Integrated TBD and ISAR imaging method and device for strong maneuvering target under low signal-to-noise ratio

Abstract

The invention discloses a TBD and ISAR imaging integration method and device for a strong maneuvering target under a low signal-to-noise ratio. The method comprises the steps that an original echo signal is acquired and processed; dividing the processed echo signal to obtain a plurality of groups of sub-aperture signals; track inversion is carried out on each group of sub-aperture signals based on a track-before-detect idea, track fusion and polynomial fitting are carried out on all track inversion results, and an expression of a motion track of the target in the full aperture is obtained; determining a group of translation compensation parameters and the order of target motion based on the expression, and performing coarse compensation on the processed echo signal by adopting the group of translation compensation parameters to obtain a signal after coarse compensation; and taking the order of the target motion as prior information, performing estimation of another group of translation compensation parameters on the signal subjected to coarse compensation by using a PSO algorithm, and performing fine compensation on the signal subjected to coarse compensation by using the estimated group of translation compensation parameters to obtain an ISAR image of the target. According to the invention, the parameter estimation efficiency and accuracy can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of radar imaging technology, and in particular relates to a method and device for integrating TBD and ISAR imaging of a strong maneuvering target under low signal-to-noise ratio. Background Art

[0002] Inverse Synthetic Aperture Radar (ISAR) imaging technology can acquire high-resolution two-dimensional images of high-value targets, making it crucial for tasks such as target identification and situational awareness. In practical applications, low signal-to-noise ratio (SNR) is a common and unavoidable problem. Under low SNR conditions, target information can be drowned out by noise in the echo signals received by the radar system, compromising target detection and imaging quality. This poses challenges for traditional cascaded translational compensation. To address the error propagation issues inherent in the cascade relationship of traditional non-parametric translational compensation methods, joint translational compensation methods have emerged. In this joint translational compensation scheme, envelope alignment and phase correction steps are no longer performed separately. The algorithm's primary goal is to estimate the range change caused by target translation. The envelope and phase errors caused by translation are then simultaneously compensated. The target's translational motion is typically modeled as a polynomial. By estimating its various-order motion parameters, the target's range change is calculated. This problem can be formulated as an optimization problem.

[0003] The main current research directions for finding optimal parameters include gradient descent-based methods and swarm intelligence optimization algorithms, such as particle swarm optimization and the Blue Whale optimization algorithm. While gradient descent methods use the principle of linear convergence to find the optimal solution, their drawback is insufficient fitting accuracy, making it impossible to accurately fit the target's motion trajectory, making it difficult to achieve high-resolution ISAR imaging. Due to factors such as lock avoidance, targets may perform more complex, higher-order motions, resulting in trajectories exhibiting high-order motion characteristics. The motion parameters of highly maneuverable targets are highly dimensional, and the coupling between parameters is strong. Furthermore, due to the lack of prior information, traditional swarm intelligence search algorithms require a sufficiently large search space to ensure that the target point is within this range. Furthermore, the specific order of the target's highest-order motion is unknown, and determining this order requires repeated searches, which incurs a significant computational burden. Traditional swarm intelligence search algorithms suffer from insufficient parameter estimation accuracy and low efficiency.

[0004] In other words, for strongly maneuvering targets under low signal-to-noise ratio conditions, the parameter estimation accuracy of the traditional ISAR imaging parametric translational motion compensation method is insufficient and the efficiency is low. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method and device for integrating TBD and ISAR imaging of strong maneuvering targets under low signal-to-noise ratio.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] The present invention provides a method for integrating TBD and ISAR imaging of a strong maneuvering target under low signal-to-noise ratio, comprising:

[0008] Obtaining original echo signal;

[0009] Processing the original echo signal to obtain a processed echo signal;

[0010] performing sub-aperture division on the processed echo signal to obtain multiple groups of sub-aperture signals;

[0011] Based on the tracking-before-detection concept, trajectory inversion is performed using each set of sub-aperture signals. Trajectory fusion and polynomial fitting are performed on the trajectory inversion results of the multiple sets of sub-aperture signals to obtain the expression of the target's motion trajectory in the full aperture.

[0012] Determining a set of translation compensation parameters and a motion order of the target based on an expression of a motion trajectory of the target in the full aperture, and performing joint translation coarse compensation on the processed echo signal using the set of translation compensation parameters to obtain a coarsely compensated signal;

[0013] The motion order is used as prior information, and a PSO algorithm is used to estimate another set of translation compensation parameters for the coarsely compensated signal. The estimated another set of translation compensation parameters is then used to perform joint translation fine compensation on the coarsely compensated signal to obtain an ISAR image of the target.

[0014] The present invention further provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0015] The memory is used to store computer programs;

[0016] The processor is used to implement the steps of the above-mentioned method for integrating TBD and ISAR imaging of strong maneuvering targets under low signal-to-noise ratio when executing the program stored in the memory.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention proposes an integrated method for TBD and ISAR imaging of strong maneuvering targets under low signal-to-noise ratio. This method divides the whole into parts, converts high orders into low orders, and uses sub-aperture division to reduce the dimension and coupling degree of the parameters to be estimated within the sub-aperture. Based on the idea of Track Before Detect (TBD), the target motion order determination and rough compensation are achieved through trajectory inversion within the sub-aperture and overall fusion and polynomial fitting. While obtaining the initial values of the motion parameters, the computational complexity is greatly reduced, and the parameter estimation efficiency and accuracy are improved. Afterwards, the order determination and rough estimation results are used as prior information, and the translation compensation parameters are further estimated based on the particle swarm optimization (PSO) algorithm according to the prior information, and the further estimated translation compensation parameters are used for fine compensation, thereby robustly achieving high-resolution ISAR imaging of strong maneuvering targets under low signal-to-noise ratio, further improving the accuracy of parameter estimation.

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is the actual scene diagram of ISAR imaging of highly maneuverable targets;

[0021] Figure 2 It is a geometric model diagram of the motion decomposition of a high-maneuverability target;

[0022] Figure 3 This is a flow chart of a method for integrating TBD and ISAR imaging of a strong maneuvering target under low signal-to-noise ratio provided by an embodiment of the present invention;

[0023] Figure 4 This is another flow chart of a method for integrating TBD and ISAR imaging of a strong maneuvering target under low signal-to-noise ratio provided by an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of sub-aperture fusion provided by an embodiment of the present invention;

[0025] Figure 6 is a schematic diagram of the results of polynomial fitting provided by an embodiment of the present invention;

[0026] Figure 7 Schematic diagram of a scattering point model of an aircraft target provided by an embodiment of the present invention;

[0027] Figure 8 are envelope alignment results of different methods provided by the embodiments of the present invention;

[0028] Figure 9 These are the imaging results of different methods provided by the embodiments of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0030] Figure 1 The three-dimensional geometric image of radar detection of strong maneuvering targets is shown; Figure 2 is the decomposition of the target motion. Figure 1 、 2 As shown, the target speed ω(t m ) can be decomposed into two components along the Y axis and the Z axis, respectively, ω r (t m ) and ω e (t m ). In ISAR imaging, the rotation component ω r (t m ) will not cause the slant range between the target and the radar to change, and has no contribution to imaging, so it can be ignored. e (t m ) is the actual useful rotation component. Let the initial coordinates of a point p on the target in the plane be (x p ,y p ), then the point is rotated by the effective angle θ(t m ) caused by the instantaneous slope distance ΔR p (t m ) is: ΔR p (t m )=y p cosθ(t m )-x p sinθ(t m ). Slant range R between scattering point p and radar p (t m ) can be expressed as: Among them, the slant distance between the target rotation center and the radar is R0+r(t m ) is the same for all scattering points, R0 represents the initial distance, r(t m ) represents the slant range change caused by the target translation component. For a strong maneuvering target, r(t m ) changes are more complex and depend on the various order motion parameters of the target. p (t m ) is the instantaneous slant range change caused by the target rotation, which reflects the difference between different scattering points. The object of this invention is mainly the translational motion of the target, so it is assumed that the target rotation angle is very small and the posture change is not large, so the target rotation speed is approximately considered to be constant, that is, ω e (t m )=ω eAccording to the first-order Taylor expansion formula of trigonometric functions, sinθ(t m )≈θ(t m )≈ω e ·t m , cosθ(t m )≈1, then R p (t m ) can be simplified to: R p (t m )=R0+r(t m )+y p -x p ω e t m In order to take into account both the effective range and the range resolution of the radar, a linear frequency modulation signal (LFM) is generally used as the transmission signal. Assume that the expression of the LFM signal transmitted by the radar is: in, Indicates fast time, Indicates full time, T p Indicates the pulse width, f c Indicates the carrier frequency, represents the modulation frequency of the LFM signal, and B is the signal bandwidth. The echo signal obtained by backscattering the transmitted signal through point p on the target is expressed as:

[0031] , where c is the propagation speed of electromagnetic waves, σ P is the backscatter coefficient of point p. Assuming that the target can be equivalent to P scattering points, the spectrum of the echo signal received by the radar is: Among them, S t (f r ) is the spectrum of the transmitted LFM signal, f r Represents the range frequency, f c Represents the carrier frequency. The expression of the echo spectrum after matched filtering is:

[0032] , where A(f r ) represents the spectrum energy of the transmitted signal, and the slow time t m It doesn't matter, is the phase error term caused by the target translation component, which needs to be estimated and compensated. p (f r ,t m ) is discretized and expressed as: Among them, n and m represent the distance unit and slow time sequence number respectively. N is the total number of distance units, M is the total number of pulses, Δf r and Δtm are the range frequency interval and the slow time sampling interval respectively. In the above formula, as long as the slant distance change r(mΔt m ) is compensated, the joint translation compensation of ISAR imaging can be completed to obtain a well-focused image.

[0033] Therefore, the joint translation compensation problem in the present invention can be transformed into the problem of the motion offset r(mΔt m ) estimation problem. In the imaging scenario of a strongly maneuvering target, the target translation usually has high-order terms. Without loss of generality, the target translation motion can be modeled as an L-order polynomial: The polynomial coefficient α represents the parameters of each order of motion of the target. For high-order motion, the parameters of each order of motion of the target can be used to calculate the slant distance r(mΔt m ) is reconstructed, therefore, the estimation of the target translational motion change of the present invention is converted into the estimation of the parameter vector α and the order L.

[0034] Based on the above analysis, this paper proposes a method for integrating TBD and ISAR imaging of highly maneuvering targets at low signal-to-noise ratios. This method focuses on the ISAR imaging of highly maneuvering targets at low signal-to-noise ratios. Using subaperture partitioning and the TBD concept, the paper achieves preliminary estimation of motion parameters. Combined with the PSO algorithm, the method implements joint translation compensation, ultimately achieving high-resolution ISAR imaging of highly maneuvering targets at low signal-to-noise ratios. Figure 3 This is a flow chart of a method for integrating TBD and ISAR imaging of a strong maneuvering target under low signal-to-noise ratio provided by an embodiment of the present invention; Figure 4 FIG. 1 is another flow chart of a method for integrating TBD and ISAR imaging of a strong maneuvering target under low signal-to-noise ratio provided by an embodiment of the present invention. Figure 3 、 4 As shown, the method includes:

[0035] S101: Acquire original echo signal.

[0036] Here, the original echo signal may be acquired in real time or in advance, and the present invention does not limit this.

[0037] S102: Process the original echo signal to obtain a processed echo signal.

[0038] For example, Figure 4 As shown, the original echo signal can be demodulated, range compressed, etc., and the processed echo signal can be obtained through these processes.

[0039] S103 , performing sub-aperture division on the processed echo signal to obtain multiple groups of sub-aperture signals.

[0040] Here, the number of sub-apertures to be divided can be set according to actual needs, and the present invention does not limit this.

[0041] S104. Based on the TBD concept, trajectory inversion is performed using each group of sub-aperture signals, trajectory inversion results of multiple groups of sub-aperture signals are fused and polynomial fitting is performed to obtain an expression of the target's motion trajectory in the full aperture.

[0042] S105. Based on the expression of the target's motion trajectory in the full aperture, determine the target's motion order and a set of translation compensation parameters, and use the set of translation compensation parameters to perform joint translation coarse compensation on the processed echo signal to obtain a coarsely compensated signal.

[0043] S106. Using the target motion order as prior information, the PSO algorithm is used to estimate another set of translation compensation parameters for the coarsely compensated signal, and the estimated another set of translation compensation parameters are used to perform joint translation fine compensation on the coarsely compensated signal to obtain an ISAR image of the target.

[0044] In the present invention, the above S104 can be implemented by the following steps:

[0045] S1041. Based on the TBD concept, trajectory inversion is performed using each group of sub-aperture signals to obtain an expression for a target trajectory corresponding to each group of sub-aperture signals.

[0046] Here, the full aperture is divided into several sub-apertures. In each sub-aperture, due to the short time, the target's motion can be approximately considered as linear motion. With the help of the TBD concept, the change process of the target's slant range R can be determined in each sub-aperture. TBD is a detection and tracking technology mainly for weak radar targets. The basic idea is to no longer detect single-frame data, but to jointly process multiple frames of data, suppress clutter while accumulating target energy, and complete target detection while tracking. When performing constant false alarm rate (CFAR) detection on the data in each sub-aperture, unlike traditional pre-tracking detection methods, the detection here does not require completely accurate detection of the target. Even if some false alarms or missed alarms occur, the impact of false detection can be minimized in the joint processing of multiple frames of data. Taking advantage of the continuity of the target's changes in time and space, the Hough transform can be used to find a trajectory that conforms to the target motion model in the multi-frame data, realizing trajectory inversion. The sub-steps of the Hough transform are as follows: Sub-step 1: Initialize the parameters required for the Hough transform, read the detected point coordinates (x i' ,y i' ) and the corresponding energy value e i' ; Sub-step 2: For each coordinate (x i' ,yi' ), calculate the ρ value under different θ values, where ρ = x i' cosθ+y i' Sinθ, θ ranges from [-90°, 90°], and maps the Cartesian coordinates to the polar coordinate system (ρ, θ) of the parameter space. Sub-step 3: When multiple points fall on the same straight line, an intersection point will be generated in the Hough space, and the energy value of the point e is used. i' To perform weighted voting, the values of the corresponding positions in the Hough transform matrix are accumulated, and finally the peak point (ρ m ,θ m ) is the desired target trajectory.

[0047] Based on the above analysis, the above S1041 is specifically implemented as follows: for each group of sub-aperture signals, CFAR detection is performed on the group of sub-aperture signals to obtain multiple point coordinates (x i ,y i ) and the energy value e of each point coordinate i' , i' ranges from 1 to 0, where 0 represents the total number of point coordinates obtained after CFAR detection of the sub-aperture signal. i' ,y i' ) is mapped from the Cartesian coordinate system to the polar coordinate system to obtain corresponding polar coordinate points, where each polar coordinate point corresponds to one or more point coordinates, and a polar coordinate point represents a straight line in the Cartesian coordinate system. i' ,y i' ) The specific principle of mapping from the Cartesian coordinate system to the polar coordinate system is: for the point coordinate (x i' ,y i' ), calculate the ρ value under different θ values, the value range of θ is [-90°, 90°], so the Cartesian coordinate (x i' ,y i' ) is mapped to the polar coordinate system (ρ,θ) of the parameter space, and the coordinates of the point (x i' ,y i' ) corresponding to multiple polar coordinate points. Next, for each polar coordinate point, the energy values of all the coordinates of the points corresponding to the polar coordinate point are added together to obtain the total energy value of the polar coordinate point; and the expression of a straight line represented by the polar coordinate point with the largest total energy value is used as the expression of a target trajectory corresponding to the group of sub-aperture signals.

[0048] S1042. Determine a set of trajectory coordinate points based on the expressions of the target trajectory corresponding to the multiple groups of sub-aperture signals, perform polynomial fitting based on the set of trajectory coordinate points and a preset highest-order term Q, and obtain an expression for the target's motion trajectory in the full aperture.

[0049] Here, after performing trajectory inversion on each group of sub-aperture signals, the trajectory inversion results of each sub-aperture are fused to obtain the motion trajectory of the target in the full aperture; for example, Figure 5 It is a schematic diagram of the process of obtaining the target's motion trajectory in the full aperture by fusing the trajectory inversion results of each sub-aperture, as shown in Figure 5 As shown in the figure, when the full aperture is divided into 10 sub-apertures, 10 groups of sub-aperture signals can be obtained. By performing trajectory inversion on these 10 groups of sub-aperture signals, the expressions of 10 target trajectories are obtained. Figure 5 The three middle figures in the figure are schematic diagrams of the trajectory inversion results of three sub-apertures. By splicing the target trajectories in all sub-apertures, the target's motion trajectory in the full aperture can be obtained. Since the target's motion is modeled as a high-order polynomial, a preliminary estimate of the order and coefficients of the high-order polynomial can be obtained by fitting the slant range variation curve. That is, after obtaining the target's motion trajectory in the full aperture, the expression of the motion trajectory can be determined by fitting the polynomial to the motion trajectory. For example, Figure 6 This is a schematic diagram of the polynomial fitting results. Figure 6 As can be seen from the figure, with the increase of the highest order term ( Figure 6 The highest order terms in (b) to (f) are 2, 3, 4, 5, and 6, respectively, and the degree of fit increases. When the highest order terms exceed 5, the degree of fit is very close. Here, the concept of goodness of fit is used to evaluate the degree of fit of the fitted line to the observed values. The formula for calculating goodness of fit is: Among them, y j' is the y coordinate of the j'th sampling point in the target's motion trajectory in the full aperture, is the average value of the y coordinates of all sampling points in the target's motion trajectory in the full aperture, is the y coordinate of the jth sampling point obtained according to the fitted polynomial. 2 The value of is between 0 and 1. The closer its value is to 1, the better the fitting curve (that is, the curve represented by the fitted polynomial) fits the sampling points. It can be seen that when the highest number of polynomial fitting reaches 5 times, the increase in the goodness of fit value is very small, and the result of retaining 5 significant digits has remained unchanged. At this time, it can be considered that the highest order of motion parameters that need to be estimated is the 5th order. Therefore, when the highest order increases and the growth rate of the goodness of fit value slows down significantly, it is considered that the point where the growth slows down is the highest order, thereby achieving the purpose of determining the order, that is, L=5 mentioned above. While determining the order L, the coefficients of each order term in the polynomial fitting result are the target motion parameters of each order that need to be estimated, α1~α L , from which the slant range r(mΔt m ) and compensate the original signal in the range Doppler domain.

[0050] Based on the above analysis, the above-mentioned S1042 is specifically implemented as follows: W trajectory coordinate points are collected at equal intervals from the trajectory represented by the expression of the target trajectory corresponding to each group of sub-aperture signals, and the coordinate axis of each collected trajectory coordinate point is converted, and all collected trajectory coordinate points are regarded as a group of trajectory coordinate points. It should be noted that the y-coordinate of each trajectory coordinate point collected from the trajectory represented by the expression of each target trajectory is in distance units, and the x-coordinate is the pulse number. Therefore, it is necessary to convert the y-coordinate of each trajectory coordinate point from distance units to distance, and convert the x-coordinate from pulse number to time, so as to obtain a group of trajectory coordinate points with the x-coordinate being time and the y-coordinate being distance. After obtaining a set of trajectory coordinate points, Q=6-order polynomial fitting is performed based on this set of trajectory coordinate points, and the goodness of fit is calculated after each fitting, and the difference between the goodness of fit this time and the goodness of fit last time is calculated, wherein the highest order of the polynomial of the q-th order polynomial fitting is q, that is, the highest order of the target motion is q, and the value of q is 1~Q; the difference between the goodness of fit this time and the goodness of fit last time is taken as the difference this time, and then the quotient between the difference this time and the difference last time is taken as the quotient this time. When the quotient value this time and the quotient value last time are both within the preset range, it is considered that the corresponding q-order polynomial is the expression of the motion trajectory of the target in the full aperture; otherwise, the next polynomial fitting is continued until the expression of the motion trajectory of the target in the full aperture is obtained.

[0051] In some embodiments, when the present invention performs sub-aperture division on the processed echo signal, U pulse signals are repeated in two adjacent groups of sub-aperture signals obtained by the division. Based on this, when the present invention collects W trajectory coordinate points at equal intervals from the trajectory represented by the expression of the target trajectory corresponding to each group of sub-aperture signals, the collection interval of the trajectory coordinate points is also U, where U and W are both positive integers greater than 1. This setting can make the trajectory coordinate points collected from the trajectory represented by the expression of adjacent target trajectories continuous, thereby making the trajectory obtained based on these trajectory coordinate points more accurate, which is conducive to improving the accuracy of subsequent polynomial fitting. It should be noted that the value of U can be set according to actual needs, for example, it can be 10, 20, etc., and the present invention is not limited to this.

[0052] In the present invention, the above S105 can be implemented by the following steps:

[0053] S1051. The coefficients of the various orders in the expression of the target's motion trajectory in the full aperture are used as a set of translation compensation parameters, and the highest order of the polynomial in the expression of the target's motion trajectory in the full aperture is used as the motion order.

[0054] Specifically, the coefficients of each order term in the expression of the target's motion trajectory in the full aperture are used as a set of translation compensation parameters, thus obtaining a set of translation compensation parameters α1~α L , taking L as the motion order.

[0055] S1052: Reconstruct the slant range change caused by the translational motion of the target based on the set of translational compensation parameters.

[0056] Specifically, α1~α L Substitute into the formula The slant distance change r(mΔt m ).

[0057] S1053 , performing joint translation coarse compensation on the processed echo signal according to the reconstructed slant range variation to obtain a coarsely compensated signal.

[0058] Specifically, using the calculated r(mΔt m ) Performing joint translation coarse compensation on the processed echo signal can obtain a coarse compensated signal. p The expression for coarse compensation of (n,m) is:

[0059] Since most of the translational motion errors have been successfully compensated after the rough compensation, the envelope is roughly aligned, so, as mentioned above Figure 4 As shown, the echo signal obtained by coarse compensation can be intercepted in the range support area, thereby reducing the number of range units and improving the efficiency of subsequent operations. Based on this, in some embodiments, the above S1053 can also be implemented as follows: performing joint translation coarse compensation on the processed echo signal based on the reconstructed slant range change to obtain a compensated signal, intercepting the compensated signal in the range support area, and using the intercepted signal as the coarse compensated signal.

[0060] In the present invention, the above S106 can be implemented by the following steps:

[0061] S1061. Generate multiple sets of initial translation compensation parameters, where the number of parameters in each set of initial translation compensation parameters is the same as the number of parameters in the set of translation compensation parameters obtained above. Here, the number of parameters in each set of translation compensation parameters is the target motion order, which is also the problem dimension in the PSO algorithm.

[0062] S1062. Using multiple groups of initial translation compensation parameters as an initial population, using each group of initial translation compensation parameters as an individual in the initial population, and using the PSO algorithm to estimate another group of translation compensation parameters for the roughly compensated signal based on the initial population to obtain another group of estimated translation compensation parameters.

[0063] The idea of particle swarm optimization (PSO) originated from the study of bird flocking foraging behavior. Bird flocks share information collectively to find the optimal destination. The steps of the PSO algorithm are as follows:

[0064] Sub-step 1: Generate an initial population, determine the population size P', problem dimension D, and maximum number of iterations K.

[0065] Sub-step 2: Calculate the fitness value of each individual in the population. Initially, the birds do not know the exact location of the food, so they each search in a certain direction. At each location, they observe the amount of food in that area to evaluate the quality of the area, which is the fitness value.

[0066] In the present invention, the fitness function is set as the two-dimensional image entropy value of the ISAR image, and the position of the individual represents another set of translation compensation parameters β that need to be estimated, β=(β1,β2,β3…β L ), compensate the coarse compensation signal and calculate the entropy value of the compensated image. The calculation formula of the fitness function is as follows:

[0067]

[0068] Where, Es represents the total image energy, M is the number of pulse echoes, and N is the number of range units. is the estimated value of β, After the rough compensation, some motion errors are still not compensated successfully. Reconstruct the remaining motion error (i.e. calculate r(mΔt m )), after performing secondary compensation on the motion errors that cannot be fully compensated by the coarse compensation, a well-focused ISAR image can be obtained. It represents the complex value of the (q,k)th position of the ISAR image after compensation. The better the image focus, the smaller the entropy value.

[0069] Sub-step 3: Update each individual's historical best fitness value and position. After evaluating the quality of each area, each individual should record the location with the best fitness value and this best value.

[0070] Sub-step 4: Update the group's historical optimal fitness value and position. Searching is a collective process, and each individual must share the area with the best fitness value they have found with the group to help the group better complete the search task.

[0071] Sub-step 5: Update the speed and position of each particle. After collective information exchange, each individual will adjust its next search direction based on its own best position and the current collective best position. In this way, after a period of searching, the group can find the location in the forest with the most food. The speed update formula is:

[0072] The position update formula is: in, represents the velocity of the d-th dimension of the i-th particle in the k-th iteration, represents the position of the ith particle in the dth dimension at the kth iteration. r1 and r2 are random numbers in the range [0, 1] used to increase the randomness of the search. c1 and c2 are learning factors. are the historical optimal positions of the individual and the group, respectively. ω is the inertia weight, which enables the algorithm to have a stronger global convergence ability in the early stage and pay more attention to the local convergence ability in the later stage.

[0073] Sub-step 6: Return to sub-step 2 and repeat the search process until the maximum number of iterations is reached, end the search, and return to the optimal individual position.

[0074] Thus, another set of translation compensation parameters β=(β1,β2,β3…β L ).

[0075] S1063. Reconstruct the slant range variation of the target caused by the translation motion that was not fully compensated in the coarse compensation using another set of estimated translation compensation parameters.

[0076] Specifically, (β1,β2,β3…β L ) into the formula The target's uncompensated slant distance change r(mΔt m ).

[0077] S1064 , performing joint translation fine compensation on the coarsely compensated signal according to the reconstructed slant range variation, to obtain an ISAR image of the target.

[0078] According to the newly calculated r(mΔt m ) Continue to compensate the coarsely compensated signal, so that a well-focused high-resolution ISAR image of the target can be obtained.

[0079] Compared with the existing joint translation compensation imaging algorithm, the present invention has great advantages in robustness and efficiency.

[0080] First, this invention leverages the concept of TBD to jointly process multiple frames of data within a subaperture, suppressing clutter while accumulating target energy. By leveraging the continuity of target changes in time and space, the Hough transform is used to find a trajectory within the multi-frame data that matches the target's motion model. This method can accurately invert trajectory even at low signal-to-noise ratios and exhibits good robustness.

[0081] Second, the present invention uses sub-aperture division to convert high-order parameters into low-order processing, reducing the coupling between parameters. By performing polynomial fitting on the sub-aperture inversion trajectory fusion results to obtain information such as the target's motion parameters and order, most of the motion errors can be compensated, and then the target support area can be intercepted, saving a lot of time for the subsequent parameter search process. The parameter boundaries can also be appropriately narrowed, improving the accuracy of the search. At the same time, the traditional swarm intelligence optimization algorithm needs to be repeated many times to determine the order of the target motion. The present invention obtains it through polynomial fitting, and the computational efficiency is greatly improved.

[0082] The present invention also provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory is used to store computer programs; and the processor is used to implement the steps of the above-mentioned method for integrating TBD and ISAR imaging of highly maneuvering targets under low signal-to-noise ratio when executing the program stored in the memory.

[0083] The effects of the present invention are verified by experiments below.

[0084] 1. Experimental conditions:

[0085] The simulation target is set as an aircraft model consisting of 259 scattering points, such as Figure 7 As shown in Figure 2, it is assumed that the scattering coefficient of each scattering point in the model is 1. The main simulation parameters are shown in Table 1. The experimental operation system is Intel(R) Core(TM) i5-1035G1CPU@1.00GHz, 64-bit Windows 10 operating system, and the simulation software is MATLAB, version R2018b.

[0086] Table 1 Main simulation parameters of the system

[0087] parameter meaning value <![CDATA[f c ]]> carrier frequency 10GHz B bandwidth 200MHz M Number of sampling points 800 <![CDATA[T p ]]> Pulse Width 5μs PRF Pulse repetition frequency 100Hz N Accumulated pulse number 500 ω Equivalent speed 0.015rad / s

[0088] 2. Experimental content and results analysis:

[0089] This paper focuses on solving the ISAR imaging problem of strong maneuvering targets under low signal-to-noise ratio. To this end, we conducted the following experiments. The aircraft target was set to have a 5th-order motion parameter, v = 20m / s, a = -18m / s 2 、j=-12m / s3 s=12m / s 4 q=18m / s 5 Gaussian white noise is added to the echo data. Under the conditions of signal-to-noise ratio (SNR) of 10dB, 5dB, 0dB, and -5dB, three methods are compared with the method proposed in the present invention. Comparison method 1: Directly use the PSO algorithm to perform joint translation compensation for the entire system; Comparison method 2: Use one-dimensional entropy in the sub-aperture to search for the target velocity and perform compensation according to the method proposed in the present invention; Comparison method 3: Use two-dimensional image entropy in the sub-aperture to search for the target velocity and perform compensation according to the method proposed in the present invention.

[0090] Figure 8 and Figure 9 The envelope alignment results and final imaging results for each method are presented. Comparing these results reveals that the proposed method is less susceptible to noise, exhibits strong robustness, and demonstrates superior performance across various signal-to-noise ratios. Furthermore, it demonstrates significant efficiency advantages, significantly reducing the time required compared to traditional joint translation compensation methods.

[0091] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0092] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0093] In the specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. Certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0094] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for integrating TBD and ISAR imaging of strong maneuvering targets under low signal-to-noise ratio, characterized in that: include: Obtaining original echo signal; Processing the original echo signal to obtain a processed echo signal; performing sub-aperture division on the processed echo signal to obtain multiple groups of sub-aperture signals; Based on the TBD concept, trajectory inversion is performed using each group of sub-aperture signals, trajectory fusion is performed on the trajectory inversion results of the multiple groups of sub-aperture signals, and polynomial fitting is performed to obtain the expression of the target's motion trajectory in the full aperture; Determining a set of translation compensation parameters and a motion order of the target based on an expression of a motion trajectory of the target in the full aperture, and performing joint translation coarse compensation on the processed echo signal using the set of translation compensation parameters to obtain a coarsely compensated signal; The motion order is used as prior information, and a PSO algorithm is used to estimate another set of translation compensation parameters for the coarsely compensated signal. The estimated another set of translation compensation parameters is then used to perform joint translation fine compensation on the coarsely compensated signal to obtain an ISAR image of the target.

2. The method according to claim 1, characterized in that The TBD-based method utilizes each group of sub-aperture signals to perform trajectory inversion, performs trajectory fusion on the trajectory inversion results of the multiple groups of sub-aperture signals, and performs polynomial fitting to obtain an expression of the target's motion trajectory in the full aperture, including: Based on the TBD idea, trajectory inversion is performed using each group of sub-aperture signals to obtain an expression of a target trajectory corresponding to each group of sub-aperture signals; According to the expression of the target trajectory corresponding to the multiple groups of sub-aperture signals, a group of trajectory coordinate points are determined, and a polynomial fitting is performed based on the group of trajectory coordinate points and the preset highest-order term Q to obtain the expression of the target's motion trajectory in the full aperture.

3. The method according to claim 2, characterized in that The TBD-based method uses each group of sub-aperture signals to perform trajectory inversion, and obtains an expression of a target trajectory corresponding to each group of sub-aperture signals, including: For each group of sub-aperture signals, CFAR detection is performed on the group of sub-aperture signals to obtain multiple point coordinates and the energy value of each point coordinate; Mapping each point coordinate from the Cartesian coordinate system to the polar coordinate system to obtain a corresponding plurality of polar coordinate points; wherein each polar coordinate point corresponds to one or more of the point coordinates, and a polar coordinate point represents a straight line in the Cartesian coordinate system; For each polar coordinate point, add up the energy values of all the coordinates of the points corresponding to the polar coordinate point as the total energy value of the polar coordinate point; An expression of a straight line represented by a polar coordinate point with the maximum energy value sum is used as an expression of a target trajectory corresponding to the group of sub-aperture signals.

4. The method according to claim 2, characterized in that The polynomial fitting is performed based on the set of trajectory coordinate points and the preset highest order term Q to obtain an expression for the motion trajectory of the target in the full aperture, including: Perform Q-order polynomial fitting on the set of trajectory coordinate points, calculate the goodness of fit after each fitting, and calculate the difference between the goodness of fit and the goodness of fit of the previous fitting, where the order of the polynomial obtained by the q-th polynomial fitting is q, and the value of q ranges from 1 to Q; The difference between the goodness of fit this time and the goodness of fit last time is taken as the difference value this time, and the quotient between the difference value this time and the difference value last time is taken as the quotient value this time; When the current quotient and the previous quotient are both within the preset range, the polynomial fitted this time is used as the expression of the target's motion trajectory in the full aperture; When the current quotient and the previous quotient are not both within the preset range, the next polynomial fitting is continued until the expression of the motion trajectory of the target in the full aperture is obtained.

5. The method according to claim 1, characterized in that: The method further comprises determining a set of translation compensation parameters and a motion order of the target based on an expression of a motion trajectory of the target in the full aperture, and performing a joint translation coarse compensation on the processed echo signal using the set of translation compensation parameters to obtain a coarsely compensated signal, including: The coefficients of the respective order terms in the expression of the target's motion trajectory in the full aperture are used as the set of translation compensation parameters, and the highest order of the polynomial in the expression of the target's motion trajectory in the full aperture is used as the motion order; reconstructing a slant range change of the target caused by the translational motion according to the set of translational compensation parameters; The processed echo signal is subjected to joint translation coarse compensation according to the reconstructed slant range variation to obtain a coarsely compensated signal.

6. The method according to claim 5, characterized in that The step of performing joint translation coarse compensation on the processed echo signal according to the reconstructed slant range variation to obtain a coarsely compensated signal includes: performing joint translation coarse compensation on the processed echo signal according to the reconstructed slant range variation to obtain a compensated signal; The compensated signal is intercepted at a distance support area, and the obtained intercepted signal is used as the roughly compensated signal.

7. The method according to claim 1, characterized in that: The method of using the motion order as prior information, estimating another set of translation compensation parameters for the coarsely compensated signal using a PSO algorithm, and performing joint translation fine compensation on the coarsely compensated signal using the estimated another set of translation compensation parameters to obtain an ISAR image of the target includes: generating a plurality of sets of initial translation compensation parameters, wherein the number of parameters in each set of initial translation compensation parameters is the motion order; Using the multiple sets of initial translation compensation parameters as an initial population, using each set of initial translation compensation parameters as an individual in the initial population, and estimating another set of translation compensation parameters on the roughly compensated signal using a PSO algorithm based on the initial population to obtain another set of estimated translation compensation parameters; Reconstructing the slant range variation of the target caused by the translation motion that was not fully compensated in the coarse compensation using another set of estimated translation compensation parameters; The coarsely compensated signal is subjected to joint translation fine compensation according to the reconstructed slant range variation to obtain an ISAR image of the target.

8. The method according to claim 1, characterized in that: The fitness function in the PSO algorithm is the two-dimensional image entropy value of the ISAR image, and the position of the individual represents another set of translation compensation parameters that need to be estimated.

9. The method according to claim 2, characterized in that: U pulse signals are repeated in two adjacent groups of sub-aperture signals; and determining a group of trajectory coordinate points according to an expression of target trajectories corresponding to the multiple groups of sub-aperture signals includes: With U as the sampling interval, W trajectory coordinate points are collected at equal intervals from the trajectory represented by the expression of the target trajectory corresponding to each group of sub-aperture signals, and the coordinate axis of each collected trajectory coordinate point is transformed; U and W are both positive integers greater than 1; All the collected trajectory coordinate points are regarded as a set of trajectory coordinate points.

10. An electronic device comprising a processor, a communication interface, a memory and a communication bus, characterized in that: The processor, the communication interface and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is configured to implement the method steps described in any one of claims 1 to 9 when executing a program stored in the memory.

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