Space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA

Abstract

The invention provides a space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA. The method comprises the following steps: calculating a plurality of distance units based on preprocessed echo signals; determining a target distance unit from the plurality of distance units; utilizing a feature vector method to process the preprocessed echo signal based on a target distance unit to obtain a rough estimation value of a space-variant phase; utilizing a preset optimization algorithm to process the preprocessed echo signal and the rough estimation value of the space-variant phase based on the target distance unit to obtain a rough estimation value of the maneuvering factor; a quasi-Newton method is utilized, and based on the rough estimation value of the space-variant phase and the rough estimation value of the maneuvering factor, a precise estimation value of the space-variant phase and a precise estimation value of the maneuvering factor are obtained by solving a minimum image entropy; performing phase correction on the preprocessed echo signal based on the fine estimation value of the space-variant phase and the fine estimation value 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 specifically to an inverse synthetic aperture radar imaging method for a space maneuvering target based on an improved BFGS-PFA. Background Art

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

[0003] In the related art, the PFA algorithm is used to suppress the migration of the cross-resolution unit of the imaging result. It is necessary to use the global optimization algorithm to minimize the image entropy model to obtain the estimation of the target rotation parameters. However, the global optimization algorithm has high computational cost and is time-consuming, which is not conducive to real-time data processing. Moreover, due to the fast movement speed and strong maneuverability of the space target, the rotation acceleration of its turntable model cannot be ignored. In addition, in order to reduce the impact of the reduction in the accuracy of the non-space-variant phase error compensation under large rotation angles, the non-space-variant phase error should be further finely compensated in the iteration. Summary of the invention

[0004] In view of the above problems, the present disclosure provides an inverse synthetic aperture radar imaging method for 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 an improved BFGS-PFA is provided, wherein the space maneuvering target moves relative to the inverse synthetic aperture radar, comprising: calculating a plurality of distance units based on a preprocessed echo signal, wherein the preprocessing characterizes 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 varies with time, and the distance unit has a corresponding relationship with the frequency domain in the preprocessed echo signal; determining a target distance unit from the plurality of distance units; and using a eigenvector method to process the preprocessed echo signal based on the target distance unit to obtain a space-varying A rough estimate of the phase; using a preset optimization algorithm, based on the target distance unit, processing the preprocessed echo signal and the rough estimate of the space-varying phase to obtain a rough estimate of the maneuvering factor; using a quasi-Newton method, based on the rough estimate of the space-varying phase and the rough estimate of the maneuvering factor, by solving the minimized image entropy, obtaining a precise estimate of the space-varying phase and a precise estimate of the maneuvering factor, the image entropy model characterizes the imaging effect of the inverse synthetic aperture radar; based on the precise estimate of the space-varying phase and the precise estimate of the maneuvering factor, performing phase correction on the preprocessed echo signal to obtain a corrected echo signal; based on the corrected echo signal, obtaining the imaging result of the space maneuvering target.

[0006] According to an embodiment of the present disclosure, the above-mentioned use of the eigenvector method to process the above-mentioned preprocessed echo signal based on the target distance unit to obtain a rough estimate of the space-varying phase includes: constructing an echo covariance matrix based on the echo signal frequency band data, wherein the above-mentioned echo signal frequency band data is obtained from the above-mentioned preprocessed echo signal based on the target distance unit; obtaining an echo eigenvector group based on the above-mentioned echo covariance matrix; and obtaining a rough estimate of the above-mentioned space-varying phase based on the above-mentioned echo eigenvector group and the above-mentioned echo signal frequency band data.

[0007] According to an embodiment of the present disclosure, the above-mentioned use of a preset optimization algorithm to obtain a rough estimate of the maneuvering factor based on the above-mentioned target distance unit by processing the above-mentioned preprocessed echo signal and the rough estimate of the above-mentioned space-varying phase includes: acquiring echo signal frequency band data corresponding to the above-mentioned target distance unit; randomly generating a plurality of maneuvering factor candidate values ​​with different initial values; iteratively solving the minimization of image entropy based on the above-mentioned plurality of maneuvering factor candidate values, the rough estimate of the above-mentioned space-varying phase and the above-mentioned echo signal frequency band data to obtain the rough estimate of the above-mentioned maneuvering factor.

[0008] According to an embodiment of the present disclosure, the above-mentioned determining the target distance unit from the above-mentioned multiple distance units includes: calculating the normalized amplitude variance of the above-mentioned multiple distance units to obtain a normalized amplitude variance group corresponding to the above-mentioned 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, the quasi-Newton method is used to obtain a precise estimate of the space-varying phase based on a rough estimate of the space-varying phase and a rough estimate of the maneuvering factor by solving the minimized image entropy, including: obtaining a gradient value of the space-varying phase based on the rough estimate of the space-varying phase; calculating a search direction based on the Hessian matrix and the gradient value of the space-varying phase; iteratively updating the rough estimate of the space-varying phase based on the search direction and the step size, wherein the step size is determined by a line search method; when the norm of the gradient value of the space-varying phase does not reach a preset threshold, returning to the step of obtaining the gradient of the space-varying phase based on the rough estimate of the space-varying phase; when the norm of the gradient value of the space-varying phase reaches a preset threshold, determining the updated rough estimate as the precise estimate of the space-varying phase.

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

[0011] According to an embodiment of the present disclosure, the imaging result of the above-mentioned space maneuvering target based on the above-mentioned corrected echo signal includes: performing a range-oriented Fourier transform on the above-mentioned corrected echo signal to obtain a Fourier transformed echo signal; using a polar coordinate format transformation algorithm to obtain a transformed echo signal based on the above-mentioned Fourier transformed echo signal; performing a range-oriented fast Fourier transform on the above-mentioned transformed echo signal to obtain a fast Fourier transformed echo signal; performing a phase gradient self-focusing process on the above-mentioned fast Fourier transformed echo signal to obtain a processed echo signal; performing an azimuth Fourier transform on the above-mentioned processed echo signal to obtain the imaging result of the above-mentioned space 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 distance unit calculation module, used to calculate multiple distance units based on a preprocessed echo signal, wherein the preprocessing characterizes 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 and is received by the inverse synthetic aperture radar, and the distance unit has a corresponding relationship with the frequency domain in the preprocessed echo signal; a determination module, used to determine a target distance unit from the multiple distance units; a space-varying phase coarse estimation module, used to use a eigenvector method to process the preprocessed echo signal based on the target distance unit to obtain a coarse estimation value of the space-varying phase; a maneuvering factor coarse estimation module , used to use a preset optimization algorithm to process the pre-processed echo signal and the rough estimate of the space-varying phase based on the target distance unit to obtain a rough estimate of the maneuvering factor; a precise estimation module, used to use a quasi-Newton method, based on the rough estimate of the space-varying phase and the rough estimate of the maneuvering factor by solving the minimized image entropy, to obtain the precise estimate of the space-varying phase and the precise estimate of the maneuvering factor, the image entropy model characterizes the imaging effect of the inverse synthetic aperture radar; a correction module, used to perform phase correction on the pre-processed echo signal based on the precise estimate of the space-varying phase and the precise estimate of the maneuvering factor to obtain a corrected echo signal; an imaging module, used to obtain the imaging result of the space maneuvering target based on the corrected echo signal.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and 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 above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

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

[0016] According to the embodiments of the present disclosure, the space-varying phase is roughly estimated by the eigenvector method and the maneuvering factor is roughly estimated by a preset optimization algorithm, so that the rough estimated value of the space-varying phase and the rough estimated value of the maneuvering factor can be used as initial values ​​to solve the minimized image entropy through the quasi-Newton method, and the precise estimated values ​​of the maneuvering factor and the space-varying phase can be obtained at an accelerated speed, so as to correct the phase and amplitude of the echo signal to compensate for the distortion caused by the platform motion, thereby improving the clarity and accuracy of imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 The application scenario diagram of the space maneuvering target inverse synthetic aperture radar imaging method based on the improved BFGS-PFA according to the embodiment of the present disclosure is schematically shown;

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

[0020] Figure 3 A schematic diagram of a geometric model of ISAR imaging of a space target according to an embodiment of the present disclosure is shown;

[0021] Figure 4 A schematic diagram of a point target according to an embodiment of the present disclosure is schematically shown;

[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 diagram of a space maneuvering target inverse synthetic aperture radar imaging of a uniformly rotating point target based on an improved BFGS-PFA according to an embodiment of the present disclosure;

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

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

[0026] Fig. 9 The structure block diagram of the space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA according to an embodiment of the present disclosure is schematically shown;

[0027] Fig.10 A block diagram of an electronic device suitable for implementing an improved BFGS-PFA-based inverse synthetic aperture radar imaging method for space maneuvering targets according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[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 exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

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

[0030] All terms (including technical and scientific terms) used herein 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] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] In ISAR imaging of maneuvering targets in space, 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 center of the target. Assuming that the target rotational motion is a small-angle uniform rotation, after translational compensation, the RD algorithm realizes imaging in the range direction by compressing the pulse of the broadband echo signal, and 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 condition of large-angle and high-maneuvering targets, the assumption of small-angle uniform rotation fails, the space-varying phase caused by large angles cannot be ignored, and the cross-resolution unit migration of the imaging result is aggravated. At the same time, the coupling between the translational component and the rotational component at large angles will also reduce the accuracy of non-space-varying phase error compensation, and also lead to a decrease in imaging quality.

[0033] In related technologies, the PFA (Polar Format Algorithm) algorithm can be used to suppress MTRC (Migration Through Resolution Cell), but the PFA algorithm needs to take the target rotation parameters as known conditions to participate in the calculation to achieve the suppression of MTRC. The global optimization algorithm can be used to minimize the image entropy model to achieve the estimation of the target rotation parameters, but the global optimization algorithm has high computational cost and is time-consuming, which is not conducive to real-time data processing. Moreover, due to the fast movement speed and strong maneuverability of space targets, the rotation acceleration of the turntable model cannot be ignored. In addition, in order to reduce the impact of the reduction in the accuracy of non-space-varying phase error compensation under large rotation angles, the non-space-varying phase error should be further finely compensated in the iteration.

[0034] In view of this, an embodiment of the present disclosure provides an inverse synthetic aperture radar imaging method for a space maneuvering target based on an improved BFGS-PFA, comprising: calculating a plurality of distance units based on a preprocessed echo signal, wherein the preprocessing characterizes 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 varies with time, and the distance unit has a corresponding relationship with the frequency domain in the preprocessed echo signal; determining a target distance unit from the plurality of distance units; using a eigenvector method, processing the preprocessed echo signal based on the target distance unit to obtain a rough estimate of the space-varying phase value; using a preset optimization algorithm, a rough estimate of the maneuvering factor is obtained by processing the preprocessed echo signal and the rough estimate of the space-varying phase based on the target distance unit; using the quasi-Newton method, based on the rough estimate of the space-varying phase and the rough estimate of the maneuvering factor, the precise estimate of the space-varying phase and the precise estimate of the maneuvering factor are obtained by solving the minimized image entropy, and the image entropy model characterizes the imaging effect of the inverse synthetic aperture radar; based on the precise estimate of the space-varying phase and the precise estimate of the maneuvering factor, the preprocessed echo signal is phase corrected to obtain a corrected echo signal; based on the corrected echo signal, the imaging result of the space maneuvering target is obtained.

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

[0036] like 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 a communication link 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 optical fiber cables, etc.

[0037] The user 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 can 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. (only for example).

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

[0039] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for 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 the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0040] Radar 106 may be an inverse synthetic aperture radar. After receiving the echo signal reflected by the space maneuvering target, the radar may perform preliminary processing on the signal and transmit it to server 105 through network 104 for further processing. Alternatively, the radar 106 may process the echo signal.

[0041] It should be noted that the space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA provided in the embodiment of the present disclosure can generally be performed by the server 105 or the radar 106. Accordingly, the space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA provided in the embodiment of the present disclosure can generally be set in the server 105 or the radar 106. The space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA provided in the embodiment of the present disclosure can also be performed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate 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 number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0043] The following will be based on Figure 1 The scene described by Figure 2~Figure 8 The inverse synthetic aperture radar imaging method for space maneuvering targets based on improved BFGS-PFA of the disclosed embodiment is described in detail.

[0044] Figure 2 The flowchart of the inverse synthetic aperture radar imaging method of a space maneuvering target based on the improved BFGS-PFA according to an embodiment of the present disclosure is schematically shown.

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

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

[0047] Among them, the preprocessing characterizes 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 varies with time, and the range unit has a corresponding relationship with the frequency domain in the preprocessed echo signal.

[0048] Figure 3 The geometric model diagram of ISAR imaging of space targets according to an embodiment of the present disclosure is schematically shown.

[0049] like Figure 3 As shown, the geometric model diagram according to this embodiment may include the radar 106 and the space maneuvering target 301, wherein a scattering point with coordinates (x, y) on the space maneuvering target 301 (hereinafter referred to as the target) is at a distance R from the radar 106. p , the distance from the rotation center is R, and the backscatter coefficient of this point is ρ(x, y). In the traditional RD algorithm, the target motion can be decomposed into a translational motion R(t m ) and the rotation around the rotation center o, the distance between the point and the radar can be expressed as R p (t m ). Among them, t is the fast time (distance time), t m It is slow time (azimuth time).

[0050] According to the embodiment of the present disclosure, the above radar may be, for example, an inverse synthetic aperture radar, and the radar transmission signal may be, for example, an LFM (Linear Frequency Modulation) signal. Without considering the attenuation during signal propagation, the radial velocity of the target may be fitted to construct a compensation term to achieve intra-pulse compensation of the echo signal. After the target intra-pulse motion correction, the echo signal is de-slanted and compressed to obtain an 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 time), t m is the slow time (azimuth time), ρ(x, y) represents the scattering coefficient of the scattering 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 delay term of the echo signal, T p is the pulse width, represents the Doppler term of the echo signal, where j is an imaginary unit, K r is the frequency modulation slope in the radar line of sight, R Δ (t m ) is the change in distance 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, which is used to compensate for the secondary 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 relative to the radar, c represents the speed of light, R Δ (t m ) can be obtained by the following formula (2).

[0053] (2)

[0054] Among them, 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 center of rotation.

[0055] According to the 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 echo signal after preprocessing, 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 Indicates pulse width, K r Represents the frequency modulation slope in the radar line of sight direction, R Δ Indicates the change in the relative distance of the target relative to the radar. Represents the carrier frequency term of the echo signal, where j is an imaginary unit, f c is the carrier frequency, is the non-space-varying phase error term, represents the angular velocity of the target, and γ represents the rotation acceleration of the target.

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

[0059] (4)

[0060] Among them, θ(t m ) indicates that the target is at slow time t m The rotation angle at the moment, ω represents the angular velocity of the target, that is, the angle of rotation per unit time, t m represents the slow time, γ represents the rotation acceleration of the target, represents the quadratic term of the rotation angle caused by the rotation acceleration. Substituting the above formula (4) into R Δ , and perform a second-order Taylor expansion to obtain the following formula (5).

[0061] (5)

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

[0063] (6)

[0064] in, At this time, the range parameter f and the azimuth parameter t in the sinc term m The coupling can no longer be ignored, and this coupling causes MTRC in the range direction. At this time, the exponential term appears as a quadratic term in time, that is, the space-varying phase. If the azimuth FFT (Fast Fourier Transform) is directly performed, it will cause severe defocusing in the azimuth direction, that is, MTRC in the azimuth direction.

[0065] According to an embodiment of the present disclosure, multiple distance units can be calculated by methods such as cross-correlation method, distance centroid method, minimum variance method, etc., each distance unit corresponds to a frequency domain of the preprocessed echo signal, and the present disclosure does not limit the method for calculating the distance unit.

[0066] In operation S220, a target distance unit is determined from among the plurality of distance units.

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

[0068] In operation S230, a rough estimation value of a space-varying phase is obtained by processing the pre-processed echo signal based on the target range unit using a eigenvector method.

[0069] According to the embodiment of the present disclosure, as mentioned above, if the azimuth FFT is directly performed on the pre-processed echo signal represented by formula (6), severe defocusing will occur in the azimuth direction. In this case, the azimuth MTRC needs to be compensated. The azimuth compensation can be performed by using MFT (Matching Fourier Transform) in the above formula (6) to obtain the following formula (7).

[0070] (7)

[0071] Among them, z(f,fm) represents the signal after the azimuth Fourier transform (MFT), where f is the frequency, f m is the frequency variable in the azimuth direction, S(f,t m ) represents the echo signal after preprocessing 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, and 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 angular velocity of the target, and MFT az To match the Fourier transform in azimuth, it can be expressed by the following formula (8).

[0072] (8)

[0073] Among them, f q is the azimuth frequency vector, is the maneuvering factor. When the rotational acceleration γ is 0, the transformation will degenerate into Fourier transform.

[0074] According to the embodiment of the present disclosure, after determining that the distance unit is not a prominent point unit, a PGA (Phase Gradient Autofocus) algorithm may be used to estimate the non-space-varying phase.

[0075] In operation S240, a rough estimate of the maneuvering factor is obtained based on the preprocessed echo signal and the rough estimate of the space-varying phase processed by the target range unit by using a preset optimization algorithm.

[0076] According to the embodiments of the present disclosure, the above-mentioned preset optimization algorithm can be, for example, a gradient algorithm, a particle swarm optimization algorithm, a genetic algorithm, etc. The gradient algorithm is a local optimization algorithm that converges faster and takes less time than the global optimization algorithm. The gradient algorithm is prone to fall into the local optimum when estimating multivariate parameters. Therefore, a single or multiple distance units deviating from the center of rotation can be selected, and a global optimization algorithm such as particle swarm optimization can be used to minimize the entropy function to achieve a rough estimate of the maneuvering factor.

[0077] In operation S250, a quasi-Newton method is used to obtain a precise estimate of the space-varying phase and a precise estimate of the maneuvering factor by solving a problem of minimizing image entropy based on a rough estimate of the space-varying phase and a rough estimate of the maneuvering factor.

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

[0079] According to the embodiments of the present disclosure, the quasi-Newton algorithm is an improvement on the Newton method. It calculates an approximate matrix at a lower cost instead of the Hessian matrix to solve the problem of excessive computational complexity in calculating the Hessian matrix. The relevant parameters of the space-varying phase and the relevant parameters of the maneuvering factor can be constructed as an unknown quantity to be estimated, and the rough estimated value of the space-varying phase and the rough estimated value of the maneuvering factor can be substituted into the image entropy model to obtain the precise estimated value of the space-varying phase and the precise estimated value of the maneuvering factor.

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

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

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

[0083] According to the embodiments of the present disclosure, the space-varying phase is roughly estimated by the eigenvector method and the maneuvering factor is roughly estimated by a preset optimization algorithm, so that the rough estimated value of the space-varying phase and the rough estimated value of the maneuvering factor can be used as initial values ​​to solve the minimized image entropy through the quasi-Newton method, and the precise estimated values ​​of the maneuvering factor and the space-varying phase can be obtained at an accelerated speed, so as to correct the phase and amplitude of the echo signal to compensate for the distortion caused by the platform motion, thereby improving the clarity and accuracy of imaging.

[0084] According to an embodiment of the present disclosure, the above-mentioned use of the eigenvector method to obtain a rough estimate of the space-varying phase based on the target distance unit processing the preprocessed echo signal includes: constructing an echo covariance matrix based on the echo signal frequency band data, wherein the echo signal frequency band data is obtained from the preprocessed echo signal based on the target distance unit; obtaining an echo eigenvector group based on the echo covariance matrix; and obtaining a rough estimate of the space-varying phase based on the echo eigenvector group and the echo signal frequency band data.

[0085] According to the disclosed embodiment, the echo signal frequency band data can be extracted based on the target distance unit and a covariance matrix can be constructed. The covariance matrix is ​​a tool for describing the statistical characteristics of a signal, which 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, which correspond to the main change trends or characteristics in the signal. Finally, a rough estimate of the space-varying phase can be obtained through the echo eigenvector group and the echo signal frequency band data. Space-varying phase refers to the phase change caused by the movement of the target or radar, which changes with time and space. Through the eigenvector method, we can estimate this phase change and provide a basis for subsequent precise compensation.

[0086] According to the embodiments of the present disclosure, the space-varying phase is roughly estimated by the eigenvector method, that is, the covariance matrix is ​​constructed by acquiring the frequency band data of the echo signal, and the echo eigenvector group is obtained based on the echo covariance matrix to finally obtain a rough estimate of the space-varying phase. The intrinsic structural information of the preprocessed echo signal can be effectively used to estimate the phase error, thereby providing a solution for subsequent precise estimation.

[0087] According to an embodiment of the present disclosure, the above-mentioned use of a preset optimization algorithm to obtain a rough estimate of the maneuvering factor based on the target distance unit processing preprocessed echo signal and a rough estimate of the space-varying phase includes: acquiring echo signal frequency band data corresponding to the target distance unit; randomly generating multiple maneuvering factor candidate values ​​with different initial values; iteratively solving the minimization of image entropy based on the multiple maneuvering factor candidate values, the rough estimate of the space-varying phase and the echo signal frequency band data to obtain a rough estimate of the maneuvering factor.

[0088] According to the embodiment of the present disclosure, for the above formula (8), an unknown quantity to be estimated can be set as , where ω represents the angular velocity of the target, that is, the angle of rotation per unit time, μ represents the maneuvering factor, Δx represents the coordinate offset of the scattering point on the target, φ1, φ2, …, φ Na represents the phase term associated with the target motion, where Na is the number of these phase terms. Taking the image entropy minimization after imaging as a model, an optimization algorithm can be used to solve the rough estimate of the maneuvering factor, converting z(f, f m ) is discretized to obtain z(m, n; k), where the image entropy can be defined using the following formula (9).

[0089] (9)

[0090] Among them, E z is the energy of the imaging result z(m, n; k), and by expanding the above formula (9), we can obtain the following formula (10).

[0091] (10)

[0092] When the image entropy is minimum, the corresponding k value is the required estimated value, and 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 distance unit, the above formula (10) can be rewritten as the following formula (12).

[0095] (12)

[0096] Among them, nz is the target distance unit.

[0097] According to the embodiments of the present disclosure, a rough estimation of the maneuvering factor can be achieved through a particle swarm optimization algorithm, and different initial values ​​are randomly generated as the maneuvering factor and candidate values. The above candidate values ​​are used in a subsequent iterative optimization process, and the purpose is to find the best maneuvering factor value to minimize the image entropy. During the iterative process, each maneuvering factor candidate value can be evaluated to calculate its corresponding image entropy, and the maneuvering factor that minimizes the image entropy is selected as the rough estimate value, thereby ensuring that the optimal solution is found in the entire search space.

[0098] According to an embodiment of the present disclosure, the above-mentioned determining a target distance unit from a plurality of distance units includes: calculating normalized amplitude variances for a plurality of distance units to obtain a normalized amplitude variance group corresponding to the distance units; and determining the distance unit with the smallest normalized amplitude variance as the target distance unit.

[0099] According to an embodiment of the present disclosure, the above-mentioned use of the quasi-Newton method, based on the rough estimate of the space-varying phase and the rough estimate of the maneuvering factor by solving the minimized image entropy, to obtain the precise estimate of the space-varying phase includes: obtaining the gradient value of the space-varying phase based on the rough estimate of the space-varying phase; calculating the search direction based on the Hessian matrix and the gradient value of the space-varying phase; iteratively updating the rough estimate of the space-varying phase based on the search direction and the step size, and the step size is determined by the line search method; when the norm of the gradient value of the space-varying phase does not reach a preset threshold, returning to execute the step of obtaining the gradient of the space-varying phase based on the rough estimate of the space-varying phase; when the norm of the gradient value of the space-varying phase reaches a preset threshold, determining the updated rough estimate as the precise estimate of the space-varying phase.

[0100] According to the embodiment of the present disclosure, in solving k, any element k i When , the values ​​of other elements in k are fixed to obtain the following formula (13).

[0101] (13)

[0102] Table 1 shows the solution for H z (k i ) Pseudocode for the minimization problem.

[0103] Table 1

[0104]

[0105] The above threshold may be, for example, a gradient threshold of an objective function, a change threshold of a continuous iteration section, or a function value change threshold, etc. The present disclosure does not impose any restrictions on the setting of the threshold.

[0106] According to the embodiments of the present disclosure, by using the rough estimate of the space-variable phase and the rough estimate of the maneuvering factor as iterative processing, the number of iterations can be reduced and falling into the local optimum can be avoided. Furthermore, through the two steps of coarse estimation and precise estimation of the space-variable phase and the maneuvering factor, the calculation speed is improved while maintaining the parameter accuracy.

[0107] According to the embodiment of the present disclosure, the above-mentioned preprocessed echo signal is obtained through the following steps: performing de-skew compression on the echo signal to obtain a de-skew compressed echo signal; using a fast Fourier transform algorithm to process the de-skew compressed echo signal to obtain a transformed echo signal; performing residual phase compensation on the transformed echo signal to obtain a compensated echo signal; performing envelope alignment processing on the compensated echo signal to obtain a preprocessed echo signal.

[0108] According to the embodiment of the present disclosure, the echo signal represented by the above formula (1) is processed by steps such as distance fast Fourier transform, residual video phase (RVP) compensation, and envelope alignment. That is, distance compression is achieved by distance FFT, RVP item compensation is achieved by constructing compensation items, and envelope alignment is achieved by the global minimum entropy envelope alignment algorithm.

[0109] According to an embodiment of the present disclosure, the imaging result of the space maneuvering target based on the corrected echo signal includes: performing a range-wise Fourier transform on the corrected echo signal to obtain a Fourier transformed echo signal; using a polar coordinate format transformation algorithm to obtain a transformed echo signal based on the Fourier transformed echo signal; performing a range-wise fast Fourier transform on the transformed echo signal to obtain a fast Fourier transformed echo signal; performing a phase gradient self-focusing process on the fast Fourier transformed echo signal to obtain a processed echo signal; and performing an azimuth Fourier transform on the processed echo signal to obtain the imaging result of the space maneuvering target.

[0110] Figure 4 A schematic diagram of a point target according to an embodiment of the present disclosure is schematically shown.

[0111] like Figure 4 As shown, the horizontal axis represents the distance to the point target obtained by decomposing the echo signal, and the vertical axis represents the azimuth to the point target obtained by decomposing the echo signal.

[0112] Table 2 below shows the specific parameter values ​​of the radar simulation settings.

[0113] Table 2

[0114]

[0115] According to the embodiment of the present disclosure, the focusing degree of the imaging result is used as a qualitative indicator, and the image entropy and image contrast are used as quantitative indicators. The following formula (14) shows a method for calculating the image entropy, and formula (15) shows a method for calculating the image contrast.

[0116] (14)

[0117] (15)

[0118] For the same data, the smaller the image entropy of the imaging result and the greater the image contrast, the better the imaging result.

[0119] Assuming that the data preprocessing has been completed, according to the target turntable model, the imaging results of the uniformly rotating point target are first analyzed. The target's angular velocity is set to 0.13 / rad s, the angular acceleration is 0, and the target rotates by about 14.86 degrees during the imaging time. Figure 5 The RD algorithm imaging diagram of a uniformly rotating point target according to an embodiment of the present disclosure is schematically shown. Figure 6 The schematic diagram shows the imaging of the space maneuvering target inverse synthetic aperture radar based on the improved BFGS-PFA of a uniformly rotating point target according to an embodiment of the present disclosure. Figure 5 and Figure 6 As shown, the imaging result of the RD method produces obvious MTRC, and the farther away from the rotation center, the more serious the migration of the scattered points. However, the algorithm disclosed in the present invention can better eliminate MTRC, and the degree of scattering point focusing is better. The following Table 3 shows the comparison of imaging indicators of uniformly rotating targets.

[0120] Table 3

[0121]

[0122] When the turntable model rotates with uniform acceleration, the imaging results of each algorithm for point targets are as follows: Figure 5 shown. Figure 7 A schematic diagram schematically shows the imaging result of the RD algorithm of a uniformly accelerated rotating point target according to an embodiment of the present disclosure. Figure 8 The schematic diagram shows the imaging diagram of the space maneuvering target inverse synthetic aperture radar based on the improved BFGS-PFA of a uniformly accelerated rotating point target according to an embodiment of the present disclosure. Figure 7 , Figure 8 As shown in the figure, the MTRC of the imaging result of the RD algorithm is more serious, the rotation acceleration causes more obvious defocusing in the azimuth, and the farther from the rotation center, the more serious the defocusing. It can be clearly seen that the imaging method proposed in the present disclosure has a better focusing degree than the traditional RD algorithm. The following Table 4 shows the comparison of imaging indicators of uniformly accelerated rotating targets.

[0123] Table 4

[0124]

[0125] Based on the above-mentioned 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. Figure 8 The device is described in detail.

[0126] Fig. 9 The structural block diagram of the space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA according to an embodiment of the present disclosure is schematically shown.

[0127] like Fig. 9 As shown, the space maneuvering target inverse synthetic aperture radar imaging device 900 based on improved BFGS-PFA of this embodiment includes a distance unit calculation module 910, a determination module 920, a space-varying phase coarse estimation module 930, a maneuvering factor coarse estimation module 940, a precise estimation module 950, a correction module 960 and an imaging module 970.

[0128] The distance unit calculation module 910 is used to calculate a plurality of distance units based on the preprocessed echo signal, wherein the preprocessing characterizes 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 and is received by the inverse synthetic aperture radar, and the distance unit has a corresponding relationship with the frequency domain in the preprocessed echo signal. In one embodiment, the distance unit calculation module 910 can be used to perform the operation S210 described above, which will not be described in detail herein.

[0129] The determination module 920 is used to determine a target distance unit from a plurality of distance units. In one embodiment, the determination module 920 may be used to perform the operation S220 described above, which will not be described in detail herein.

[0130] The space-variant phase rough estimation module 930 is used to obtain a rough estimation value of the space-variant phase by processing the pre-processed echo signal based on the target range unit using the eigenvector method. In one embodiment, the space-variant phase rough estimation module 930 can be used to perform the operation S230 described above, which will not be repeated here.

[0131] The maneuvering factor rough estimation module 940 is used to obtain a rough estimation value of the maneuvering factor based on the rough estimation value of the echo signal and the space-varying phase after the preprocessing of the target range unit using a preset optimization algorithm. In one embodiment, the maneuvering factor rough estimation module 940 can be used to perform the operation S240 described above, which will not be repeated here.

[0132] The precise estimation module 950 is used to obtain the precise estimation value of the space-varying phase and the precise estimation value of the maneuvering factor by solving the minimum image entropy based on the rough estimation value of the space-varying phase and the rough estimation value of the maneuvering factor by using the quasi-Newton method, and the image entropy model characterizes the inverse synthetic aperture radar imaging effect. In one embodiment, the precise estimation module 950 can be used to perform the operation S250 described above, which will not be repeated here.

[0133] The correction module 960 is used to perform phase correction on the preprocessed echo signal based on the precise estimated value of the space-variant phase and the precise estimated value of the maneuvering factor to obtain a corrected echo signal. In one embodiment, the correction module 960 can be used to perform the operation S260 described above, which will not be repeated here.

[0134] The imaging module 970 is used to obtain an imaging result of the space 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 described in detail here.

[0135] According to an embodiment of the present disclosure, any multiple modules among the distance unit calculation module 910, the determination module 920, the space-variant phase rough estimation module 930, the maneuvering factor rough estimation module 940, the precise estimation module 950, the correction module 960 and the imaging module 970 can be combined into one module for implementation, or any one of the modules 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 distance unit calculation module 910, the determination module 920, the space-variant phase rough estimation module 930, the maneuvering factor rough 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 a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware and firmware or by a proper combination of any of them. Alternatively, at least one of the distance unit calculation module 910, the determination module 920, the space-variant phase rough estimation module 930, the maneuvering factor rough 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 is run, the corresponding function can be performed.

[0136] Fig.10A block diagram of an electronic device suitable for implementing an improved BFGS-PFA-based inverse synthetic aperture radar imaging method for space maneuvering targets according to an embodiment of the present disclosure is schematically shown.

[0137] like Fig.10 As 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 part 1008 to a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard 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.

[0138] In RAM 1003, various programs and data required for the operation of electronic device 1000 are stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0139] According to an embodiment of the present disclosure, the electronic device 1000 may further include an input / output (I / O) interface 1005, which 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 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 drive 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 drive 1010 as needed, so that a computer program read therefrom is installed into the storage portion 1008 as needed.

[0140] 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 independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0141] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

[0142] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA provided by the embodiment of the present disclosure.

[0143] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 1001. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0144] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1009, and / or installed from the removable medium 1011. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0145] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0146] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment 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 process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "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 the remote computing device, or entirely on the 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 (for example, using an Internet service provider to connect through the Internet).

[0147] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0148] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of 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 may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0149] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A space maneuvering target inverse synthetic aperture radar imaging method based on improved BFGS-PFA, characterized in that: The space maneuvering target moves relative to the inverse synthetic aperture radar, and the method comprises: A plurality of distance units are calculated based on the preprocessed echo signal, wherein the preprocessing characterizes 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 and is received by the inverse synthetic aperture radar, and the distance unit has a corresponding relationship with the frequency domain in the preprocessed echo signal; determining a target range cell from the plurality of range cells; Using a eigenvector method, the preprocessed echo signal is processed based on a target distance unit to obtain a rough estimate of the space-varying phase; Using a preset optimization algorithm, the target distance unit processes the preprocessed echo signal and the rough estimate of the space-varying phase to obtain a rough estimate of the maneuvering factor; Using a quasi-Newton method, based on a rough estimate of the space-varying phase and a rough estimate of the maneuvering factor, a precise estimate of the space-varying phase and a precise estimate of the maneuvering factor are obtained by solving a minimized image entropy, wherein the image entropy model characterizes the inverse synthetic aperture radar imaging effect; Performing phase correction on the preprocessed echo signal based on the precise estimated value of the space-variant phase and the precise estimated value of the maneuvering factor to obtain a corrected echo signal; An imaging result of the space maneuvering target is obtained based on the corrected echo signal.

2. The method according to claim 1, characterized in that The method of using the eigenvector method to process the preprocessed echo signal based on the target distance unit to obtain a rough estimate of the space-varying phase includes: Constructing an echo covariance matrix based on echo signal frequency band data, wherein the echo signal frequency band data is obtained from the preprocessed echo signal based on the target distance unit; Obtaining an echo feature vector group based on the echo covariance matrix solution; A rough estimation value of the space-varying phase is obtained based on the echo feature vector group and the echo signal frequency band data.

3. The method according to claim 1, characterized in that The method of obtaining a rough estimate of the maneuvering factor by using a preset optimization algorithm based on the target distance unit processing the preprocessed echo signal and the rough estimate of the space-varying phase includes: Acquire echo signal frequency band data corresponding to the target distance unit; Randomly generate multiple maneuver factor candidate values ​​with different initial values; The rough estimate of the maneuvering factor is obtained by iteratively solving the minimization of image entropy based on the multiple maneuvering factor candidate values, the rough estimate of the space-varying phase and the echo signal frequency band data.

4. The method according to claim 1, characterized in that: The determining of a target distance unit from the plurality of distance units comprises: Calculating normalized amplitude variances for the multiple distance units to obtain normalized amplitude variance groups corresponding to the distance units; The distance unit with the smallest normalized amplitude variance is determined as the target distance unit.

5. The method according to claim 1, characterized in that The method of using the quasi-Newton method to obtain the precise estimated value of the space-varying phase by solving the minimum image entropy based on the rough estimated value of the space-varying phase and the rough estimated value of the maneuvering factor includes: Obtaining a gradient value of the space-varying phase based on a rough estimate value of the space-varying phase; The search direction is obtained by calculating the gradient value of the Hessian matrix and the space-varying phase; Iteratively updating the coarse estimate of the space-varying phase based on the search direction and the step size, wherein the step size is determined by a line search method; When the norm of the gradient value of the space-varying phase does not reach a preset threshold, returning to the step of obtaining the gradient of the space-varying phase based on the rough estimated value of the space-varying phase; When the norm of the gradient value of the space-varying phase reaches a preset threshold, the updated rough estimation value is determined as the precise estimation value of the space-varying phase.

6. The method according to claim 1, characterized in that The pre-processed echo signal is obtained by the following steps: Performing de-skewing compression on the echo signal to obtain a de-skewing compressed echo signal; Using a fast Fourier transform algorithm, the de-skewing and compressed echo signal is processed to obtain a transformed echo signal; Performing residual phase compensation on the transformed echo signal to obtain a compensated echo signal; The compensated echo signal is subjected to envelope alignment processing to obtain a preprocessed echo signal.

7. The method according to claim 1, characterized in that The step of obtaining an imaging result of the space maneuvering target based on the corrected echo signal includes: Performing a range-direction Fourier transform on the corrected echo signal to obtain a Fourier-transformed echo signal; Using a polar coordinate format conversion algorithm, a transformed echo signal is obtained based on the echo signal after Fourier transformation; Performing a range-direction fast Fourier transform on the transformed echo signal to obtain a fast Fourier transformed echo signal; Performing phase gradient self-focusing processing on the echo signal after the fast Fourier transform to obtain a processed echo signal; The processed echo signal is subjected to azimuth Fourier transform to obtain an imaging result of the space maneuvering target.

8. A space maneuvering target inverse synthetic aperture radar imaging device based on improved BFGS-PFA, characterized in that: The device comprises: A distance unit calculation module, used for calculating a plurality of distance units based on a preprocessed echo signal, wherein the preprocessing characterizes 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 varies with time, and the distance unit has a corresponding relationship with the frequency domain in the preprocessed echo signal; A determination module, configured to determine a target distance unit from the plurality of distance units; A space-variant phase rough estimation module, used for obtaining a rough estimation value of the space-variant phase by processing the pre-processed echo signal based on the target distance unit using a eigenvector method; A maneuvering factor rough estimation module, used to obtain a rough estimation value of the maneuvering factor based on the rough estimation value of the preprocessed echo signal and the space-variant phase processed by the target distance unit by using a preset optimization algorithm; A precise estimation module, configured to obtain a precise estimation value of the space-varying phase and a precise estimation value of the maneuvering factor by solving a minimized image entropy based on a rough estimation value of the space-varying phase and a rough estimation value of the maneuvering factor using a quasi-Newton method, wherein the image entropy model represents the inverse synthetic aperture radar imaging effect; A correction module, configured to perform phase correction on the preprocessed echo signal based on the precise estimated value of the space-variant phase and the precise estimated value of the maneuvering factor to obtain a corrected echo signal; An imaging module is used to obtain an imaging result of the space maneuvering target based on the corrected echo signal.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that 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 7.

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

Images