Satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integration method, equipment and medium

Abstract

The invention discloses a satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integration method, equipment and a medium, and relates to the technical field of radar signal processing and radar imaging. The objective of the invention is to solve the problems of inaccurate motion compensation and difficult large-rotation-angle imaging in ISAR imaging. According to the method, a particle swarm optimization algorithm is adopted to optimize motion parameters, optimal motion parameters are obtained, and the motion parameters comprise relative translation motion parameters and relative rotation motion parameters of a target and a radar; and obtaining focusing and calibration imaging results based on the optimal motion parameters and echo data reflected by the target. The influence of the translation compensation effect on rotation parameter estimation is reduced, more accurate parameter estimation and better imaging focusing performance are realized, and the ISAR imaging problem under the condition of violent relative movement in the rendezvous scene is solved.

Description

Technical Field

[0001] The present application belongs to the field of radar signal processing and radar imaging technology. Background Art

[0002] With the development of human life and commercial activities, more and more satellites are being launched into space, especially low-orbit satellites. This has led to a shortage of space resources and an increased risk of satellite collisions. Space situational awareness is a key means of maintaining space satellites. Radar is widely used due to its all-day, all-weather, long-range monitoring, and two-dimensional high-resolution imaging capabilities. Because ground-based radars have limited observation angles for space targets, they often require long periods of accumulation. Spaceborne radars, however, can be deployed in space and have a wider observation angle than ground-based radars. In intersection scenarios, the relative motion between the target and the radar is more intense, resulting in greater relative rotation angles. This allows for higher azimuth resolution, facilitating subsequent target identification and classification.

[0003] The primary mission of spaceborne radar is to acquire high-quality, highly calibrated images of space targets. This process primarily involves two phases: motion compensation and imaging followed by calibration. In ISAR imaging, relative motion can be divided into relative translation and relative rotation. Translation does not contribute to imaging and needs to be compensated, while relative rotation contributes to azimuth resolution. In intersection scenarios, the relative motion between the radar and target is more intense, and the relative rotation angle is larger, making it more conducive to achieving high azimuth resolution. However, intense relative translation produces significant range motion, leading to image distortion and defocus. In contrast, intense relative rotation produces large relative rotation angles, which, while resulting in higher azimuth resolution, also produces additional range motion, leading to azimuth defocus. Summary of the Invention

[0004] This application aims to solve the problems of inaccurate motion compensation and difficulty in large-angle imaging in ISAR imaging. It now provides an integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration based on parameter estimation.

[0005] The first aspect of the present application provides an integrated method for motion-compensated imaging calibration of a spaceborne inverse synthetic aperture radar, comprising:

[0006] The particle swarm optimization algorithm is used to optimize the motion parameters to obtain the optimal motion parameters, which include the relative translational motion parameters and relative rotational motion parameters of the target and the radar;

[0007] A focused and calibrated imaging result is obtained based on the optimal motion parameters and the echo data reflected by the target.

[0008] In a possible design, the particle swarm optimization algorithm is used to optimize the motion parameters to obtain the optimal motion parameters, including:

[0009] S1: Calculate the local image entropy corresponding to each particle position under the k-th iteration and determine IE(x l k)<IE(pb l ), if yes, then make pb l =x l k , then execute S2, otherwise execute S2 directly, where x l k is the position of the lth particle in the kth iteration, pb l is the optimal position of the lth particle, IE(x l k ) and IE(pb l ) are x l k and pb l The local image entropy of

[0010] S2: Select the optimal particle position gb under the kth iteration k , gb k =arg min[IE(pb l )];

[0011] S3: Determine k+1>K, if yes, then gb k as the optimal motion parameter, otherwise execute S4;

[0012] S4: Determine gb k Keep N iterations unchanged, 30≤N≤50, if yes, gb k as the optimal motion parameter, otherwise execute S5;

[0013] S5: Update the position of each particle so that k=k+1, and then return to S1.

[0014] In one example, the above calculation of the local image entropy corresponding to each particle position at the k-th iteration includes:

[0015] Constructing a translation compensation phase term based on the position of each particle at the kth iteration, and then performing translation compensation on the echo data reflected by the target;

[0016] The modified polar coordinate format algorithm is used to image and calibrate the echo data after translation compensation;

[0017] Scattering points in the imaging results are extracted, a target imaging area is extracted based on the scattering points, and the local image entropy corresponding to each particle position in the target imaging area is calculated.

[0018] In one example, the translation compensation phase term is constructed based on the position of each particle at the kth iteration, including:

[0019] The translation compensation phase term is constructed according to the following formula:

[0020]

[0021] in, The translation compensation phase term constructed by the lth particle at the kth iteration is t is the slow time, f is the fast time distance frequency, j is the imaginary unit, c is the speed of light, and are the relative translational motion parameters of the lth particle in the kth iteration.

[0022] In one example, the modified polar coordinate format algorithm is used to image and calibrate the echo data after translation compensation, including:

[0023] Processing the translationally compensated echo data using a range Doppler algorithm to obtain a scattering point echo map of the target inverse synthetic aperture;

[0024] Extracting scattering points from the scattering point echo map, confirming the distance unit where the target center is located, and redefining the rotation center of the target;

[0025] Based on the rotational motion parameters, the polar coordinate format algorithm is used to image and calibrate the echo data of the redefined rotation center.

[0026] In one example, extracting scattered points from the imaging results includes:

[0027] The amplitudes of the scattering points in the imaging results are sorted, and the scattering points ranked in the top 0.5% to 5% are extracted.

[0028] In one example, the above updating of the position of each particle includes:

[0029] Update the search step size of each particle according to the following formula:

[0030] v l k+1 =κv l k +c1r1(pb l -x l k )+c2r1(gb k -x l k ),

[0031] Among them, v l k+1 and v l k are the search steps of the lth particle in the k+1th and kth iterations respectively, κ is the inertia coefficient, c1 and c2 are learning factors, r1 and r2 are random numbers in [0,1];

[0032] Update the particle position according to the updated search step size:

[0033] x l k+1 =x l k +v l k+1 ,

[0034] Among them, x l k+1 is the updated position of the lth particle in the kth iteration.

[0035] In one possible design, the method for obtaining echo data reflected by the target includes: performing distance compression on the echo signals reflected by each scattering point on the target to obtain echo data.

[0036] A second aspect of the present application provides a space-borne inverse synthetic aperture radar motion compensation imaging calibration integrated device, characterized in that the space-borne inverse synthetic aperture radar motion compensation imaging calibration integrated device includes a processor and a memory, the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above-mentioned space-borne inverse synthetic aperture radar motion compensation imaging calibration integrated method.

[0037] A third aspect of the present application provides a computer storage medium, characterized in that at least one instruction is stored in the computer storage medium, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration.

[0038] Beneficial effects of this application:

[0039] 1. This application proposes an integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging and calibration based on parameter estimation, targeting the relative motion characteristics of spaceborne ISAR and space targets in rendezvous scenarios, thereby achieving high-resolution imaging and accurate calibration of space targets.

[0040] 2. Data processing results show that the proposed method can realize spaceborne ISAR imaging in rendezvous scenarios, and can also perform well under low signal-to-noise ratio conditions with high robustness.

[0041] 3. This application is mainly aimed at the imaging of space targets by satellite-borne ISAR in rendezvous scenarios. The motion characteristics of relative translation and relative rotation are derived. For situations where the relative translation motion is intense and the relative rotation angle is large, precise focused imaging and calibration are achieved through motion parameter estimation.

[0042] 4. This application proposes an integrated method for motion-compensated imaging and calibration of spaceborne inverse synthetic aperture radar (ISAR) based on parameter estimation. This method simultaneously estimates both translational and rotational motion parameters, thereby integrating motion compensation, imaging, and calibration. This design approach reduces the impact of translational compensation on rotational parameter estimation, achieving more accurate parameter estimation and improved imaging focusing performance, addressing the ISAR imaging challenge in situations with intense relative motion in rendezvous scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the integrated method for motion-compensated imaging calibration of spaceborne inverse synthetic aperture radar based on parameter estimation described in this application;

[0044] Figure 2 Schematic diagram of the intersection scene;

[0045] Figure 3 Schematic diagram of the applicable scope of the sinusoidal translational motion model in the intersection scenario;

[0046] Figure 4 Image entropy result diagram obtained by directly estimating translational motion parameters;

[0047] Figure 5 This is an image entropy result graph obtained by estimating translational motion parameters after parameter transformation as described in this application;

[0048] Figure 6 Schematic diagram of the relative rotational motion between the spaceborne ISAR and the space target in the rendezvous scenario;

[0049] Figure 7 This is the simulation verification result diagram of the relative rotation motion characteristics in the intersection scenario;

[0050] Figure 8 This is a schematic diagram of the interpolation effect of the polar coordinate format algorithm described in this application;

[0051] Figure 9 A local image entropy result graph obtained by estimating translational motion parameters after parameter transformation as described in this application;

[0052] Figure 10 is the scattering point model of the simulation target;

[0053] Figure 11 is a schematic diagram of imaging results obtained by applying the minimum image entropy-range Doppler algorithm to the translational motion model, including (a) the quadratic polynomial translational motion model, (b) the cubic polynomial translational motion model, and (c) the sinusoidal translational motion model;

[0054] FIG12 is a schematic diagram of the imaging results obtained by applying the minimum image entropy-range instantaneous Doppler algorithm based on the translational motion model, wherein (a) the quadratic polynomial translational motion model, (b) the cubic polynomial translational motion model, and (c) the sinusoidal translational motion model;

[0055] FIG13 is a schematic diagram of imaging results obtained by applying the method of the present application based on translational motion models, wherein (a) a quadratic polynomial translational motion model, (b) a cubic polynomial translational motion model, and (c) a sinusoidal translational motion model;

[0056] Figure 14 : This is the imaging result diagram of the method of the present application under 0dB signal-to-noise ratio;

[0057] Figure 15 This is the imaging result diagram of the method of this application at a -5dB signal-to-noise ratio;

[0058] Figure 16 This is the imaging result diagram of the method of this application at a signal-to-noise ratio of -10dB;

[0059] Figure 17 Schematic diagram of electromagnetic simulation model;

[0060] FIG18 is a schematic diagram of imaging results obtained by applying the minimum image entropy-range Doppler algorithm based on the translational motion model of the electromagnetic simulation model, wherein (a) the quadratic polynomial translational motion model, (b) the cubic polynomial translational motion model, and (c) the sinusoidal translational motion model;

[0061] FIG19 is a schematic diagram of imaging results obtained by applying the minimum image entropy-range instantaneous Doppler algorithm based on the translational motion model of the electromagnetic simulation model, wherein (a) the quadratic polynomial translational motion model, (b) the cubic polynomial translational motion model, and (c) the sinusoidal translational motion model;

[0062] FIG20 is a schematic diagram of imaging results obtained by applying the method of the present application to an electromagnetic simulation model based on a translational motion model, wherein (a) a quadratic polynomial translational motion model, (b) a cubic polynomial translational motion model, and (c) a sinusoidal translational motion model;

[0063] Figure 21 This is an imaging result diagram of the electromagnetic simulation model using the method of the present application at a 0dB signal-to-noise ratio;

[0064] Figure 22 This is an imaging result diagram of the electromagnetic simulation model using the method of the present application at a -5dB signal-to-noise ratio;

[0065] Figure 23 This is an imaging result diagram of the electromagnetic simulation model using the method of this application at a -10dB signal-to-noise ratio. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0067] By accurately estimating the translational motion parameters and relative rotational angular velocity between the radar and the target, we can precisely construct the translational compensation phase term, achieving integrated motion compensation, imaging, and calibration. Therefore, the key to spaceborne radar imaging in rendezvous scenarios lies in estimating the target's translational motion parameters and relative rotational angular velocity.

[0068] In the paper "Contrast maximization-based technique for 2-DISAR autofocusing" (IEE Proc. Radar Sonar Navig. vol. 152, no. 4, pp. 253-262, Aug. 2005), M. Martorell et al. proposed a method for motion parameter estimation based on maximizing the contrast of ISAR images obtained using a range-Doppler algorithm. However, due to the large relative rotation angle between the radar and the target in rendezvous scenarios, the range-Doppler algorithm based on a small rotation angle approximation cannot produce a focused ISAR image. Using the contrast of an unfocused image for parameter estimation can mislead the estimation results and prevent accurate target motion reconstruction for motion compensation. Therefore, this method is not suitable for spaceborne radar imaging in rendezvous scenarios.

[0069] In the paper "ISAR imaging of space stations based on ephemerisdata error compensation" (IGARSS 2020-2020 IEEE Int. Geosci. Remote Sens. Symp., Waikoloa, HI, USA, 2020, pp. 1161-1164), Anqi Gao et al. proposed a translational motion compensation method that uses six orbital elements to perform a coarse correction of the echo slant range, followed by a secondary correction using precise ephemeris data. However, in ISAR imaging, the target is non-cooperative, meaning its orbital information is unknown, making this method difficult to implement.

[0070] In the paper “Hybrid SAR-ISAR Imaging for Space Target via 2-D Spectrum and SIHR With Spaceborne Radar” (IEEE Transactions on Aerospace and Electronic Systems, vol. 60, no. 2, pp. 2106-2127, April 2024), Chen Ruida et al. proposed a spaceborne radar SAR-ISAR hybrid imaging method based on 2-D spectral SAR processing and ISAR smoothed integral high resolution (SIHR) technology. This algorithm approximates the relative translation motion model based on a cubic polynomial. However, the approximate accuracy of the cubic polynomial model is insufficient to describe the relative translation motion in the rendezvous scenario.

[0071] In summary, the existing motion compensation methods and imaging calibration methods are not suitable for spaceborne radar imaging of space targets in rendezvous scenarios. There are problems such as inaccurate motion compensation and difficulty in large-angle imaging.

[0072] In view of this, the embodiment of the present application provides an integrated method for motion compensation imaging calibration of spaceborne inverse synthetic aperture radar in order to solve the above problems. Figures 1 to 9 , the scheme of the implementation method of this application is described in detail.

[0073] Specific embodiment 1: The integrated method for motion-compensated imaging and calibration of a space-borne inverse synthetic aperture radar described in this embodiment includes:

[0074] The particle swarm optimization algorithm is used to optimize the motion parameters to obtain the optimal motion parameters, which include the relative translational motion parameters and relative rotational motion parameters of the target and the radar;

[0075] A focused and calibrated imaging result is obtained based on the optimal motion parameters and the echo data reflected by the target.

[0076] In one embodiment, optimizing the motion parameters using a particle swarm optimization algorithm to obtain the optimal motion parameters includes:

[0077] S1: Calculate the local image entropy corresponding to each particle position under the k-th iteration and determine IE(x l k )<IE(pb l ), if yes, then make pb l =x l k, then execute S2, otherwise execute S2 directly, where x l k is the position of the lth particle in the kth iteration, pb l is the optimal position of the lth particle, IE(x l k ) and IE(pb l ) are x l k and pb l The local image entropy of

[0078] S2: Select the optimal particle position gb under the kth iteration k , gb k =arg min[IE(pb l )];

[0079] S3: Determine k+1>K, if yes, then gb k as the optimal motion parameter, otherwise execute S4;

[0080] S4: Determine gb k Keep N iterations unchanged, 30≤N≤50, if yes, gb k as the optimal motion parameter, otherwise execute S5;

[0081] S5: Update the position of each particle so that k=k+1, and then return to S1.

[0082] In one embodiment, calculating the local image entropy corresponding to each particle position at the kth iteration includes:

[0083] Constructing a translation compensation phase term based on the position of each particle at the kth iteration, and then performing translation compensation on the echo data reflected by the target;

[0084] The modified polar coordinate format algorithm is used to image and calibrate the echo data after translation compensation;

[0085] Scattering points in the imaging results are extracted, a target imaging area is extracted based on the scattering points, and the local image entropy corresponding to each particle position in the target imaging area is calculated.

[0086] In one embodiment, constructing a translation compensation phase term based on the position of each particle at the kth iteration includes:

[0087] The translation compensation phase term is constructed according to the following formula:

[0088]

[0089] in, The translation compensation phase term constructed by the lth particle at the kth iteration is t is the slow time, f is the fast time distance frequency, j is the imaginary unit, c is the speed of light, and are the relative translational motion parameters of the lth particle in the kth iteration.

[0090] In one embodiment, the imaging and calibration of the translationally compensated echo data using a modified polar coordinate format algorithm includes:

[0091] Processing the translationally compensated echo data using a range Doppler algorithm to obtain a scattering point echo map of the target inverse synthetic aperture;

[0092] Extracting scattering points from the scattering point echo map, confirming the distance unit where the target center is located, and redefining the rotation center of the target;

[0093] Based on the rotational motion parameters, the polar coordinate format algorithm is used to image and calibrate the echo data of the redefined rotation center.

[0094] In one embodiment, extracting scattering points from the imaging results includes:

[0095] The amplitudes of the scattering points in the imaging results are sorted, and the scattering points ranked in the top 0.5% to 5% are extracted.

[0096] In one embodiment, updating the position of each particle includes:

[0097] Update the search step size of each particle according to the following formula:

[0098] v l k+1 =κv l k +c1r1(pb l -x l k )+c2r1(gb k -x l k ),

[0099] Among them, v l k+1 and v l k are the search steps of the lth particle in the k+1th and kth iterations respectively, κ is the inertia coefficient, c1 and c2 are learning factors, r1 and r2 are random numbers in [0,1];

[0100] Update the particle position according to the updated search step size:

[0101] x l k+1=x l k +v l k+1 ,

[0102] Among them, x l k+1 is the updated position of the lth particle in the kth iteration.

[0103] In one embodiment, the method for obtaining echo data reflected by a target includes:

[0104] The echo signals reflected from each scattering point on the target are compressed in distance to obtain echo data.

[0105] To further introduce the embodiments of this application, Figure 1 A flow chart for an integrated method for motion-compensated imaging calibration of spaceborne inverse synthetic aperture radar (ISAR) based on parameter estimation is provided. The flow chart includes steps 1 through 6. The numbering of each step does not necessarily define the order in which they are performed. Each step is described in detail below:

[0106] The integrated method for motion-compensated imaging calibration of a spaceborne inverse synthetic aperture radar based on parameter estimation described in this embodiment is performed in the following steps:

[0107] Step 1: The sub-satellite trajectories of the target and radar orbits intersect. The intersection scenario is when the sub-satellite points of the radar and target reach the intersection point of the sub-satellite trajectories of the two orbits at the same time, such as Figure 2 In this motion scenario, the relative translational motion between the radar and the target is more intense, and the commonly used polynomial models (cubic polynomial and quadratic polynomial models) are no longer applicable. Therefore, a sinusoidal model is used in this embodiment to describe the relative translational motion between the radar and the target.

[0108] In the rendezvous scenario, the relative motion between the radar and the target can be described by the initial distance and velocity angle at the rendezvous moment. Figure 3 The applicable velocity angle ranges for the quadratic polynomial model, cubic polynomial model, and the sinusoidal model proposed in this embodiment in rendezvous scenarios are presented for different orbital radius differences between the radar and the target. The results show that under the same conditions, the sinusoidal model more accurately describes relative motion. Furthermore, compared to the polynomial model, the sinusoidal model has a wider range of applicability.

[0109] Therefore, in this embodiment, the satellite-borne radar obtains the echo signals reflected by each scattering point on the target, and then performs distance compression processing on the echo signals to obtain the echo data S after distance compression. R (f,t):

[0110]

[0111] Among them, T obs is the observation time, rect(·) is the rectangular window function, t is the slow time, B is the signal bandwidth, f is the fast time distance frequency, f0 is the center frequency, j is the imaginary unit, c is the speed of light, (x p ,y p ) is the coordinate of the scattering point p relative to the rotation center, α, ω and b are the relative translational motion parameters, Ω is the relative rotational motion parameter, and V is the spatial region that defines the reflectivity function σ(p).

[0112] Step 2: At the initial moment, the relative distance R0 between the radar and the target satisfies:

[0113] R0=αcos(ωt)| t＝0 +b (2).

[0114] Since the constant phase caused by the translational motion parameter b does not affect the focusing effect of the image, motion compensation only needs to estimate the motion parameters (α, ω, Ω). If the motion parameters are estimated directly, due to the changing characteristics of the sinusoidal model, the simultaneous changes in amplitude and frequency will have a complex impact on the overall distance description, such as Figure 4 As shown in the figure, this characteristic will lead to the existence of multiple local optimal results, affecting the parameter estimation effect. In order to decouple the amplitude and frequency of the cosine function, the parameter dR is introduced. dR is the change in the distance between the radar and the target during the observation time. The relationship between dR and the relative translation motion parameters satisfies:

[0115]

[0116] The indirect translational motion parameter estimation can be achieved through parameter transformation. The simultaneous equations (2) and (3) are:

[0117]

[0118] like Figure 5 As shown in Figure 3, this parameter transformation makes the local optimal position obvious, which is beneficial for subsequent parameter estimation.

[0119] Under the initial conditions, the estimated results of the relative translational motion parameters and relative rotational motion parameters of each particle are x l 1 Expressed as:

[0120] x l 1 =(α l 1 ,ω l 1 ,Ω l 1 ),

[0121] Where l represents the particle number, l = 1, 2, ..., L, L is the total number of particles; x l 1 That is the initial optimal position of the particle pb l 1 .

[0122] Step 3: Construct the translation compensation phase term based on the motion parameters:

[0123]

[0124] in, represents the translation compensation phase term at the kth iteration, x l k =(α l k ,ω l k ,Ω l k ) represents the motion parameters at the kth iteration.

[0125] Using the translation compensation phase term For the echo data S obtained in step 1 R (f, t) is used to compensate for translation.

[0126] Step 4: Based on the rotational motion parameters, a modified polar coordinate format algorithm is applied to image the echo data after range compression and translation compensation processing reflected from each scattering point on the target, and the calibration operation is completed;

[0127] Step 41: Apply the range Doppler algorithm to process the echo data after translation compensation to obtain the scattering point echo map of the target inverse synthetic aperture;

[0128] Step 42: extract some strong scattering points from the initial target inverse synthetic aperture scattering point echo map, confirm the distance unit where the target center is located, and redefine the rotation center by performing phase compensation on the echo data after translation compensation;

[0129] Extract M strong scattering points and obtain the range bins for each. Because constant phase is ignored during translational compensation, the target echo spectrum after translational compensation may not be at the center of the spectrum or even scattered on both sides. Therefore, before performing polar coordinate interpolation, spectrum shifting is required to redefine the target's rotation center. Using the size information of common low-orbit satellites as a standard, if the difference between the maximum and minimum range bins of the extracted strong scattering points is less than the set target size, it indicates that the target spectrum is not dispersed. Spectrum shifting is only required based on the average range bin value of the extracted strong scattering points. If it exceeds the set target size, the total number of range bins must be added to shift the spectrum to the center.

[0130] Step 43: Based on the rotational motion parameters, a polar coordinate format algorithm is applied to perform target imaging and calibration on the echo data of the redefined rotation center.

[0131] The relative turning angle between the radar and the target is determined by the movement perpendicular to the radar line of sight caused by the relative speed of the radar and the target. Since the radar observation is short, the relative speed direction of the radar and the target can be approximated to be unchanged, such as Figure 6 As shown. Since the right ascension of the ascending node only causes the rotation of the satellite orbit around the z-axis, it ultimately only affects the position of the intersection of the target and the radar's subsatellite track. Therefore, for the convenience of analysis, the right ascension of the ascending node of the target and the radar is set to zero. Then, the relative velocity V between the target and the radar can be obtained based on the six orbital numbers. d and the radar line of sight unit vector i o :

[0132]

[0133]

[0134] Among them, a r 、v r 、 Ψ r are the radar's orbital radius, orbital velocity, orbital inclination, and true anomaly at the rendezvous moment, respectively. t 、v t 、 Ψ t are the target's orbital radius, orbital velocity, orbital inclination, and true anomaly at the rendezvous moment.

[0135] Then the relative rotation angle θ(t) between the radar and the target can be obtained as:

[0136]

[0137] Therefore, during the observation time, although the violent relative motion leads to a large relative rotation angle, it can be approximated as a linear change. Figure 7 As shown in Figure 1, two sets of orbital data were used for verification. The difference between the radar and target orbit heights was 100 km, and the orbital inclination differences were 60° and 70°. The angle changes obtained by simulating the orbital data during the observation time and the angle changes approximately calculated according to formula (8) were given, proving the validity of the approximate linear change of the angle.

[0138] On this basis, the polar coordinate format algorithm can be implemented by projecting the echo data surface onto a plane and then interpolating the data into equally spaced samples. Figure 8This is the resampling process of the polar coordinate interpolation method. The blue fan-shaped area is the original echo data, and the orange rectangular area is the resampling grid. The overlapping part of the resampling grid and the original echo data can be obtained by interpolation. The area covered by the resampling grid but not by the original echo data needs to be padded with zeros to finally obtain uniformly sampled echo data.

[0139] Step 5: Based on the imaging results from step 4, sort the scattering point amplitudes and extract the top 0.5% to 5% of the scattering points as strong scattering points. Based on the distance unit information of the strong scattering points and the size information of common low-orbit satellites, extract the target imaging area and calculate the local image entropy of the target imaging area;

[0140] Since image entropy reflects the global focusing effect of the image, such as Figure 5 As shown in the figure, in low signal-to-noise ratio scenarios, it is easily affected by noise distribution. Therefore, this embodiment proposes local image entropy as a criterion for judging the degree of image focus, such as Figure 9 As shown. Figure 5 and Figure 9 When the local image entropy is used as the evaluation criterion, the peak corresponding to the global optimal value is more obvious, which is more conducive to parameter estimation. Therefore, based on the target ISAR image obtained after translation compensation and large-angle imaging in step 4, strong scattering points are extracted, and the target area is determined by combining the target reference size. Then, the local image entropy IE(α, ω, Ω) is calculated according to formula (8):

[0141]

[0142] in, I(τ,υ,α,ω,Ω) is the scattering point echo map of the inverse synthetic aperture after the target area is extracted, τ is the time delay variable, and υ is the Doppler variable.

[0143] Step 6: If the image entropy is minimum or the maximum number of cycles is reached, the focusing and calibration imaging results are output. If the conditions are not met, the parameters are updated and the above steps are repeated until the conditions are met and the cycle is terminated.

[0144] Determine IE(x l k )<IE(pb l ), where x l k is the position of the lth particle in the kth iteration, pb l is the optimal position of the lth particle, IE(x l k ) and IE(pb l ) are x l k and pb lThe local image entropy of

[0145] If yes, make pb l =x l k , and then find the optimal particle position gb under the kth iteration k Otherwise, find the optimal particle position gb under the kth iteration k , gb k =arg min[IE(pb l )];

[0146] Judge k+1>K, if yes, then gb k As the optimal motion parameter, otherwise gb k Keep N iterations unchanged, 30≤N≤50, if yes, gb k As the optimal motion parameter, otherwise update the position of each particle so that k=k+1, and then repeat the above steps.

[0147] v l k+1 =κv l k +c1r1(pb l -x l k )+c2r1(gb l -x l k ) (10),

[0148] x l k+1 =x l k +v l k+1 (11),

[0149] Among them, v l k+1 and v l k are the search steps for the particle at the k+1th and kth iterations, respectively; κ is the inertia coefficient, ranging from [0.4, 2]; c1 and c2 are both learning factors, ranging from [0, 4]; r1 and r2 are both random numbers in the range of [0, 1].

[0150] This implementation analyzes the motion characteristics of the relative translational motion between the spaceborne ISAR and the space target in a rendezvous scenario, verifies the accuracy of translational motion compensation based on the sinusoidal translational motion model, and gives the applicable scopes of the sinusoidal translational motion model and the quadratic and cubic polynomial translational motion models in rendezvous scenarios, respectively.

[0151] This implementation analyzes the motion characteristics of the relative rotational motion between the spaceborne ISAR and the space target in a rendezvous scenario, modifies the traditional PFA algorithm, and realizes large-angle imaging in a rendezvous scenario through preprocessing with redefinition of the rotation center.

[0152] The simulation parameters are as follows:

[0153] The target scattering point model used in the simulation is as follows: Figure 10 As shown in the simulation scenario, the radar orbit is at an altitude of 700 km, with an orbital inclination of 0°, a right ascension of the ascending node of 0°, and an initial true anomaly of 5°. The target orbit is at an altitude of 600 km, with an orbital inclination of 70°, a right ascension of the ascending node of 0°, and an initial true anomaly of 1.21°. The radar system's transmission parameters are shown in Table 1, with the echo signal-to-noise ratio (after pulse compression) set to -10 dB, -5 dB, and 0 dB, respectively.

[0154] Table 1 System simulation parameters

[0155]

[0156] Figures 11(a)-(c) are ISAR images obtained by parameter estimation using the minimum image entropy-range Doppler algorithm based on the quadratic polynomial, the cubic polynomial and the sinusoidal model proposed in this application. The results show that regardless of whether it is the quadratic polynomial or the cubic polynomial, due to the errors in the description of the relative translational motion, the range image of the imaging result is obviously defocused, and the relative rotation angle is large, resulting in the failure of the range Doppler algorithm based on the small angle approximation, and the azimuth direction of the imaging result is obviously defocused. However, the torso of the target can be observed more clearly in Figure 11(c). This is because the relative translational motion compensation based on the sinusoidal model proposed in this application is more accurate, which reduces the defocus in the range direction.

[0157] Figures 12(a)-(c) are ISAR images obtained by using the minimum image entropy-range instantaneous Doppler algorithm for parameter estimation based on a quadratic polynomial, a cubic polynomial, and the sinusoidal model proposed in this application. Although the target's torso outline can be seen, the translation compensation based on the polynomial model in Figures 12(a) and (b) is not accurate enough. Moreover, due to the large relative rotation angle, it is difficult to effectively accumulate energy in the range and azimuth directions of the image, resulting in the loss of scattering points. However, in Figure 12(c), although the target image is distorted, the target's outline can be observed more clearly. This is because the motion compensation based on the sinusoidal model proposed in this application is more accurate. However, due to the large relative rotation angle, the azimuth direction is defocused, resulting in the appearance of false scattering points.

[0158] Figures 13(a)-(c) are ISAR images obtained by parameter estimation using the method described in this application based on a quadratic polynomial, a cubic polynomial, and the sine model proposed in this application, respectively. Figures (a) and (b) perform azimuth processing based on the parameter estimation method and imaging method of this application, effectively reducing azimuth defocus. However, the translational motion compensation based on the polynomial translational model is not accurate enough, resulting in blurred imaging results. In contrast, the result of Figure (c) is obtained based on the translational model, parameter estimation method, and imaging calibration method of this application, which effectively compensates for image distortion and defocusing effects and achieves image calibration.

[0159] Table 2 is a comparison of the estimation results of the relative motion parameters of the radar and the scattering point target by the method of the present application and the true values. It can be seen that the estimated values of the method of the present application are very close to the true values.

[0160] Table 2 Estimation results of relative motion parameters between radar and scattering point targets

[0161]

[0162] Figure 14-16 The scattering point model imaging results obtained based on the method of the present application at signal-to-noise ratios of 0dB, -5dB, and -10dB respectively show that even at low signal-to-noise ratios, the method of the present application effectively achieves motion compensation and imaging calibration.

[0163] In order to be closer to the actual application scenario, the electromagnetic simulation model is used to further illustrate the effectiveness of the model and algorithm proposed in this application. Figure 17 The electromagnetic model used for the simulation has a hexagonal main body. Four parabolic antennas are mounted at the base, with two larger antennas connected and fixed by two metal rods to ensure structural stability. Solar panels are mounted on both sides to collect energy.

[0164] Figures 18(a)-(c) are ISAR images obtained by using the minimum image entropy-range Doppler algorithm for parameter estimation based on the quadratic polynomial, the cubic polynomial, and the sinusoidal model proposed in this application. Similarly, due to the existence of large rotation angles, the imaging results obtained by the range Doppler algorithm are obviously defocused along the azimuth direction. However, comparing these three imaging results, it can be clearly seen that the imaging results based on the sine model are better than those based on the polynomial. The outline of the model is clearly visible, but the internal structure of the model is blurred due to defocus. The imaging results based on the polynomial are more severely defocused, and their outlines are blurred, which further confirms the accuracy of reconstructing the relative translational motion of the radar and the target based on the sine model in the intersection scenario.

[0165] Figures 19(a)-(c) show ISAR images obtained by using the minimum image entropy-range instantaneous Doppler algorithm for parameter estimation based on quadratic polynomials, cubic polynomials, and the sinusoidal model proposed in this application. Comparing Figure 19(c) with Figure 18(c) reveals that the focusing effect of the solar panel has been improved, with the outline of its main structure clearer. However, due to the inability to effectively accumulate defocus within its internal structure, the range instantaneous Doppler algorithm can only reduce defocus in the azimuth dimension to a certain extent. Furthermore, the imaging results of Figures 19(a)-(b) using motion compensation based on the polynomial model show that the translational compensation accuracy is insufficient, resulting in errors, and coupled with large azimuth rotational motion, the imaging results are severely defocused.

[0166] Figures 20(a)-(c) are ISAR images obtained by parameter estimation using the method described in this application based on quadratic polynomials, cubic polynomials and the sine model proposed in this application, respectively. Comparing the imaging results of Figures 18 and 19, Figures 20(a) and 20(b) are the imaging results obtained by the algorithm of this application based on the polynomial model. The focusing effect is improved, and the target outline can be clearly seen. Although there is still defocusing, the metal rods of its internal structure and the structure of the parabolic antenna are reconstructed. Figure 20(c) is the imaging result obtained based on the model and algorithm proposed in this application. The structure of the target is clearly reproduced, and its internal metallic structure, parabolic antenna and solar panel are clearly visible. Based on the accurate parameter estimation results, the target is calibrated and the size information of the target is obtained.

[0167] Table 3 compares the estimation results of the relative motion parameters of the radar and electromagnetic simulation targets by the present application method with the true values. It can be seen that the estimated values by the present application method are very close to the true values.

[0168] Table 3 Estimation results of relative motion parameters of radar and electromagnetic simulation targets

[0169]

[0170] Figure 21-23 The imaging results of the electromagnetic simulation model obtained based on the method of the present application at signal-to-noise ratios of 0dB, -5dB, and -10dB respectively show that even at low signal-to-noise ratios, the method of the present application effectively achieves motion compensation and imaging calibration.

[0171] Specific embodiment 2: The satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integrated device described in this embodiment includes a processor and a memory, and the memory stores at least one instruction. The at least one instruction is loaded and executed by the processor to implement the satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integrated method as described in specific embodiment 1.

[0172] Specific embodiment three: A computer storage medium described in this embodiment stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the integrated method for space-borne inverse synthetic aperture radar motion compensation imaging calibration as described in specific embodiment one.

[0173] Although the present application is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the present application. It should therefore be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the present application as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. An integrated method for motion-compensated imaging calibration of spaceborne inverse synthetic aperture radar, characterized in that: include: The particle swarm optimization algorithm is used to optimize the motion parameters to obtain the optimal motion parameters, which include the relative translational motion parameters and relative rotational motion parameters of the target and the radar; A focused and calibrated imaging result is obtained based on the optimal motion parameters and the echo data reflected by the target.

2. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 1, characterized in that: The particle swarm optimization algorithm is used to optimize the motion parameters to obtain the optimal motion parameters, including: S1: Calculate the local image entropy corresponding to each particle position under the k-th iteration and determine IE(x l k )<IE(pb l ), if yes, then make pb l =x l k , then execute S2, otherwise execute S2 directly, where x l k is the position of the lth particle in the kth iteration, pb l is the optimal position of the lth particle, IE(x l k ) and IE(pb l ) are x l k and pb l The local image entropy of S2: Select the optimal particle position gb under the kth iteration k , gb k =argmin[IE(pb l )]; S3: Determine k+1>K, if yes, then gb k as the optimal motion parameter, otherwise execute S4; S4: Determine gb k Keep N iterations unchanged, 30≤N≤50, if yes, gb k as the optimal motion parameter, otherwise execute S5; S5: Update the position of each particle so that k=k+1, and then return to S1.

3. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 2, characterized in that: The calculation of the local image entropy corresponding to each particle position at the k-th iteration includes: Constructing a translation compensation phase term based on the position of each particle at the kth iteration, and then performing translation compensation on the echo data reflected by the target; The modified polar coordinate format algorithm is used to image and calibrate the echo data after translation compensation; Scattering points in the imaging results are extracted, a target imaging area is extracted based on the scattering points, and the local image entropy corresponding to each particle position in the target imaging area is calculated.

4. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 3, characterized in that: The constructing of the translation compensation phase term based on the position of each particle at the kth iteration includes: The translation compensation phase term is constructed according to the following formula: in, The translation compensation phase term constructed by the lth particle at the kth iteration is t is the slow time, f is the fast time distance frequency, j is the imaginary unit, c is the speed of light, and are the relative translational motion parameters of the lth particle in the kth iteration.

5. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 3, characterized in that: The modified polar coordinate format algorithm is used to image and calibrate the echo data after translation compensation, including: Processing the translationally compensated echo data using a range Doppler algorithm to obtain a scattering point echo map of the target inverse synthetic aperture; Extracting scattering points from the scattering point echo map, confirming the distance unit where the target center is located, and redefining the rotation center of the target; Based on the rotational motion parameters, the polar coordinate format algorithm is used to image and calibrate the echo data of the redefined rotation center.

6. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 3, characterized in that: The extracting of scattering points in the imaging result includes: The amplitudes of the scattering points in the imaging results are sorted, and the scattering points ranked in the top 0.5% to 5% are extracted.

7. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 2, characterized in that: The updating of the position of each particle includes: Update the search step size of each particle according to the following formula: Among them, v l k+1 and v l k are the search steps of the lth particle in the k+1th and kth iterations respectively, κ is the inertia coefficient, c1 and c2 are learning factors, r1 and r2 are random numbers in [0,1]; Update the particle position according to the updated search step size: x l k+1 =x l k +v l k+1 , Among them, x l k+1 is the updated position of the lth particle in the kth iteration.

8. The integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to claim 1, characterized in that: The method for obtaining echo data reflected by the target includes: The echo signals reflected from each scattering point on the target are compressed in distance to obtain echo data.

9. Spaceborne inverse synthetic aperture radar motion compensation imaging calibration integrated equipment, characterized by: The spaceborne inverse synthetic aperture radar motion compensation imaging and calibration integrated device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the spaceborne inverse synthetic aperture radar motion compensation imaging and calibration integrated method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that The computer storage medium stores at least one instruction, which is loaded and executed by the processor to implement the integrated method for spaceborne inverse synthetic aperture radar motion compensation imaging calibration according to any one of claims 1 to 8.

Images