Space Maneuvering Target Inverse Synthetic Aperture Radar Imaging Method Based on Improved BFGS-PFA

Abstract

The present disclosure provides a method for inverse synthetic aperture radar imaging of a space maneuvering target based on improved BFGS-PFA. The above method includes: calculating a plurality of range cells based on the preprocessed echo signal; determining a target range cell from the plurality of range cells; using the eigenvector method to process the preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variant phase; using a preset optimization algorithm to process the preprocessed echo signal and the rough estimate of the non-empty variant phase based on the target range cell to obtain a rough estimate of the maneuvering factor; using the quasi-Newton method to solve the minimization of image entropy based on the rough estimate of the non-empty variant phase and the rough estimate of the maneuvering factor to obtain a refined estimate of the non-empty variant phase and a refined estimate of the maneuvering factor; performing phase correction on the preprocessed echo signal based on the refined estimate of the non-empty variant phase and the refined estimate of the maneuvering factor to obtain a corrected echo signal; and obtaining an imaging result of the space maneuvering target based on the corrected echo signal.

Description

Technical Field

[0001] The present disclosure relates to the field of radar technology, and more particularly to a method for inverse synthetic aperture radar imaging of space maneuvering targets based on improved BFGS-PFA. Background Art

[0002] Inverse synthetic aperture radar imaging, as an active microwave imaging technology, can achieve long-distance imaging of various non-cooperative moving targets on the sea surface, in the air, and in space, such as ships, aircraft, satellites, and space stations, all day and all weather. Using ISAR (Inverse Synthetic Aperture Radar) to achieve all-weather and high-resolution imaging of space maneuvering targets such as satellites, space stations, space debris, and near-Earth asteroids, and constructing a comprehensive and reliable space target monitoring system to ensure real-time monitoring and safety warning of various space targets has become an urgent problem.

[0003] In the related art, suppressing the migration of resolution cells in the imaging result by using the PFA algorithm requires obtaining the estimation of the target rotation parameters by minimizing the image entropy model through a global optimization algorithm. However, the global optimization algorithm has a high computational cost and takes a long time, which is not conducive to real-time data processing. Moreover, due to the fast movement speed and strong maneuverability of space targets, the rotational acceleration of its turntable model cannot be ignored. In addition, in order to reduce the influence brought by the reduction of the non-empty-variant phase error compensation accuracy at large rotation angles, the non-empty-variant phase error should be further finely compensated during iteration. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a method for inverse synthetic aperture radar imaging of space maneuvering targets based on improved BFGS-PFA.

[0005] According to a first aspect of the present disclosure, a method for inverse synthetic aperture radar imaging of a space maneuvering target based on improved BFGS-PFA is provided. The above space maneuvering target makes relative motion with respect to the above inverse synthetic aperture radar, including: calculating a plurality of range cells based on the preprocessed echo signal, wherein the above preprocessing represents that the echo signal is converted from a time-domain signal to a frequency-domain signal, the above echo signal is a reflection signal received by the above inverse synthetic aperture radar that changes with time, and the above range cells have a corresponding relationship with the frequency domain in the above preprocessed echo signal; determining a target range cell from the above plurality of range cells; using the eigenvector method to process the above preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variant phase; using a preset optimization algorithm to process the above preprocessed echo signal and the above rough estimate of the non-empty variant phase based on the above target range cell to obtain a rough estimate of the maneuvering factor; using the quasi-Newton method to solve the minimization of the image entropy based on the above rough estimate of the non-empty variant phase and the above rough estimate of the maneuvering factor to obtain a refined estimate of the non-empty variant phase and a refined estimate of the maneuvering factor, and the above image entropy model represents the above inverse synthetic aperture radar imaging effect; performing phase correction on the above preprocessed echo signal based on the above refined estimate of the non-empty variant phase and the above refined estimate of the maneuvering factor to obtain a corrected echo signal; obtaining an imaging result of the above space maneuvering target based on the above corrected echo signal.

[0006] According to an embodiment of the present disclosure, the above using the eigenvector method to process the above preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variant phase includes: constructing an echo covariance matrix based on the echo signal frequency band data, wherein the above echo signal frequency band data is obtained from the above preprocessed echo signal based on the target range cell; solving the above echo covariance matrix to obtain an echo eigenvector group; obtaining the above rough estimate of the non-empty variant phase based on the above echo eigenvector group and the above echo signal frequency band data.

[0007] According to an embodiment of the present disclosure, the above using a preset optimization algorithm to process the above preprocessed echo signal and the above rough estimate of the non-empty variant phase based on the above target range cell to obtain a rough estimate of the maneuvering factor includes: obtaining the echo signal frequency band data corresponding to the above target range cell; randomly generating a plurality of maneuvering factor candidate values with different initial values; iteratively solving the minimization of the image entropy based on the above plurality of maneuvering factor candidate values, the above rough estimate of the non-empty variant phase, and the above echo signal frequency band data to obtain the above rough estimate of the maneuvering factor.

[0008] According to an embodiment of the present disclosure, determining the target distance unit from the multiple distance units includes: calculating the normalized amplitude variance for the multiple distance units to obtain a group of normalized amplitude variances corresponding to the distance units; and determining the distance unit with the smallest normalized amplitude variance as the target distance unit.

[0009] According to an embodiment of the present disclosure, obtaining the refined estimate of the non-empty variant phase by using the quasi-Newton method based on the rough estimate of the non-empty variant phase and the rough estimate of the maneuvering factor by solving the minimization of the image entropy includes: obtaining the gradient value of the empty variant phase based on the rough estimate of the non-empty variant phase; calculating the search direction based on the Hessian matrix and the gradient value of the empty variant phase; iteratively updating the rough estimate of the non-empty variant phase based on the search direction and the step size, where the step size is determined by the line search method; returning to execute the step of obtaining the gradient of the empty variant phase based on the rough estimate of the non-empty variant phase when the norm of the gradient value of the empty variant phase does not reach the preset threshold; and determining the updated rough estimate as the refined estimate of the non-empty variant phase when the norm of the gradient value of the empty variant phase reaches the preset threshold.

[0010] According to an embodiment of the present disclosure, the preprocessed echo signal is obtained through the following steps: performing dechirp compression on the echo signal to obtain the dechirp-compressed echo signal; processing the dechirp-compressed echo signal by using the fast Fourier transform algorithm to obtain the transformed echo signal; performing residual phase compensation on the transformed echo signal to obtain the compensated echo signal; and performing envelope alignment processing on the compensated echo signal to obtain the preprocessed echo signal.

[0011] According to an embodiment of the present disclosure, obtaining the imaging result of the spatial maneuvering target based on the corrected echo signal includes: performing range-direction Fourier transform on the corrected echo signal to obtain the Fourier-transformed echo signal; using the polar format transformation algorithm to obtain the transformed echo signal based on the Fourier-transformed echo signal; performing range-direction fast Fourier transform on the transformed echo signal to obtain the fast Fourier-transformed echo signal; performing phase gradient autofocus processing on the fast Fourier-transformed echo signal to obtain the processed echo signal; and performing azimuth-direction Fourier transform on the processed echo signal to obtain the imaging result of the spatial maneuvering target.

[0012] The second aspect of the present disclosure provides a space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA, including: a range cell calculation module for calculating a plurality of range cells based on the preprocessed echo signal, where the above preprocessing represents that the echo signal is converted from a time-domain signal to a frequency-domain signal, the above echo signal is a reflection signal received by the above inverse synthetic aperture radar that changes with time, and the above range cells have a corresponding relationship with the frequency domain in the above preprocessed echo signal; a determination module for determining a target range cell from the above plurality of range cells; a non-empty variant phase rough estimation module for using the eigenvector method to process the above preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variant phase; a maneuver factor rough estimation module for using a preset optimization algorithm to process the above preprocessed echo signal and the above rough estimate of the non-empty variant phase based on the above target range cell to obtain a rough estimate of the maneuver factor; a fine estimation module for using the quasi-Newton method to solve the minimization of image entropy based on the above rough estimate of the non-empty variant phase and the above rough estimate of the maneuver factor to obtain a fine estimate of the non-empty variant phase and a fine estimate of the maneuver factor, where the above image entropy model represents the above inverse synthetic aperture radar imaging effect; a correction module for performing phase correction on the above preprocessed echo signal based on the above fine estimate of the non-empty variant phase and the above fine estimate of the maneuver factor to obtain a corrected echo signal; and an imaging module for obtaining an imaging result of the above space maneuvering target based on the above corrected echo signal.

[0013] The third aspect of the present disclosure provides an electronic device, including: one or more processors; a memory for storing one or more computer programs, where the above one or more processors execute the above one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium, on which a computer program or instruction is stored, and when the above computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0015] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instruction, and when the above computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0016] According to the embodiments of the present disclosure, the rough estimation of the non-empty variant phase is performed by the eigenvector method and the rough estimation of the maneuver factor is performed by a preset optimization algorithm, so that the rough estimates of the non-empty variant phase and the maneuver factor can be used as initial values to solve the minimization of image entropy by the quasi-Newton method, accelerating the obtaining of the fine estimates of the maneuver factor and the non-empty variant phase, thereby correcting the phase and amplitude of the echo signal to compensate for the distortion caused by platform movement, and thus improving the clarity and accuracy of imaging. Description of the Drawings

[0017] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above content and other objects, features, and advantages of the present disclosure will become clearer. In the drawings:

[0018] Figure 1 Schematically shows an application scenario diagram of a space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA according to an embodiment of the present disclosure;

[0019] Figure 2 Schematically shows a flowchart of a space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA according to an embodiment of the present disclosure;

[0020] Figure 3 Schematically shows a geometric model diagram of space target ISAR imaging according to an embodiment of the present disclosure;

[0021] Figure 4 Schematically shows a point target schematic diagram according to an embodiment of the present disclosure;

[0022] Figure 5 Schematically shows an imaging diagram of a uniformly rotating point target RD algorithm according to an embodiment of the present disclosure;

[0023] Figure 6 Schematically shows an imaging result diagram of a uniformly rotating point target based on improved BFGS-PFA for space maneuvering target inverse synthetic aperture radar according to an embodiment of the present disclosure;

[0024] Figure 7 Schematically shows a schematic diagram of an imaging result of a uniformly accelerating rotating point target RD algorithm according to an embodiment of the present disclosure;

[0025] Figure 8 Schematically shows an imaging diagram of a uniformly accelerating rotating point target based on improved BFGS-PFA for space maneuvering target inverse synthetic aperture radar according to an embodiment of the present disclosure;

[0026] Figure 9 Schematically shows a structural block diagram of a space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA according to an embodiment of the present disclosure;

[0027] Figure 10 Schematically shows a block diagram of an electronic device suitable for implementing a space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA. Detailed Embodiments

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

[0029] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] In the case of using expressions such as "at least one of A, B, and C, etc.", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0032] In the ISAR imaging of a space maneuvering target, the traditional RD (Range-Doppler) imaging algorithm equates the relative motion between the radar and the target to a turntable model, and decomposes the target motion into translational motion along the radar line of sight and rotational motion around the target center. Assuming that the target rotational motion is a small-angle uniform rotation, after translational motion compensation, the RD algorithm realizes imaging in the range direction by pulse compression of the broadband echo signal, and realizes imaging in the azimuth direction by the Doppler information of the target rotating around the center, thereby realizing two-dimensional imaging of the target. However, under the conditions of large target rotation angle and high maneuverability, the assumption of small-angle uniform rotation fails, the spatially variant phase caused by the large rotation angle cannot be ignored, and the cross-range migration of the imaging result is aggravated. At the same time, the coupling between the translational component and the rotational component at large rotation angles will also reduce the compensation accuracy of the non-spatially variant phase error, and will also lead to a decrease in imaging quality.

[0033] In the related art, the PFA (Polar Format Algorithm) can be used to suppress MTRC (Migration Through Resolution Cell). However, the PFA algorithm needs to use the target rotation parameters as known conditions in the operation to suppress MTRC. The target rotation parameters can be estimated by minimizing the image entropy model through a global optimization algorithm. However, the global optimization algorithm has a high computational cost and takes a long time, which is not conducive to real-time data processing. Moreover, due to the fast movement speed and strong mobility of space targets, the rotational acceleration of their turntable models cannot be ignored. In addition, to reduce the impact of the reduced compensation accuracy of non-air-variant phase errors at large turning angles, the non-air-variant phase errors should be further finely compensated during iteration.

[0034] In view of this, an embodiment of the present disclosure provides a method for inverse synthetic aperture radar imaging of a space maneuvering target based on improved BFGS-PFA, including: calculating a plurality of range cells based on the preprocessed echo signal, where the preprocessing represents the conversion of the echo signal from a time-domain signal to a frequency-domain signal, the echo signal is a reflection signal received by the inverse synthetic aperture radar that changes with time, and the range cells have a corresponding relationship with the frequency domain in the preprocessed echo signal; determining a target range cell from the plurality of range cells; using the eigenvector method to process the preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-air-variant phase; using a preset optimization algorithm to process the preprocessed echo signal and the rough estimate of the non-air-variant phase based on the target range cell to obtain a rough estimate of the maneuvering factor; using the quasi-Newton method to solve the minimization of the image entropy based on the rough estimate of the non-air-variant phase and the rough estimate of the maneuvering factor to obtain a refined estimate of the non-air-variant phase and a refined estimate of the maneuvering factor, where the image entropy model represents the inverse synthetic aperture radar imaging effect; performing phase correction on the preprocessed echo signal based on the refined estimate of the non-air-variant phase and the refined estimate of the maneuvering factor to obtain a corrected echo signal; and obtaining an imaging result of the space maneuvering target based on the corrected echo signal.

[0035] Figure 1 Fig. shows an application scenario diagram of the method for inverse synthetic aperture radar imaging of a space maneuvering target based on improved BFGS-PFA according to an embodiment of the present disclosure.

[0036] As Figure 1As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, a server 105, and a radar 106. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 105, and the radar 106. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0037] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0038] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc.

[0039] The server 105 may be a server providing various services, such as a background management server (for example only) that supports the websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process data such as received user requests, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0040] The radar 106 may be an inverse synthetic aperture radar. After receiving the echo signal reflected by the space maneuvering target, the radar may preliminarily process the signal and transmit it to the server 105 through the network 104 for further processing, or the echo signal may also be processed by the radar 106.

[0041] It should be noted that the method for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA provided by the embodiments of the present disclosure can generally be executed by the server 105 or the radar 106. Correspondingly, the device for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA provided by the embodiments of the present disclosure can generally be disposed in the server 105 or the radar 106. The method for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the device for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA provided by the embodiments of the present disclosure can also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105.

[0042] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0043] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. Figure 1 are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. Figures 2 to 8 The following will describe in detail the method for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA according to the embodiments of the present disclosure based on

[0044] Figure 2 FIG. schematically shows a flowchart of the method for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA according to the embodiments of the present disclosure.

[0045] As Figure 2 shown, the method for inverse synthetic aperture radar imaging of a space maneuvering target based on the improved BFGS-PFA in this embodiment includes operations S210 to S270.

[0046] In operation S210, a plurality of range cells are calculated based on the preprocessed echo signal.

[0047] Among them, the preprocessing represents that the echo signal is converted from a time-domain signal to a frequency-domain signal. The echo signal is a reflection signal that changes with time received by the inverse synthetic aperture radar, and the range cell has a corresponding relationship with the frequency domain in the preprocessed echo signal.

[0048] Figure 3 FIG. schematically shows a geometric model diagram of ISAR imaging of a space target according to the embodiments of the present disclosure.

[0049] AsFigure 3 As shown, the geometric model diagram according to this embodiment may include a radar 106 and a space maneuvering target 301. The distance between a scatter point with coordinates (x, y) on the above-mentioned space maneuvering target 301 (hereinafter simply referred to as the target) and the radar 106 is R. p , the distance from the rotation center is R, and the backscattering coefficient of this point is ρ(x, y). In the traditional RD algorithm, the target motion can be decomposed into a translational motion R along the radar direction (t m ) and a rotational motion around the rotation center o. After decomposition, the distance between this point and the radar can be expressed as R p (t m ). Among them, t is the fast time (range direction time), and t m is the slow time (azimuth direction time).

[0050] According to an embodiment of the present disclosure, the above-mentioned radar may be, for example, an inverse synthetic aperture radar, and the radar transmitted signal may be, for example, an LFM (Linear Frequency Modulation) signal. Without considering the attenuation in the signal propagation process, the in-pulse compensation of the echo signal can be achieved by fitting the radial velocity of the target and constructing a compensation term. After the in-pulse motion correction of the target, the echo signal is dechirped and compressed to obtain the echo signal represented by the following formula (1).

[0051] (1)

[0052] Among them, s(t, tm) represents the echo signal, t is the fast time (range direction time), t m is the slow time (azimuth direction time), ρ(x, y) represents the scattering coefficient of the scatter point P(x, y), r represents the speed of light, ect represents the envelope of the echo signal, where e is the base of the natural logarithm, represents the time delay term of the echo signal, T p is the pulse width, represents the Doppler term of the echo signal, where j is the imaginary unit, K r is the frequency modulation slope in the radar line-of-sight direction, R Δ (t m ) is the distance change of the target relative to the radar, represents the carrier frequency term of the echo signal, where f c is the carrier frequency, represents the quadratic term of the echo signal, used to compensate for the quadratic motion of the target, R p (t m ) represents the distance of the target relative to the radar, R(t m ) represents the distance of the target relative to the rotation center, R Δ (t m) represents the relative distance change of the target with respect to the radar, c represents the speed of light, R Δ (t m ) can be obtained through the following formula (2).

[0053] (2)

[0054] where, R p (t m ) represents the distance of the target with respect to the radar, R(t m ) represents the distance of the target with respect to the rotation center.

[0055] According to an embodiment of the present disclosure, the preprocessed echo signal can be expressed as the following formula (3)

[0056] (3)

[0057] where, S(f, tm) represents the preprocessed echo signal, f represents the frequency, tm represents the slow time, ρ(x, y) represents the scattering coefficient of the scattering point P(x, y), T P represents the pulse width, K r represents the frequency modulation slope in the radar line-of-sight direction, R Δ represents the relative distance change of the target with respect to the radar, represents the carrier frequency term of the echo signal, where j is the imaginary unit, f c is the carrier frequency, is a non-empty variable phase error term, represents the rotational angular velocity of the target, γ represents the rotational acceleration of the target.

[0058] Modeling the target rotation as a uniformly accelerated rotation, the rotation angle of the target in the slow time can be obtained as shown in the following formula (4).

[0059] (4)

[0060] where, θ(t m ) represents the rotation angle of the target at the slow time t m moment, ω represents the rotational angular velocity of the target, that is, the angle rotated per unit time, t m represents the slow time, γ represents the rotational acceleration of the target, represents the quadratic term of the rotation angle caused by the rotational acceleration. Substituting the above formula (4) into R Δ , and performing a second-order Taylor expansion, the following formula (5) can be obtained.

[0061] (5)

[0062] Substituting the above formula (5) into the above formula (3) gives the following formula (6).

[0063] (6)

[0064] wherein, at this time, the coupling between the range parameter f and the azimuth parameter t in the sinc term m can no longer be ignored, and this coupling causes range MTRC. At this time, a quadratic term of time appears in the exponential term. If a direct azimuth FFT (Fast Fourier Transform) is performed, it will result in severe defocusing in the azimuth direction, that is, azimuth MTRC.

[0065] According to the embodiments of the present disclosure, multiple range cells can be calculated by methods such as cross-correlation method, range centroid method, and minimum variance method. Each range cell corresponds to a frequency domain of the preprocessed echo signal. The present disclosure does not limit the method for calculating the range cells.

[0066] In operation S220, a target range cell is determined from the multiple range cells.

[0067] According to the embodiments of the present disclosure, the prominent point cell in the range cell can be determined as the target range cell, or a cell with obvious features or a cell with a higher scattering intensity can be selected as the target range cell. The present application does not limit the selection of the target range cell.

[0068] In operation S230, using the eigenvector method, the preprocessed echo signal is processed based on the target range cell to obtain a rough estimate of the non-empty variable phase.

[0069] According to the embodiments of the present disclosure, as mentioned above, if a direct azimuth FFT is performed on the preprocessed echo signal represented by formula (6), it will result in severe defocusing in the azimuth direction. At this time, azimuth MTRC needs to be compensated. The azimuth can be compensated by performing MFT (Matching Fourier Transform) on the above formula (6) to obtain the following formula (7).

[0070] (7)

[0071] wherein, z(f, fm) represents the signal after azimuth Fourier transform (MFT), where f is the frequency, and f m is the frequency variable in the azimuth direction, S(f, t m ) represents the preprocessed echo signal represented by the above formula (6), represents the phase compensation term, represents the quadratic phase term caused by the target rotation, c is the speed of light, f c is the carrier frequency, x is the coordinate of the scattering point on the target, Δx is the offset of the rotation center relative to the image center, ω is the rotation angular velocity of the target, MFT az is the azimuth matching Fourier transform and can be expressed by the following formula (8).

[0072] (8)

[0073] where f q is the azimuth frequency vector, is the maneuver factor. When the rotational acceleration γ is 0, this transform will degenerate into a Fourier transform.

[0074] According to an embodiment of the present disclosure, after determining that the range cell is not a prominent point cell, the PGA (Phase Gradient Autofocus) algorithm can be used to estimate the non-empty variant phase.

[0075] In operation S240, using a preset optimization algorithm, based on the target range cell, the preprocessed echo signal and the rough estimate of the non-empty variant phase are processed to obtain a rough estimate of the maneuver factor.

[0076] According to an embodiment of the present disclosure, the above preset optimization algorithm can be, for example, a gradient-based algorithm, a particle swarm optimization algorithm, a genetic algorithm, etc. The gradient-based algorithm is a local optimization algorithm, which converges faster and takes less time than the global optimization algorithm. When the gradient-based algorithm is used for multi-parameter estimation, it is easy to fall into a local optimum. Therefore, one or more range cells deviating from the rotation center can be selected, and a global optimization algorithm such as particle swarm optimization can be used to achieve a rough estimate of the maneuver factor with the goal of minimizing the entropy function.

[0077] In operation S250, using the quasi-Newton method, based on the rough estimate of the non-empty variant phase and the rough estimate of the maneuver factor, by solving the minimization of the image entropy, a refined estimate of the non-empty variant phase and a refined estimate of the maneuver factor are obtained.

[0078] Among them, the image entropy model characterizes the inverse synthetic aperture radar imaging effect.

[0079] According to an embodiment of the present disclosure, the quasi-Newton algorithm is an improvement of the Newton method. It calculates an approximate matrix instead of the Hessian matrix at a smaller cost to solve the problem of excessive computational amount of calculating the Hessian matrix. The relevant parameters of the empty variant phase and the relevant parameters of the maneuver factor can be constructed as an unknown quantity to be estimated, and the above rough estimates of the non-empty variant phase and the maneuver factor are substituted into the image entropy model to obtain a refined estimate of the non-empty variant phase and a refined estimate of the maneuver factor.

[0080] In operation S260, phase correction is performed on the preprocessed echo signal based on the refined estimated value of the non-constant phase and the refined estimated value of the maneuvering factor to obtain the corrected echo signal.

[0081] According to an embodiment of the present disclosure, after rough estimation and precise estimation, the precise rotation parameter θ of the target can be obtained through the above formula, and further, the correction of MTRC can be realized through the PFA algorithm.

[0082] In operation S270, an imaging result of the spatial maneuvering target is obtained based on the corrected echo signal.

[0083] According to an embodiment of the present disclosure, the rough estimation of the non-constant phase is performed by the eigenvector method and the rough estimation of the maneuvering factor is performed by a preset optimization algorithm. Thus, the rough estimated value of the non-constant phase and the rough estimated value of the maneuvering factor can be used as initial values to solve the minimization of image entropy through the quasi-Newton method, and the refined estimated values of the maneuvering factor and the non-constant phase can be obtained quickly, so as to correct the phase and amplitude of the echo signal to compensate for the distortion caused by platform movement, thereby improving the clarity and accuracy of imaging.

[0084] According to an embodiment of the present disclosure, the above-mentioned eigenvector method is used to obtain the rough estimated value of the non-constant phase based on the preprocessed echo signal processed by the target range cell, including: constructing an echo covariance matrix based on the echo signal frequency band data, where the echo signal frequency band data is obtained from the preprocessed echo signal based on the target range cell; solving the echo covariance matrix to obtain an echo eigenvector group; and obtaining the rough estimated value of the non-constant phase based on the echo eigenvector group and the echo signal frequency band data.

[0085] According to an embodiment of the present disclosure, the echo signal frequency band data can be extracted based on the target range cell and a covariance matrix can be constructed. The covariance matrix is a tool for describing the statistical characteristics of signals, and it contains the correlation information between the various components of the signal. By solving the eigenvectors of the covariance matrix, the main directions describing the signal structure can be obtained, and these directions correspond to the main change trends or characteristics in the signal. Finally, the rough estimated value of the non-constant phase can be obtained through the echo eigenvector group and the echo signal frequency band data.

[0086] According to an embodiment of the present disclosure, the rough estimation of the non-constant phase is performed by the eigenvector method, that is, by obtaining the echo signal frequency band data to construct a covariance matrix, and finally obtaining the rough estimated value of the non-constant phase based on the echo covariance matrix and the echo eigenvector group. The internal structural information of the preprocessed echo signal can be effectively utilized to estimate the phase error, thereby providing a basis for subsequent refined estimation.

[0087] According to an embodiment of the present disclosure, the above-mentioned method of using a preset optimization algorithm to obtain a rough estimate of the maneuvering factor based on the processed echo signal of the target range cell and the rough estimate of the non-empty variable phase includes: obtaining the echo signal frequency band data corresponding to the target range cell; randomly generating multiple candidate values of the maneuvering factor with different initial values; and iteratively solving to minimize the image entropy based on the multiple candidate values of the maneuvering factor, the rough estimate of the non-empty variable phase, and the echo signal frequency band data to obtain the rough estimate of the maneuvering factor.

[0088] According to an embodiment of the present disclosure, for the above formula (8), an unknown quantity to be estimated can be set , where ω represents the rotational angular velocity of the target, that is, the angle rotated per unit time, μ represents the maneuvering factor, Δx represents the coordinate offset of the scattering point on the target, and φ1, φ2, …, φ Na represent the phase terms related to the target motion, where Na is the number of these phase terms. Taking the minimization of the image entropy of the imaged image as a model, an optimization algorithm can be used to solve the rough estimate of the maneuvering factor. After discretizing z(f, f m ), z(m, n; k) is obtained, where the image entropy can be defined by the following formula (9).

[0089] (9)

[0090] Where E z is the energy of the imaging result z(m, n; k). Expanding the above formula (9), the following formula (10) can be obtained.

[0091] (10)

[0092] When the image entropy is minimized, the corresponding k value is the required estimated value. The image entropy minimization optimization model can be expressed by the following formula (11).

[0093] (11)

[0094] Since the parameter k to be estimated is the same for the data of each range cell, the above formula (10) can be rewritten as the following formula (12).

[0095] (12)

[0096] Where nz is the target range cell.

[0097] According to an embodiment of the present disclosure, the rough estimation of the maneuvering factor can be achieved through a particle swarm optimization algorithm. Different initial values are randomly generated as the maneuvering factor and candidate values. The above candidate values are used for subsequent iterative optimization processes, with the aim of finding the optimal maneuvering factor value to minimize the image entropy. During the iteration process, each candidate value of the maneuvering factor can be evaluated to calculate its corresponding image entropy, and the maneuvering factor that minimizes the image entropy is selected as the rough estimation value, thereby ensuring finding the optimal solution in the entire search space.

[0098] According to an embodiment of the present disclosure, the above determining the target range cell from multiple range cells includes: calculating the normalized amplitude variance for multiple range cells to obtain a group of normalized amplitude variances corresponding to the range cells; determining the range cell with the minimum normalized amplitude variance as the target range cell.

[0099] According to an embodiment of the present disclosure, the above using the quasi - Newton method to obtain the refined estimation value of the non - empty variant phase by solving the minimization of the image entropy based on the rough estimation value of the non - empty variant phase and the rough estimation value of the maneuvering factor includes: obtaining the gradient value of the spatially variant phase based on the rough estimation value of the non - empty variant phase; calculating the search direction based on the Hessian matrix and the gradient value of the spatially variant phase; iteratively updating the rough estimation value of the non - empty variant phase based on the search direction and the step size, where the step size is determined by the line search method; in the case where the norm of the gradient value of the spatially variant phase does not reach the preset threshold, returning to execute the step of obtaining the gradient of the spatially variant phase based on the rough estimation value of the non - empty variant phase; in the case where the norm of the gradient value of the spatially variant phase reaches the preset threshold, determining the updated rough estimation value as the refined estimation value of the non - empty variant phase.

[0100] According to an embodiment of the present disclosure, when solving for any element \(k\) in \(k\) i while fixing the values of other elements in \(k\), the following formula (13) is obtained.

[0101] (13)

[0102] As shown in Table 1 below, the pseudo - code for solving the minimization problem of \(H\) z \((k\) i ) is schematically shown.

[0103] Table 1

[0104]

[0105] Among them, the above - mentioned threshold can be, for example, the gradient threshold of the objective function, the change threshold of consecutive iterations, or the function value change threshold, etc. The present disclosure does not limit the setting of the threshold.

[0106] According to the embodiments of the present disclosure, by using the rough estimates of the non-empty variable phase and the maneuvering factor as the initial values of the iteration, the number of iterations can be reduced and the local optimum can be avoided. Further, through the two steps of rough estimation and fine estimation of the empty variable phase and the maneuvering factor, while maintaining the parameter accuracy, the operation speed is improved.

[0107] According to the embodiments of the present disclosure, the preprocessed echo signal is obtained through the following steps: performing dechirp compression on the echo signal to obtain the dechirped echo signal; using the fast Fourier transform algorithm to process the dechirped echo signal to obtain the transformed echo signal; performing residual phase compensation on the transformed echo signal to obtain the compensated echo signal; and performing envelope alignment processing on the compensated echo signal to obtain the preprocessed echo signal.

[0108] According to the embodiments of the present disclosure, the echo signal represented by the above formula (1) is subjected to range-direction fast Fourier transform, residual video phase (RVP) compensation, envelope alignment and other processing steps. That is, range compression is achieved through range-direction FFT, RVP term compensation is achieved by constructing a compensation term, and envelope alignment is achieved through the global minimum entropy envelope alignment algorithm.

[0109] According to the embodiments of the present disclosure, obtaining the imaging result of the spatial maneuvering target based on the corrected echo signal includes: performing range-direction Fourier transform on the corrected echo signal to obtain the Fourier-transformed echo signal; using the polar format transformation algorithm to obtain the transformed echo signal based on the Fourier-transformed echo signal; performing range-direction fast Fourier transform on the transformed echo signal to obtain the fast Fourier-transformed echo signal; performing phase gradient autofocus processing on the fast Fourier-transformed echo signal to obtain the processed echo signal; and performing azimuth-direction Fourier transform on the processed echo signal to obtain the imaging result of the spatial maneuvering target.

[0110] Figure 4 Schematically shows a schematic diagram of a point target according to the embodiments of the present disclosure.

[0111] As Figure 4 shown, where the abscissa represents the range-direction point target obtained by decomposing the echo signal, and the ordinate identifies the azimuth-direction point target obtained by decomposing the echo signal.

[0112] The following Table 2 shows the specific parameter values of the radar simulation settings.

[0113] Table 2

[0114] According to the embodiments of the present disclosure, the focusing degree of the imaging result is used as a qualitative index, and the image entropy and the image contrast are used as quantitative indexes. The following formula (14) shows the calculation method of the image entropy, and formula (15) shows the calculation method of the image contrast.

[0115] (14)

[0116] (15)

[0117] For the same data, the smaller the image entropy of the imaging result and the larger the image contrast, the better the imaging result.

[0118] Assume that the preprocessing of the data has been completed. According to the target turntable model, first analyze the imaging result of a uniformly rotating point target. Set the rotational angular velocity of the target to 0.13 / rad s, the angular acceleration to 0, and the angle that the target rotates within the imaging time is approximately 14.86 degrees. Figure 5 Schematically shows the imaging diagram of a uniformly rotating point target by the RD algorithm according to the embodiments of the present disclosure. Figure 6 Schematically shows the imaging result diagram of a uniformly rotating point target by the inverse synthetic aperture radar for space maneuvering targets based on the improved BFGS-PFA according to the embodiments of the present disclosure. As Figure 5 and Figure 6 shown, among which, obvious MTRC appears in the imaging result of the RD method, and the farther the scattering point migrates from the rotation center, the more serious the migration is. However, the algorithm of the present disclosure can better eliminate MTRC, and the focusing degree of the scattering points is better. The following Table 3 shows the comparison of imaging indexes of uniformly rotating targets.

[0119] Table 3

[0120] When the turntable model rotates with uniform acceleration, the imaging results of each algorithm for point targets are as Figure 5 shown. Figure 7 Schematically shows the schematic diagram of the imaging result of a point target by the RD algorithm with uniform acceleration rotation according to the embodiments of the present disclosure. Figure 8 Schematically shows the imaging diagram of a point target with uniform acceleration rotation by the inverse synthetic aperture radar for space maneuvering targets based on the improved BFGS-PFA according to the embodiments of the present disclosure. As Figure 7 , Figure 8 shown, the MTRC of the imaging result of the RD algorithm is more serious, and the rotational acceleration causes more obvious defocusing in the azimuth direction, and the farther away from the rotation center, the more serious the defocusing is. It can be clearly seen that the focusing degree of the imaging method proposed by the present disclosure is better than that of the traditional RD algorithm. The following Table 4 shows the comparison of imaging indexes of uniformly accelerating rotating targets.

[0121] Table 4

[0122]

[0123] Based on the above space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA, the present disclosure also provides a space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA. The following will be combined with Figure 8 to describe this device in detail.

[0124] Figure 9 Schematically shows a structural block diagram of a space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA according to an embodiment of the present disclosure.

[0125] As Figure 9 shown, the space maneuvering target inverse synthetic aperture radar imaging device 900 of this embodiment includes a range cell calculation module 910, a determination module 920, a non-empty variable phase rough estimation module 930, a maneuver factor rough estimation module 940, a fine estimation module 950, a correction module 960, and an imaging module 970.

[0126] The range cell calculation module 910 is used to calculate a plurality of range cells based on the preprocessed echo signal. Among them, the preprocessing represents that the echo signal is converted from a time-domain signal to a frequency-domain signal. The echo signal is a reflection signal received by the inverse synthetic aperture radar that changes with time. The range cell has a corresponding relationship with the frequency domain in the preprocessed echo signal. In one embodiment, the range cell calculation module 910 can be used to perform the operation S210 described above, which will not be elaborated here.

[0127] The determination module 920 is used to determine the target range cell from the plurality of range cells. In one embodiment, the determination module 920 can be used to perform the operation S220 described above, which will not be elaborated here.

[0128] The non-empty variable phase rough estimation module 930 is used to use the eigenvector method to process the preprocessed echo signal based on the target range cell to obtain a rough estimate value of the non-empty variable phase. In one embodiment, the non-empty variable phase rough estimation module 930 can be used to perform the operation S230 described above, which will not be elaborated here.

[0129] The maneuver factor rough estimation module 940 is used to use a preset optimization algorithm to process the preprocessed echo signal and the rough estimate value of the non-empty variable phase based on the target range cell to obtain a rough estimate value of the maneuver factor. In one embodiment, the maneuver factor rough estimation module 940 can be used to perform the operation S240 described above, which will not be elaborated here.

[0130] The fine estimation module 950 is used to use the quasi-Newton method to obtain the fine estimation value of the non-empty variable phase and the fine estimation value of the maneuvering factor by solving the minimization of the image entropy based on the coarse estimation value of the non-empty variable phase and the coarse estimation value of the maneuvering factor. The image entropy model characterizes the inverse synthetic aperture radar imaging effect. In one embodiment, the fine estimation module 950 can be used to perform the operation S250 described above, which will not be elaborated here.

[0131] The correction module 960 is used to perform phase correction on the preprocessed echo signal based on the fine estimation value of the non-empty variable phase and the fine estimation value of the maneuvering factor to obtain the corrected echo signal. In one embodiment, the correction module 960 can be used to perform the operation S260 described above, which will not be elaborated here.

[0132] The imaging module 970 is used to obtain the imaging result of the spatial maneuvering target based on the corrected echo signal. In one embodiment, the imaging module 970 can be used to perform the operation S270 described above, which will not be elaborated here.

[0133] According to an embodiment of the present disclosure, any multiple of the range cell calculation module 910, the determination module 920, the non-empty variable phase coarse estimation module 930, the maneuvering factor coarse estimation module 940, the fine estimation module 950, the correction module 960, and the imaging module 970 can be combined and implemented in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the range cell calculation module 910, the determination module 920, the non-empty variable phase coarse estimation module 930, the maneuvering factor coarse estimation module 940, the fine estimation module 950, the correction module 960, and the imaging module 970 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable means such as hardware or firmware through circuit integration or packaging, or can be implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the range cell calculation module 910, the determination module 920, the non-empty variable phase coarse estimation module 930, the maneuvering factor coarse estimation module 940, the fine estimation module 950, the correction module 960, and the imaging module 970 can be at least partially implemented as a computer program module, and when the computer program module runs, it can execute the corresponding functions.

[0134] Figure 10A block diagram of an electronic device suitable for implementing a spatial maneuvering target inverse synthetic aperture radar imaging method based on an improved BFGS-PFA according to an embodiment of the present disclosure is schematically shown.

[0135] As Figure 10 shown, the electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include on-board memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0136] In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. The processor 1001 performs various operations of the method flow according to an embodiment of the present disclosure by executing the programs in the ROM 1002 and / or the RAM 1003. It should be noted that the programs may also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to an embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0137] According to an embodiment of the present disclosure, the electronic device 1000 may further include an input / output (I / O) interface 1005, and the input / output (I / O) interface 1005 is also connected to the bus 1004. The electronic device 1000 may further include one or more of the following components connected to the input / output (I / O) interface 1005: an input portion 1006 including a keyboard, a mouse, etc.; an output portion 1007 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk, etc.; and a communication portion 1009 including a network interface card such as a LAN card, a modem, etc. The communication portion 1009 performs communication processing via a network such as the Internet. A driver 1010 is also connected to the input / output (I / O) interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the driver 1010 as needed so that a computer program read from it can be installed into the storage portion 1008 as needed.

[0138] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0139] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 1002 and / or RAM 1003 and / or one or more memories other than ROM 1002 and RAM 1003.

[0140] An embodiment of the present disclosure further includes a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program code is used to cause the computer system to implement the method for inverse synthetic aperture radar imaging of a space maneuvering target based on improved BFGS-PFA provided by the embodiments of the present disclosure.

[0141] When the computer program is executed by the processor 1001, the above functions defined in the system / apparatus of the embodiments of the present disclosure are executed. According to an embodiment of the present disclosure, the above-described systems, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0142] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and be downloaded and installed through the communication part 1009, and / or be installed from the removable medium 1011. The program code included in the computer program may be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0143] In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above-described functions defined in the system of the embodiments of the present disclosure are executed. According to the embodiments of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0144] According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedures and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, python, the "C" language, or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0145] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0146] Those skilled in the art can understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0147] The embodiments of the present disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. A method for inverse synthetic aperture radar imaging of space maneuvering targets based on improved BFGS-PFA, characterized in that, The space maneuvering target makes relative motion with respect to the inverse synthetic aperture radar, and the method includes: Calculating a plurality of range cells based on the preprocessed echo signal, where the preprocessing represents the conversion of the echo signal from a time-domain signal to a frequency-domain signal, the echo signal is a reflection signal received by the inverse synthetic aperture radar that changes with time, and the range cells have a corresponding relationship with the frequency domain in the preprocessed echo signal; Determining a target range cell from the plurality of range cells; Using the eigenvector method, processing the preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variant phase; Using a preset optimization algorithm, processing the preprocessed echo signal and the rough estimate of the non-empty variant phase based on the target range cell to obtain a rough estimate of the maneuvering factor; Using the quasi-Newton method, based on the rough estimate of the non-empty variant phase and the rough estimate of the maneuvering factor, by solving the minimization of the image entropy, obtaining the refined estimate of the non-empty variant phase and the refined estimate of the maneuvering factor, where the image entropy characterizes the imaging effect of the inverse synthetic aperture radar; Performing phase correction on the preprocessed echo signal based on the refined estimate of the non-empty variant phase and the refined estimate of the maneuvering factor to obtain a corrected echo signal; Obtaining an imaging result of the space maneuvering target based on the corrected echo signal; The using the quasi-Newton method, based on the rough estimate of the non-empty variant phase and the rough estimate of the maneuvering factor, by solving the minimization of the image entropy, obtaining the refined estimate of the non-empty variant phase includes: Obtaining the gradient value of the non-empty variant phase based on the rough estimate of the non-empty variant phase; Calculating a search direction based on the Hessian matrix and the gradient value of the non-empty variant phase; Iteratively updating the rough estimate of the non-empty variant phase based on the search direction and the step size, where the step size is determined by the line search method; In the case where the norm of the gradient value of the non-empty variant phase does not reach a preset threshold, returning to execute the step of obtaining the gradient of the non-empty variant phase based on the rough estimate of the non-empty variant phase; In the case where the norm of the gradient value of the non-empty variant phase reaches a preset threshold, determining the updated rough estimate as the refined estimate of the non-empty variant phase.

2. The method according to claim 1, wherein The using the eigenvector method, processing the preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variant phase includes: Constructing an echo covariance matrix based on the echo signal frequency band data, where the echo signal frequency band data is obtained from the preprocessed echo signal based on the target range cell; Solving to obtain an echo eigenvector group based on the echo covariance matrix; Obtaining the rough estimate of the non-empty variant phase based on the echo eigenvector group and the echo signal frequency band data.

3. The method according to claim 1, characterized in that, The using a preset optimization algorithm, processing the preprocessed echo signal and the rough estimate of the non-empty variant phase based on the target range cell to obtain a rough estimate of the maneuvering factor includes: Obtaining the echo signal frequency band data corresponding to the target range cell; Randomly generating a plurality of maneuvering factor candidate values with different initial values; Iteratively solve for minimizing the image entropy based on the multiple candidate values of the maneuvering factor, the rough estimate of the non-empty variable phase, and the echo signal frequency band data to obtain the rough estimate of the maneuvering factor.

4. The method according to claim 1, characterized in that The determining the target range cell from the multiple range cells includes: Calculating the normalized amplitude variance for the multiple range cells to obtain a group of normalized amplitude variances corresponding to the range cells; Determining the range cell with the minimum normalized amplitude variance as the target range cell.

5. The method according to claim 1, characterized in that The preprocessed echo signal is obtained through the following steps: Performing dechirp compression on the echo signal to obtain the dechirp-compressed echo signal; Using the fast Fourier transform algorithm to process the dechirp-compressed echo signal to obtain the transformed echo signal; Performing residual phase compensation on the transformed echo signal to obtain the compensated echo signal; Performing envelope alignment processing on the compensated echo signal to obtain the preprocessed echo signal.

6. The method according to claim 1, characterized in that, The obtaining the imaging result of the space maneuvering target based on the corrected echo signal includes: Performing range-direction Fourier transform on the corrected echo signal to obtain the Fourier-transformed echo signal; Using the polar format transformation algorithm to obtain the transformed echo signal based on the Fourier-transformed echo signal; Performing range-direction fast Fourier transform on the transformed echo signal to obtain the fast Fourier-transformed echo signal; Performing phase gradient autofocus processing on the fast Fourier-transformed echo signal to obtain the processed echo signal; Performing azimuth-direction Fourier transform on the processed echo signal to obtain the imaging result of the space maneuvering target.

7. A space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA, characterized in that, The device includes: A range cell calculation module for calculating multiple range cells based on the preprocessed echo signal, where the preprocessing represents the conversion of the echo signal from a time-domain signal to a frequency-domain signal, the echo signal is a reflection signal received by the inverse synthetic aperture radar that changes with time, and the range cells have a corresponding relationship with the frequency domain in the preprocessed echo signal; A determination module for determining a target range cell from the multiple range cells; A non-empty variable phase rough estimation module for using the eigenvector method to process the preprocessed echo signal based on the target range cell to obtain a rough estimate of the non-empty variable phase; A maneuvering factor rough estimation module for using a preset optimization algorithm to process the preprocessed echo signal and the rough estimate of the non-empty variable phase based on the target range cell to obtain a rough estimate of the maneuvering factor; A fine estimation module for using the quasi-Newton method to obtain a fine estimate of the non-empty variable phase and a fine estimate of the maneuvering factor by solving for minimizing the image entropy based on the rough estimate of the non-empty variable phase and the rough estimate of the maneuvering factor, where the image entropy characterizes the inverse synthetic aperture radar imaging effect; A correction module for performing phase correction on the preprocessed echo signal based on the fine estimate of the non-empty variable phase and the fine estimate of the maneuvering factor to obtain the corrected echo signal; An imaging module for obtaining the imaging result of the space maneuvering target based on the corrected echo signal; The fine estimation module includes: a gradient value determination sub-module, configured to obtain the gradient value of the non-empty variable phase based on the rough estimation value of the non-empty variable phase; a search direction determination sub-module, configured to calculate a search direction based on the Hessian matrix and the gradient value of the non-empty variable phase; a rough estimation value update sub-module, configured to iteratively update the rough estimation value of the non-empty variable phase based on the search direction and a step size, where the step size is determined by a line search method; a return execution sub-module, configured to return and execute the step of obtaining the gradient of the non-empty variable phase based on the rough estimation value of the non-empty variable phase when the norm of the gradient value of the empty variable phase does not reach a preset threshold; a fine estimation value determination sub-module, configured to determine the updated rough estimation value as the fine estimation value of the non-empty variable phase when the norm of the gradient value of the non-empty variable phase reaches the preset threshold.

8. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instruction, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

Images