coefficients of the second-order Taylor series expansion of ).

[0206] O los (t m ) indicates a higher-order term that can be ignored.

[0207] The other steps and parameters are the same as in Specific Implementation Method 1.

[0208] Specific Implementation Method 3: This implementation method differs from Specific Implementation Method 1 or 2 in that, in step 2, the received signal is compensated for translational motion based on the target slant range in step 1 to obtain the compensated translational signal, and then a wedge transformation is performed on the compensated translational signal to obtain the wedge-transformed signal.

[0209] The specific process is as follows:

[0210] Step Two One:

[0211] The ISAR system transmits a linear frequency modulated (LFM) signal, which is then pulse-compressed to obtain the pulse-compressed target echo spectrum s(f). r ,t m The target echo spectrum s(f) after pulse compression r ,t m The following is represented:

[0212]

[0213] in,

[0214] N represents the number of scattering points on the target;

[0215] σ p This represents the amplitude of the linear frequency modulated signal transmitted by the ISAR system;

[0216] f r Represents the frequency of the distance dimension;

[0217] B represents the bandwidth of the linear frequency modulated signal transmitted by the ISAR system;

[0218] f c Indicates the carrier frequency;

[0219] λ represents the radar wavelength;

[0220] rect(·) represents a rectangular window;

[0221] j represents the imaginary unit, j 2 =-1;

[0222] R p (t m The distance migration of the scattering point p relative to the radar is represented as:

[0223] R p (t m ) = R T (t m )+R Rot,p (t m )

[0224] R Rot,p (t m ) = i los (t m )·p T

[0225] in,

[0226] R Rot,p (t m ) represents the rotational distance migration of the scattering point p relative to the radar;

[0227] • Represents vector product;

[0228] p represents the three-dimensional coordinate vector of the scattering point p of the target;

[0229] The superscript T indicates transpose;

[0230] Step 22: Based on the target slant range R in Step 1 T (t m The Taylor series expansion coefficients of f are used to construct the compensation term H(f). r ,t m );

[0231] Steps two and three: The compensation term H(f) r ,tm ) and the target echo spectrum after pulse compression s(f r ,t m Multiply by , and obtain the translationally compensated signal s'(f) r ,t m );

[0232] Step 24: For the translationally compensated signal s'(f) r ,t m Perform a wedge transform to obtain the wedge-transformed signal s'(f) r ,τ m ).

[0233] Other steps and parameters are the same as in specific implementation method one or two.

[0234] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that, in step two, the target slant range R from step one is used as a basis. T (t m The Taylor series expansion coefficients of f are used to construct the compensation term H(f). r ,t m The specific process is as follows:

[0235]

[0236] in,

[0237] c represents the speed of light.

[0238] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0239] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that, in steps two and three, the compensation term H(f) is... r ,t m ) and the target echo spectrum after pulse compression s(f r ,t m Multiply by , and obtain the translationally compensated signal s'(f) r ,t m ); is represented as:

[0240] s'(f r ,t m )=s(f r ,t m )×H(f r ,t m )

[0241] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0242] Specific Implementation Method Six: This implementation method differs from Specific Implementation Methods One to Five in that, in step two and four, the translationally compensated signal s'(f) r ,t m Perform a wedge transform to obtain the wedge-transformed signal s'(f) r ,τ m );

[0243] in,

[0244] τ m τ represents the time component after the wedge transformation. m =(f r +f c ) / f c t m .

[0245] The other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0246] Specific Implementation Method Seven: This implementation method differs from one of the specific implementation methods one to six in that, in step three, the wedge-transformed signal obtained in step two is truncated over time to obtain the truncated signal, and the truncated signal is subjected to imaging processing to obtain a coarse focused image 1.

[0247] Using an adaptive template matching method (such as a multi-scale Laplacian of Gaussian (LOG) detector), prominent points are extracted from the coarse focused image 1, and the spatial Doppler parameter distribution of the prominent points is obtained.

[0248] The specific process is as follows:

[0249] Step 31:

[0250] After the processing in step two, there is still a high-order spatial range migration in the echo. To avoid the impact of this error on subsequent parameter estimation, the range of the second-order range migration can be estimated based on the changes in radar line of sight and the average size of the space target, with the second-order range migration within the intercept time period being less than c / 2B as the criterion.

[0251] According to the criteria, the signal s'(f) after wedge transformation is... r ,τ m Extract the first time interval (according to the criteria, transform the wedge-shaped signal s'(f)). r ,τ m (Extract only the first time segment) and obtain the extracted signal s' p (f r ,τ m );

[0252] Step 32:

[0253] The truncated signal s' p (f r ,τ m The image 1S' is obtained by sequentially performing the inverse Fourier transform (IFT) in the distance dimension and the Fourier transform (FT) in the orientation dimension. p (t r ,f m );

[0254] Among them, t r f represents the time dimension of the distance dimension. m Indicates the frequency of the azimuth dimension;

[0255] Step 33:

[0256] For coarse focusing image 1S' p (t r ,f m A single prominent point in the image, coarsely focused image 1S' p (t r ,f m The echo intensity of each prominent point in the coarse focused image 1S' p (t r ,f m The distribution on ) usually follows a specific pattern.

[0257] Therefore, adaptive template matching methods (such as multi-scale Laplacian of Gaussian (LOG) detectors) are used to coarsely focus the image 1S'. p (t r ,f m Extract the prominent points in the data and obtain the spatial Doppler parameter distribution of all prominent points;

[0258] The spatial Doppler parameter distribution of all prominent points is the set of zeroth and first-order Doppler parameters of all extracted prominent points {(D 0,p D 1,p p = 1, 2, ..., N L ;

[0259] N L The number of extracted highlight points;

[0260] D 0,p This is the set of zero-order Doppler parameters for all extracted prominent points;

[0261] D 1,p This is the set of first-order Doppler parameters for all extracted prominent points;

[0262] Steps three and four:

[0263] Based on the results of the LOG detector, the azimuth signal corresponding to each prominent point extracted in step three can be extracted using an image domain filter; the specific process is as follows:

[0264] Taking a prominent point p as an example, the azimuth signal corresponding to the p-th prominent point extracted in step three is extracted using an image domain filter, and is represented as s'. p (τ m );

[0265] Step 35:

[0266] Estimate s' using ICPF (Integrated cubic phase function) p (τ m The modulation frequency k 2,p ;

[0267] Based on s' p (τ m The modulation frequency k 2,p Obtain the second-order Doppler parameter D corresponding to the salient point p. 2,p ; indicates as:

[0268]

[0269] Among them, f r,p The fast time frequency corresponding to the distance dimension of the prominent point p is...

[0270] Among them, T l This indicates the pulse width of the linear frequency modulated signal transmitted by the ISAR system;

[0271] Step 36: Repeat steps 34 to 35 to obtain the second-order Doppler parameters D corresponding to all prominent points. 2,p p = 1, 2, ..., N L ;

[0272] Step 37: Based on the set of zeroth and first-order Doppler parameters of all distinctive points extracted in Step 33 {(D 0,p D 1,p p = 1, 2, ..., N L The second-order Doppler parameters D corresponding to all the prominent points obtained in step 36. 2,p p = 1, 2, ..., N L ; Obtain the spatial Doppler parameter set of all prominent points {(D 0,p D 1,p D 2,p p = 1, 2, ..., N L .

[0273] The other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0274] Specific Implementation Method Eight: This implementation method differs from any of Specific Implementation Methods One to Seven in that, in step four, the spatial Doppler parameter distribution results of the prominent points obtained in step three are interpolated to obtain the complete spatial Doppler parameter distribution results on the image projection plane.

[0275] Based on the complete spatial Doppler parameter distribution results, the real-time phase change of each region in the image is obtained;

[0276] The wedge-transformed signal obtained in step two is processed for imaging to obtain coarse focused image 2. Based on real-time phase change, coarse focused image 2 is divided into sub-regions to obtain the coordinate range of each region in the image domain.

[0277] Based on the coordinate range of each region, the local target signal corresponding to each region is obtained and compensated to obtain the compensated local target signal. Based on the compensated local target signal of each region, the compensated complete target signal is obtained.

[0278] High-precision imaging results are obtained based on the compensated complete target signal;

[0279] The specific process is as follows:

[0280] Step 41:

[0281] The spatial distribution results of the Doppler parameters of the prominent points obtained in step three {(D 0,p D 1,p D 2,p p = 1, 2, ..., N L The second-order Doppler parameter {D} 2,p}, p=1,2,...,N L This can be used to characterize the local second-order Doppler information near each salient point; the second-order Doppler parameters at the remaining locations besides the salient points still need to be estimated. Next, based on the natural neighbor interpolation method of triangulation, the second-order Doppler parameters D corresponding to all salient points obtained in step three are calculated. 2,p p = 1, 2, ..., N L Interpolation is performed to obtain the interpolated second-order Doppler parameters D2;

[0282] Step 42: Obtain the real-time phase change based on the interpolated second-order Doppler parameter D2. Represented as:

[0283]

[0284] Step 43: Constructing a coarse-focused image 2S'(t) r,f m The specific process is as follows:

[0285] The wedge-transformed signal s'(f) obtained in step two r ,τ m Performing IFT in the distance dimension and FT in the orientation dimension sequentially yields the coarse focused image 2S'(t). r ,f m );

[0286] Although D2 is obtained by estimating parameters using the truncated signal, considering the stability of the trajectory of the attitude-stabilized space target, the current short-term estimation result D2 can still represent the spatial distribution of the second-order Doppler parameters over a long period and be applied to subsequent refined motion compensation. When the phase change caused by the second-order Doppler parameters is less than π / 4, the error term caused by these parameters can be ignored. Therefore, based on this neglect condition, the coarse-focused image 2 can be divided into multiple sub-regions, and compensation can be performed using the average value of the second-order parameters of the current sub-regions. The coarse-focused image 2 can be obtained from the signal s'(f) after the wedge transformation in step one. r ,τ m ) obtain, for s'(f r ,τ m Performing IFT in the distance dimension and FT in the orientation dimension respectively yields the coarse focused image 2S'(t). r ,f m ).

[0287] Step 44: When the real-time phase changes When the value is less than π / 4, the error term caused by this second-order Doppler parameter can be ignored. Therefore, based on this neglect condition and the adaptive partitioning principle, the coarse-focused image 2S'(t) can be divided into... r ,f m Divide it into Q sub-regions;

[0288] The image corresponding to the q-th sub-region is represented as S' q (t r ,f m );

[0289] To S' q (t r ,f m Perform Fourier Transform (FT) in the range dimension and Inverse Fourier Transform (IFT) in the azimuth dimension sequentially to obtain the signal s' corresponding to the q-th sub-region. q (f r ,t m );

[0290] Steps four and five: Construct the compensation term h for the signal corresponding to the q-th sub-region. q (f r ,τ m); is represented as:

[0291]

[0292] in, The average value of the second-order Doppler parameters in the q-th sub-region;

[0293] Step Four Six:

[0294] The compensation term h of the signal corresponding to the q-th sub-region constructed in steps four and five. q (f r ,τ m The signal s' corresponding to the q-th sub-region obtained in step 4.4 q (f r ,t m Multiply by , and obtain the compensated signal for the q-th sub-region;

[0295] Step 47:

[0296] Repeat steps four and five to four and six to obtain the complete target signal after compensation for all sub-regions:

[0297]

[0298] Step Four Eight:

[0299] The complete target signal s after compensation of all sub-regions t (f r ,τ m The range dimension IFT and the azimuth dimension IFT are performed sequentially to obtain high-precision imaging results of the target.

[0300] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0301] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods One to Eight in that, in step five, the target attitude parameters are accurately estimated by fitting the plane coefficients using the radar line-of-sight unit vector Taylor expansion result obtained in step one, the spatial Doppler parameter distribution result of the prominent point obtained in step three, and the high-precision imaging result obtained in step four.

[0302] The specific process is as follows:

[0303] Step 51:

[0304] The Doppler parameter distribution results {(D 0,p D 1,p D 2,p p = 1, 2, ..., N L Generate point cloud data D, where the three-dimensional coordinates of point cloud data D are Di, ... 0,pp = 1, 2, ..., N L D 1,p p = 1, 2, ..., N L D 2,p p = 1, 2, ..., N L ;

[0305] D 0,p D is the x-axis coordinate. 1,p D is the y-axis coordinate. 2,p Z-axis coordinate;

[0306] Step 52: Let the iteration number i = 1;

[0307] Step 53: Use principal component analysis to perform plane fitting on the point cloud data D to obtain the fitting plane and plane coefficients C for the i-th iteration. 0,i C 1,i ,1,C bias,i ;

[0308] in,

[0309] C 0,i D 0,p The plane coefficient;

[0310] C 1,i D 1,p The plane coefficient;

[0311] 1 is D 2,p The plane coefficient;

[0312] C bias,i For plane coefficient constants;

[0313] Step 54: Calculate N obtained in Step 3 L The Euclidean distance d from each prominent point to the fitted plane p p = 1, 2, ..., N L ; indicates as:

[0314]

[0315] Step 55: Calculate the standard deviation σ of the Euclidean distance; expressed as:

[0316]

[0317] in, N is the mean of the Euclidean distance. i The number of highlighted points in the i-th iteration (initial value is the total number of highlighted points N obtained in step three). L (number of items);

[0318] Steps five and six: Keep d pFor each prominent point less than σ, let the iteration count be i = i + 1, and update the number of prominent points and the point cloud data. Repeat steps 5.3 to 5.5 until the number of remaining prominent points is less than three or the Euclidean distance of each retained prominent point satisfies d. p ≥σ;

[0319] The plane coefficients fitted to all prominent points are obtained and denoted as {(C 0,i C 1,i ,1,C bias,i )}, i=1,2,...,N C ;

[0320] in,

[0321] N C The number of fitted planes;

[0322] Step 57: Calculate the normal vector direction n corresponding to the i-th plane. i :

[0323]

[0324] Step 58: In N C From the fitted planes, select the normal vector corresponding to the solar wing plane.

[0325] The superscript T indicates transpose;

[0326] Step 59:

[0327] The unit vector l of the longer side of the solar array is marked on the high-precision imaging result of the target obtained in step four. 1,proj unit vector l of the shorter side 2,proj ;

[0328] Step 50: Unit vector l based on the long side of the solar array 1,proj unit vector l of the shorter side 2,proj Calculate the three-dimensional vector l1 corresponding to the long side and the three-dimensional vector l2 corresponding to the short side of the solar array;

[0329] At this point, the target attitude estimation result consists of the three-dimensional vector l1 corresponding to the long side of the solar array, the three-dimensional vector l2 corresponding to the short side, and the normal vector. The direction is indicated.

[0330] The other steps and parameters are the same as those in one of the specific implementation methods one to eight.

[0331] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods One to Nine in that, in step Fifty, the unit vector l based on the long side of the solar array... 1,proj unit vector l of the shorter side 2,projCalculate the three-dimensional vector l1 corresponding to the long side and the three-dimensional vector l2 corresponding to the short side of the solar array; expressed as:

[0332]

[0333] in,

[0334] |·|2 represents the second norm of the vector being solved;

[0335] l 1,proj (1) indicates l 1,proj The first element; l 1,proj (2) indicates l 1,proj The second element; l 2,proj (1) indicates l 2,proj The first element; l 2,proj (2) indicates l 2,proj The second element;

[0336] inv() represents the inverse of a matrix;

[0337] The superscript T indicates transpose;

[0338] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0339] Example:

[0340] The proposed method for joint imaging and attitude inversion of space targets based on a spaceborne platform was verified by simulation, and the results are explained.

[0341] Simulation 1 is used to verify the effectiveness of this invention in situational awareness of spatial targets in different attitudes. The relative orbits are as follows: Figure 2 As shown in Figure 3, the situational awareness algorithm proposed in this invention obtains high-precision imaging results of the target under different attitudes. The corresponding image entropy and attitude estimation error are shown in Table 1, where the attitude estimation error is represented by the angle between the actual normal vector of the target's solar array and the estimated result. As can be seen from Figure 3 and Table 1, the algorithm proposed in this invention can obtain high-precision imaging results of the target under different attitudes and achieve effective target attitude estimation.

[0342] Table 1. Results of Space Target Situational Awareness Estimation

[0343]

[0344] Simulation 2 was used to verify the effectiveness of the present invention in situational awareness of space targets with different orbital errors relative to the orbital path, such as... Figure 2As shown in Table 2, the orbital errors are also shown. Figure 4 illustrates the high-precision imaging results of the situational awareness algorithm proposed in this invention when different orbital errors exist, and the corresponding image entropy and attitude estimation errors are shown in Table 3. As can be seen from Figure 4 and Table 3, the algorithm proposed in this invention can obtain high-precision imaging results and achieve effective target attitude estimation when different orbital errors exist.

[0345] Table 2 Track Error

[0346]

[0347] Table 3. Results of Space Target Situation Awareness Estimation

[0348]

[0349] This invention may have other embodiments. Without departing from the spirit and essence of this invention, those skilled in the art can make various corresponding changes and modifications according to this invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for joint imaging and attitude inversion of space targets based on a spaceborne platform, characterized in that: The specific process of the method is as follows: Step 1: The radar's narrowband tracking system acquires the target slant range and radar line-of-sight unit vector; Step 2: Perform translational compensation on the received signal based on the target slant range in Step 1 to obtain the translationally compensated signal. Then, perform a wedge transformation on the translationally compensated signal to obtain the wedge-transformed signal. Step 3: Perform time truncation on the wedge-transformed signal obtained in Step 2 to obtain the truncated signal. Perform imaging processing on the truncated signal to obtain coarse focused image 1. Using an adaptive template matching method, we extract prominent points from the coarse-focused image and obtain the spatial Doppler parameter distribution of these prominent points. Step 4: Perform interpolation processing on the spatial Doppler parameter distribution results of the prominent points obtained in Step 3 to obtain the complete spatial Doppler parameter distribution results on the image projection plane; Based on the complete spatial Doppler parameter distribution results, the real-time phase change of each region in the image is obtained; The wedge-transformed signal obtained in step two is processed for imaging to obtain a coarse-focused image two. Based on the real-time phase change, the coarse-focused image two is divided into sub-regions to obtain the coordinate range of each region in the image domain. Based on the coordinate range of each region, the local target signal corresponding to each region is obtained and compensated to obtain the compensated local target signal. Based on the compensated local target signal of each region, the compensated complete target signal is obtained. Imaging results are obtained based on the compensated complete target signal; Step 5: Using the radar line-of-sight unit vector Taylor expansion results obtained in Step 1, the spatial Doppler parameter distribution results of the prominent points obtained in Step 3, and the imaging results obtained in Step 4, the target attitude parameters are accurately estimated by fitting the plane coefficients. In step three, the wedge-transformed signal obtained in step two is truncated over time to obtain the truncated signal. The truncated signal is then processed for imaging to obtain a coarse focused image. Using an adaptive template matching method, we extract prominent points from the coarse-focused image and obtain the spatial Doppler parameter distribution of these prominent points. The specific process is as follows: Step 31: Migrate the second-order distance within the intercepted time period to a value less than [a certain value]. As a standard; in, Represents the speed of light; This represents the bandwidth of the linear frequency modulated signal transmitted by the ISAR system; According to the criteria, the signal after wedge transformation is... Extract the first time segment and obtain the extracted signal. ; Step 32: Process the intercepted signal By sequentially performing the inverse Fourier transform in the distance dimension and the Fourier transform in the orientation dimension, a coarse focused image is obtained. ; in, Represents the time dimension of distance. Indicates the frequency of the azimuth dimension; Represents the frequency of the distance dimension; The time component after wedge transformation. , Indicates the carrier frequency; This refers to the azimuth dimension in the time domain. , Indicates the pulse repetition period. To accumulate pulse count, ; Step 33: Using the adaptive template matching method to coarsely focus the image. The distinctive points in the data are extracted to obtain the spatial Doppler parameter distribution of all distinctive points; The spatial Doppler parameter distribution of all prominent points is the extracted set of zeroth and first-order Doppler parameters for all prominent points. ; The number of extracted highlight points; This is the set of zero-order Doppler parameters for all extracted prominent points; This is the set of first-order Doppler parameters for all extracted prominent points; Steps 3 and 4: Extract the azimuth signal corresponding to each prominent point extracted in Step 3 using an image domain filter; the specific process is as follows: The third step, extracted using image domain filters, is... The azimuth signal corresponding to each distinctive point is represented as: ; Step 35: Estimating using ICPF frequency modulation ; based on frequency modulation Obtain special points The corresponding second-order Doppler parameters ; indicates as: in, For special points The fast time frequency corresponding to the distance dimension cell. ; in, This indicates the pulse width of the linear frequency modulated signal transmitted by the ISAR system; Step 36: Repeat steps 34 to 35 to obtain the second-order Doppler parameters corresponding to all prominent points. ; Step 37: Based on the set of zeroth and first-order Doppler parameters of all prominent points extracted in Step 33. And the second-order Doppler parameters corresponding to all the special points obtained in step 36 Obtain the spatial Doppler parameter set for all prominent points. ; In step four, the spatial Doppler parameter distribution results of the prominent points obtained in step three are interpolated to obtain the complete spatial Doppler parameter distribution results on the image projection plane. Based on the complete spatial Doppler parameter distribution results, the real-time phase change of each region in the image is obtained; The wedge-transformed signal obtained in step two is processed for imaging to obtain a coarse-focused image two. Based on the real-time phase change, the coarse-focused image two is divided into sub-regions to obtain the coordinate range of each region in the image domain. Based on the coordinate range of each region, the local target signal corresponding to each region is obtained and compensated to obtain the compensated local target signal. Based on the compensated local target signal of each region, the compensated complete target signal is obtained. Imaging results are obtained based on the compensated complete target signal; The specific process is as follows: Step 41: Using the natural neighbor interpolation method based on triangulation, calculate the second-order Doppler parameters corresponding to all prominent points obtained in Step 3. Perform interpolation to obtain the interpolated second-order Doppler parameters. ; Step 42: Based on the interpolated second-order Doppler parameters To obtain real-time phase changes ; indicates as: in, Indicates the radar wavelength; Step 4.3: Constructing a coarse-focused image (II) The specific process is as follows: The signal after wedge transformation obtained in step two Performing IFT in the distance dimension and IFT in the azimuth dimension sequentially yields a coarse-focused image. ; Step 44: When the real-time phase changes Less than At that time, the coarse-focused image is divided into two parts according to the adaptive partitioning principle. Divide into Q sub-regions; No. The image representation of each sub-region is as follows: ; right Perform the Fourier Transform (FT) in the distance dimension and the Inverse Fourier Transform (IFT) in the orientation dimension sequentially to obtain the first... The signal corresponding to each sub-region ; Steps four and five: Constructing the first Compensation terms for the signals corresponding to each sub-region ; indicates as: in, For the first The average value of the second-order Doppler parameters in each sub-region; Represents the imaginary unit. ; Step 46: The first step constructed in step 45... Compensation terms for the signals corresponding to each sub-region The first one obtained in step four. The signal corresponding to each sub-region Multiply to obtain the first... The signal after compensation for each sub-region; Step 47: Repeat steps 45 and 46 to obtain the complete target signal after compensation for all sub-regions: Step 48: Compensate all sub-regions for the complete target signal The target imaging results are obtained by sequentially performing IFT in the range dimension and FT in the azimuth dimension.

2. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 1, characterized in that: In step one, the radar's narrowband tracking system acquires the target slant range and the radar line-of-sight unit vector; the specific process is as follows: Based on the radar's narrowband tracking system The radar's narrowband tracking system acquires the target slant range. Pitch angle of radar line of sight Azimuth parameter; The target slant range is expressed using Taylor expansion as follows: in, for The coefficients of the 0th order Taylor series expansion; for The coefficients of the first-order Taylor series expansion; for The coefficients of the second-order Taylor series expansion; Indicates a higher-order term that can be ignored; Radar line-of-sight unit vector Represented as: radar line-of-sight unit vector Taylor expansion yields: in, for The coefficients of the 0th order Taylor series expansion; for The coefficients of the first-order Taylor series expansion; for The coefficients of the second-order Taylor series expansion; This indicates a higher-order term that can be ignored.

3. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 2, characterized in that: In step two, the received signal is compensated for translational motion based on the target slant range in step one to obtain the compensated translational signal. Then, the compensated translational signal is subjected to wedge transformation to obtain the transformed wedge signal. The specific process is as follows: Step 21: The ISAR system transmits a linear frequency modulated (LFM) signal, performs pulse compression on the LFM signal, and obtains the target echo spectrum after pulse compression. Target echo spectrum after pulse compression It is expressed as follows: in, Indicates the number of scattering points on the target; This represents the amplitude of the linear frequency modulated signal transmitted by the ISAR system; Represents a rectangular window; Represents the scattering point The range migration relative to radar is expressed as: in, Represents the scattering point Migration relative to the radar's rotation distance; Represents vector product; Represents the scattering point of the target 3D coordinate vector; The superscript T indicates transpose; Step 22: Based on the target slant range in Step 1 constructing compensation terms from the Taylor series expansion coefficients ; Steps two and three: Include the compensation items Compared with the target echo spectrum after pulse compression Multiply to obtain the translationally compensated signal. ; Step 24: For the translationally compensated signal Perform a wedge transform to obtain the signal after the wedge transform. .

4. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 3, characterized in that: In step two, the target slant range from step one is used. constructing compensation terms from the Taylor series expansion coefficients ; The specific process is as follows: 。 5. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 4, characterized in that: In steps two and three, the compensation item will be... Compared with the target echo spectrum after pulse compression Multiply to obtain the translationally compensated signal. ; indicates as: 。 6. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 5, characterized in that: The signal after translational compensation in step two or four Perform a wedge transform to obtain the signal after the wedge transform. .

7. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 6, characterized in that: In step five, the target attitude parameters are accurately estimated by fitting plane coefficients using the Taylor expansion result of the radar line-of-sight unit vector obtained in step one, the spatial Doppler parameter distribution result of the prominent point obtained in step three, and the imaging result obtained in step four. The specific process is as follows: Step 51: Analyze the Doppler parameter distribution results Generate point cloud data Point cloud data The three-dimensional coordinates are respectively , , ; for Axis coordinates for Axis coordinates for Axis coordinates; Step 52: Let the number of iterations be... ; Step 53: Use principal component analysis to analyze the point cloud data. Perform plane fitting to obtain the first... Fitting plane and plane coefficients in the second iteration ; in, for The plane coefficient; for The plane coefficient; for The plane coefficient; For plane coefficient constants; Step 54: Calculate the results obtained in Step 3 Euclidean distance from each prominent point to the fitted plane ; indicates as: Step 55: Calculate the standard deviation of the Euclidean distance ; indicates as: in, The mean of the Euclidean distance is . For the first The number of highlighted points in the next iteration; Steps five and six: Retain The distinctive feature is that the number of iterations is reduced. Then update the number of highlighted points and the point cloud data, repeating steps 53 to 55 until the number of remaining highlighted points is less than three or the Euclidean distance of each retained highlighted point satisfies the condition. ; The plane coefficients fitted to all prominent points are expressed as follows: ; in, The number of fitted planes; Step 57: Calculate the first... The normal vector directions corresponding to each plane : Step 58: In From the fitted planes, select the normal vector corresponding to the solar wing plane. ; The superscript T indicates transpose; Step 59: Mark the unit vector of the long side of the solar array on the high-precision imaging result of the target obtained in Step 4. unit vector of the shorter side ; Step 50: Unit vector based on the long side of the solar array unit vector of the shorter side Calculate the three-dimensional vector corresponding to the long side of the solar array. The three-dimensional vector corresponding to the short side ; At this point, the target attitude estimation result is derived from the three-dimensional vector corresponding to the long side of the solar array. The three-dimensional vector corresponding to the short side and normal vector The direction is indicated.

8. The method for joint imaging and attitude inversion of space targets based on a spaceborne platform according to claim 7, characterized in that: In step fifty, the unit vector is based on the long side of the solar array. unit vector of the shorter side Calculate the three-dimensional vector corresponding to the long side of the solar array. The three-dimensional vector corresponding to the short side ; indicates as: in, This represents the second norm of the vector being solved; express The first element; express The second element; express The first element; express The second element; This indicates finding the inverse of a matrix; The superscript T indicates transpose.

Images