Method, device, equipment and storage medium for estimating initial motion state of target

Abstract

The present invention is applicable to the field of radar technology, and provides a method, device, equipment and storage medium for estimating the initial motion state of a target. The method for estimating the initial motion state of a target includes: obtaining the current motion state measurement data of the target in rectangular coordinates and the motion model of the target, the motion model including a uniform linear motion model, a uniformly accelerated linear motion model and a uniformly decelerated linear motion model; determining the measurement model of the initial position parameters of the target in a polar coordinate system according to the current motion state measurement data and the motion model; and calculating the first estimated value of the initial position parameters of the target by polynomial fitting according to the measurement model. This scheme can determine the measurement model of the initial position parameters of the target by the measurement data of the current motion state of the target and the motion model of the target, and then can calculate an estimated value of the initial position parameters of the target by a polynomial fitting algorithm, thereby realizing the application of low-cost radar without angle measurement capability in the estimation of the target motion state.

Description

Technical Field

[0001] The present invention belongs to the field of radar technology, and in particular relates to a method, device, equipment and storage medium for estimating the initial motion state of a target. Background Art

[0002] At present, radar technology is booming, and various radar applications are emerging. In addition to the military field, radar is now widely used in the civilian field. Different applications will put forward different tasks and functional requirements for radar. In some low-cost applications, the application itself does not pay attention to the specific location of the target, but pays more attention to the target's motion state, which is convenient for predicting the target's future motion trend and making corresponding decisions in advance. Based on cost, size, power consumption and other considerations, such applications usually do not have the ability to measure angles, and only provide target distance and speed measurements. The target's motion state estimation and motion trend prediction will be obtained from these measurement data.

[0003] In the process of radar detecting targets, the target state is generally established in a rectangular coordinate system, while the nonlinear measurement is obtained in a polar / spherical coordinate system. The incompatibility between the state and the measurement leads to the so-called nonlinear estimation problem. For this nonlinear estimation problem, Bayesian filters are often used for parameter estimation, including extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), particle filter (PF), conversion measurement Kalman filter (CMKF), etc. These methods have high accuracy, good real-time performance and are easy to implement, and are widely used in practice.

[0004] However, for radars without angle measurement capability, the lack of key angle information makes it impossible to perform state initialization well when using the above existing methods, and the Bayesian filter-based method is greatly affected by the initial value. It can be seen that for low-cost radars without angle measurement capability, the existing target motion state estimation method has poor estimation effect on the initial motion state of the target. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a method for estimating the initial motion state of a target, aiming to solve the technical problem that the existing target motion state estimation method has poor estimation effect on the initial motion state of the target for a low-cost radar without angle measurement capability.

[0006] The embodiment of the present invention is implemented in such a way that the target initial motion state estimation method comprises:

[0007] Acquire current motion state measurement data of the target in rectangular coordinates and a motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model, and a uniformly decelerated linear motion model;

[0008] Determine a measurement model of initial position parameters of the target in a polar coordinate system according to the current motion state measurement data and the motion model;

[0009] A first estimated value of the target initial position parameter is calculated according to the measurement model by polynomial fitting.

[0010] Another object of an embodiment of the present invention is to provide a device for estimating an initial motion state of a target, the device comprising:

[0011] A data acquisition module, used to acquire the current motion state measurement data of the target in rectangular coordinates and the motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model and a uniformly decelerated linear motion model;

[0012] A measurement model determination model, used to determine a measurement model of initial position parameters of a target in a polar coordinate system according to the current motion state measurement data and the motion model; and

[0013] The first estimated value calculation module is used to calculate the first estimated value of the target initial position parameter through polynomial fitting according to the measurement model.

[0014] Another object of an embodiment of the present invention is to provide a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned target motion state estimation method.

[0015] Another object of an embodiment of the present invention is to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned target motion state estimation method.

[0016] An embodiment of the present invention provides a method for estimating the initial motion state of a target. This scheme can determine the measurement model of the initial position parameters of the target through the measurement data of the current motion state of the target and the motion model of the target, and then calculate an estimated value of the initial position parameters of the target through a polynomial fitting algorithm. The application of low-cost radar without angle measurement capability in target motion state estimation is realized. Compared with algorithms such as extended Kalman filter (EKF), unscented Kalman filter (UKF), volumetric Kalman filter (CKF), particle filter (PF), conversion measurement Kalman filter (CMKF), etc., which cannot perform state initialization well when there is a lack of angle information, and may cause the filtering results to diverge or converge too slowly, the method in this scheme uses the measurement data of the previous motion state of the target in a rectangular coordinate system, which can be unaffected by the angle, thereby improving the estimation effect of the target motion state. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for estimating the initial motion state of a target provided by an embodiment of the present invention;

[0018] Figure 2 A flowchart of another method for estimating the initial motion state of a target provided by an embodiment of the present invention;

[0019] Figure 3 A structural block diagram of a device for estimating the initial motion state of a target provided by an embodiment of the present invention;

[0020] Figure 4 A structural block diagram of another device for estimating the initial motion state of a target provided by an embodiment of the present invention;

[0021] Figure 5 is a block diagram of the internal structure of a computer device in one embodiment;

[0022] Figure 6 A schematic diagram of a target uniform linear motion model provided by an embodiment of the present invention;

[0023] Figure 7 A schematic diagram of a motion path of a target provided by an embodiment of the present invention;

[0024] Figure 8 A schematic diagram of measuring the distance and speed of a target provided by an embodiment of the present invention;

[0025] Fig. 9 This is a graph of iterative convergence of target initial motion parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0028] In the prior art, when the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), particle filter (PF), conversion measurement Kalman filter (CMKF) and other methods are used for radars without angle measurement capability, the estimation accuracy of the target motion state is limited. The reasons for the poor estimation effect are analyzed as follows:

[0029] Assume that the state sequence of the target is x0, x1, x2, ...x N , the measurement sequence is y1, y2, ....y N , then its posterior probability can be expressed as:

[0030] It is assumed that the observation noise is uncorrelated and both the measurement and the state satisfy the Markov property, then:

[0031]

[0032]

[0033]

[0034] It can be seen from the above formula that the posterior probability is mainly composed of three parts: the initial state probability p(x0), the state transition probability p(x n |x n-1 ) and the measurement probability p(y n |x n ), for radars without angle measurement capability, it is difficult to obtain a relatively accurate initial state x0 using conventional methods. If the initial state deviates too far from its true position, the filtering result may diverge or converge too slowly, which leads to poor motion state estimation based on the Bayesian filter, which is undesirable in practical applications.

[0035] like Figure 1 As shown, in one embodiment, a method for estimating the initial motion state of a target is proposed. The method for estimating the initial motion state of a target may specifically include the following steps:

[0036] Step S202, obtaining the current motion state measurement data of the target in rectangular coordinates and the motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model and a uniformly decelerated linear motion model.

[0037] In the embodiment of the present invention, the current motion state measurement data of the target in rectangular coordinates can be obtained by radar measurement. The current motion state measurement data of the target includes the position information and speed information of the target.

[0038] In the embodiment of the present invention, the target motion model is pre-set and can be determined based on empirical knowledge of the target motion. The target motion model is used to reflect the relationship between the target initial motion state parameters and the current motion state parameters, for example, Figure 6 As shown in the figure, it is a schematic diagram of the target uniform linear motion model. When the initial position of the target in the polar coordinates is (r0, α) and the initial velocity is (v0, β), the initial position and initial velocity of the target are converted to the rectangular coordinate system as follows:

[0039]

[0040]

[0041] Since the target moves in a uniform linear motion on a two-dimensional plane, the current moment t n The position of the target in the rectangular coordinate system (x n ,y n ) can be expressed as:

[0042]

[0043] Step S204: determining a measurement model of initial position parameters of the target in a polar coordinate system according to the current motion state measurement data and the motion model.

[0044] In the embodiment of the present invention, the current time t can be n The position of the target in the rectangular coordinate system (x n ,y n ) can be expressed by the initial position and initial velocity of the target in polar coordinates. The measurement model of the initial position parameters of the target in the polar coordinate system can be determined by converting the position information data and velocity information data of the target in the rectangular coordinate system to the polar coordinate system. For example, the motion state of the target at the current moment in polar coordinates is (r n , v n ), then r n and v n They can be expressed as:

[0045]

[0046] Let cos(β-α)=η, (r0, v0, η) is the parameter to be estimated, the initial position parameter of the target in polar coordinates can be expressed as (r0, v0, η), then the measurement model of the initial position parameter of the target in polar coordinates can be specifically expressed as:

[0047]

[0048] Among them, (r0, α) is the initial position of the target in polar coordinates, (v0, β) is the initial velocity of the target in polar coordinates η = cos (β-α), t n is the current moment, w(n) and s(n) are t n The measurement noise at the moment can be approximately Gaussian distributed, satisfying σ r is the distance measurement accuracy of the radar system, σ v is the velocity measurement accuracy of the radar system, σ r and σ v The specific value depends on the designed radar system. Usually, when the radar system is not modified, the distance accuracy and speed accuracy remain unchanged. n , α) is the target in polar coordinates t n The position at the moment, (v n , β) is the target in polar coordinates t n The speed of time.

[0049] Step S206: Calculate first estimated values ​​of target initial position parameters by polynomial fitting according to the measurement model.

[0050] In the embodiment of the present invention, step S206 may specifically include the following steps:

[0051] Step S302, obtaining the following relationship according to the measurement model:

[0052] Where p(n) and q(n) are the noise diffusion components;

[0053] In the embodiment of the present invention, the above relationship can be obtained by transforming the measurement model of the initial position parameter. If the measurement model conversion is not performed, the relationship between the measurement value and the state value is a complex nonlinear relationship. After the conversion, the relationship between the pseudo-measurement and the state value can be simplified, and then the coupling term result of the state value can be obtained by polynomial fitting. The noise diffusion components p(n) and q(n) can also approximately obey the Gaussian distribution. p(n) obeys the mean of 0 and the standard deviation of The normal distribution of q(n) has a mean of 0 and a standard deviation of Step S304: The curve is fitted with a second-order polynomial to determine the second fitting coefficient W2, where w2 = [w 20 , w 21 , w 22 ] is a 1×3 dimensional vector;

[0054] Step S306, for t n -r n v nThe curve is fitted with a first-order polynomial to determine the first fitting coefficient W1, where w1 = [w 10 , w 11 ] is a 1×2 dimensional vector;

[0055] Step S308, determining a first estimated value of the target initial position parameter according to the first fitting coefficient and the second fitting coefficient, specifically:

[0056] in is the estimated value corresponding to r0, is the estimated value corresponding to v0, is the estimated value corresponding to η. Polynomial fitting is a prior art method, and the process of determining the second fitting coefficient W2, determining the first fitting coefficient W1, and obtaining the rough estimated value is not elaborated in detail in this embodiment.

[0057] An embodiment of the present invention provides a method for estimating the initial motion state of a target. This scheme can determine the measurement model of the initial position parameters of the target through the measurement data of the current motion state of the target and the motion model of the target, and then calculate an estimated value of the initial position parameters of the target through a polynomial fitting algorithm. The application of low-cost radar without angle measurement capability in target motion state estimation is realized. Compared with algorithms such as extended Kalman filter (EKF), unscented Kalman filter (UKF), volumetric Kalman filter (CKF), particle filter (PF), conversion measurement Kalman filter (CMKF), etc., which cannot perform state initialization well when there is a lack of angle information, and may cause the filtering results to diverge or converge too slowly, the method in this scheme uses the measurement data of the previous motion state of the target in a rectangular coordinate system, which can be unaffected by the angle, thereby improving the estimation effect of the target motion state.

[0058] like Figure 2 As shown, in another embodiment of the present invention, the target initial motion state estimation method further includes the following steps:

[0059] Step S208: determining the estimated value of the current motion parameter of the target in polar coordinates according to the first estimated value of the initial position parameter of the target.

[0060] In the embodiment of the present invention, the current motion parameter estimation value of the target in polar coordinates is determined according to the first estimation value of the initial position parameter of the target, that is, the current motion parameter estimation value of the target in polar coordinates t n The estimated value of the motion parameters at time t, the target is in polar coordinates n The motion parameters at the moment include the distance parameter value r n According to the measurement model of the initial position parameters of the target in the polar coordinate system, we know that r nThe relationship between the initial position parameters (r0, v0, η) of the target is obtained by replacing r0, v0, η in the relationship with the first estimated value of the initial position parameters, so that the estimated value of the current motion parameter marked in polar coordinates can be obtained.

[0061] Step S210, according to the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates, determine the log-likelihood function model of the replacement variable by variable substitution, wherein the variable is a variable represented by the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates.

[0062] In the embodiment of the present invention, step S210 may specifically include the following steps:

[0063] Step S402, determine the replacement variable S n and Q n , specifically:

[0064] in is the target in polar coordinates determined by the first estimated value of the target initial position parameter n The distance parameter value at the time, w s (n) and s q (n) is the noise diffusion component, satisfying:

[0065] In the embodiment of the present invention, since r n , v n are independent of each other, replacing the variable S n and Q n They are also independent of each other.

[0066] Step S404, determine the replacement variable S n and Q n The corresponding log-likelihood function model is as follows:

[0067]

[0068] Step S212, calculating a second estimated value of the target initial position parameter according to the log-likelihood function model.

[0069] In the embodiment of the present invention, assuming that the parameter to be estimated is [r0, v0, η] = θ, then the maximum likelihood estimate of the initial position parameter θ can be obtained according to the log-likelihood function model as follows:

[0070] Among them, θ=[r0,v0,η], and satisfies R min ≤r0≤R max , vmin ≤v0≤v max , -1≤η≤1, R min , R max , v min , v max It is a preset value, and the specific value can be set according to the actual scenario. Since η=cos(β-α), the condition of |η|≤1 needs to be satisfied.

[0071] In an embodiment of the present invention, after determining the maximum likelihood estimate of the target initial position parameter θ, the second estimate can be calculated by a numerical optimization algorithm based on the maximum likelihood estimate of the initial position parameter θ. The parameter estimation problem in the present invention is converted into a nonlinear programming problem under inequality constraints, for example, it can be solved by a penalty function method, a feasible direction method, a trust region method, and an interior point method. The embodiment of the present invention uses the penalty function method plus the Levenberg-Marquardt (LM) algorithm as an example to illustrate the process of calculating the second estimate of the target initial position parameter: Construct the objective function according to the penalty function method:

[0072]

[0073] Where μ is the penalty factor. The smaller the value, the closer the final solution is to the original optimal solution. According to the iteration of the LM algorithm, we can know that:

[0074] θ k+1 =θ k -(H(θ k )+λI) -1 J(θ)

[0075] where θ k+1 ,θ k are the state estimates for k+1 and k iterations respectively, λ is the damping factor, the value of which can be found in the literature related to the LM algorithm, J(θ) is the Jacobian matrix corresponding to the objective function, and H(θ) is the Hessian matrix corresponding to the objective function. Finally, the iteration is terminated by setting the iteration termination condition, and the second estimate of the target initial position parameter is calculated.

[0076] In the embodiment of the present invention, it is assumed that the initial distance of the target is 150m, the moving speed is 20m / s, the angle between the moving speed and the line of sight is 45°, the radar detection period is 50ms, the distance measurement accuracy is 2m, the speed measurement accuracy is 0.5m / s, and the number of observation points N=50. The target's moving trajectory and the corresponding distance and speed measurements are as follows: Figure 7-8 As shown, according to the initial distance of the target, the moving speed and the angle between the moving speed and the line of sight, the motion parameter to be estimated is θ = [r0, v0, η] T The true value of The initial motion state parameters of the target are estimated by a target initial motion state estimation method provided by an embodiment of the present invention. The measurement samples obtained by radar detection are r1, r2, ..., r 50 , v1, v2, …, v 50 According to the empirical value, σ r =5,σ v =2; then, according to the measurement model, the first estimated value of the target initial position parameter is calculated by polynomial fitting, and the second fitting coefficient w2 = [624.3, -5062.7, 23168.5] and the first fitting coefficient w1 = [413.9, -2159.3] are obtained. is 152.2, is 20.3, and the estimated value of β-α is 45.6°. It can be seen that although the first estimated value obtained by the present application scheme has a certain error with the true value of the target initial motion state, the error value is small. If the user has a small requirement for the accuracy of the estimated value, the first estimated value obtained can be used as the estimation result of the target initial motion state. Of course, if you want to obtain a higher estimation accuracy, you can further calculate the second estimated value of the target initial position parameter according to the log-likelihood function model. Since r1=152.2, according to the distance measurement accuracy σ r =5m, the value interval of r0 can be selected as r0∈[142.2,162.2]. And the speed v1=-15.3m / s, according to the target characteristics (vehicle), the value interval of v0 is selected as v0∈[14,50]. Since the target distance is gradually decreasing, the value interval of η is set to η∈(0,1]. Further, the calculation using the LM algorithm is used as an example. For example, the penalty factor μ is set to 0.00001, and the maximum number of iterations of the LM algorithm is set to 100. When the relative step is less than 10 -5 When , the iteration stops, and the iteration result of the estimated parameters is as follows Fig. 9 Finally, the precise estimated values ​​of the target motion state are obtained, r0=150.1, v0=20.0, β-α=45.1°. It can be seen that the estimation accuracy of the target initial motion state is further improved by calculating the second estimated value of the target initial position parameter according to the log-likelihood function model.

[0077] An embodiment of the present invention provides a method for estimating an initial motion state of a target. The method obtains an estimated value of a current motion parameter of the target in polar coordinates according to a first estimated value of the initial position parameter of the target. Then, based on the estimated value of the current motion parameter of the target in polar coordinates and the measured value of the current motion parameter of the target in polar coordinates, a log-likelihood function model of a replacement variable is determined by variable substitution. A second estimated value of the initial position parameter of the target is calculated according to the log-likelihood function model. The second estimated value is used as an estimation result of the initial motion state estimation of the target, thereby further improving the accuracy of the estimation of the initial motion state of the target.

[0078] like Figure 3 As shown, in one embodiment, a target initial motion state estimation device is provided, which can be integrated into a computer device, and specifically can include a data acquisition module 510, a measurement model determination model 520, and a first estimation value calculation module 530.

[0079] The data acquisition module 510 is used to acquire the current motion state measurement data of the target in rectangular coordinates and the motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model and a uniformly decelerated linear motion model;

[0080] A measurement model determination model 520, for determining a measurement model of initial position parameters of a target in a polar coordinate system according to the current motion state measurement data and the motion model; and

[0081] The first estimated value calculation module 530 is used to calculate the first estimated value of the target initial position parameter through polynomial fitting according to the measurement model.

[0082] An embodiment of the present invention provides a target initial motion state estimation device, which includes a data acquisition module 510, a measurement model determination model 520, and a first estimation value calculation module 530, whose functional implementations correspond one-to-one to steps S202, S204, and S206 in the above target initial motion state estimation method. For the specific explanation of the target initial motion state estimation device, as well as the related refinement and optimization contents, please refer to the specific embodiments of the above target initial motion state estimation method, which will not be repeated here.

[0083] like Figure 4 As shown, in another embodiment of the present invention, the target initial motion state estimation device may further include a current motion parameter estimation module 540, a function model determination module 550, and a second estimation value calculation module 560;

[0084] A current motion parameter estimation module 540, configured to determine a current motion parameter estimation value of the target in polar coordinates according to a first estimation value of the target initial position parameter;

[0085] A function model determination module 550 is used to determine a log-likelihood function model of a replacement variable by variable substitution according to the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates, wherein the variable is a variable represented by the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates;

[0086] The second estimation value calculation module 560 is used to calculate the second estimation value of the target initial position parameter according to the log-likelihood function.

[0087] An embodiment of the present invention provides a target initial motion state estimation device, which includes a current motion parameter estimation module 540, a function model determination module 550, and a second estimation value calculation module 560, which correspond one-to-one to steps S208, S210, and S212 in the above target initial motion state estimation method. For the specific explanation of the target initial motion state estimation device, as well as the related refinement and optimization contents, please refer to the specific embodiment of the target initial motion state estimation method above, which will not be repeated here.

[0088] Figure 5 FIG. 2 shows an internal structure diagram of a computer device in one embodiment. Figure 5 As shown, the computer device includes a processor, a memory, a network interface, an input device and a display screen connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the target initial motion state estimation method. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can execute the target initial motion state estimation method. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0089] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] In one embodiment, the target initial motion state estimation device provided by the present application can be implemented in the form of a computer program. The computer program can be Figure 5 The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the target initial motion state estimation device, such as: Figure 3 The data acquisition module 510, the measurement model determination model 520, and the first estimation value calculation module 530 are shown. The computer program composed of various program modules enables the processor to execute the steps of the target initial motion state estimation method of each embodiment of the present application described in this specification.

[0091] For example, Figure 5 The computer device shown can be Figure 3 The data acquisition module 510 in the target initial motion state estimation device shown in the figure performs step S202. The computer device can perform step S204 through the measurement model determination model 520. The computer device can perform step S206 through the first estimation value calculation module 530.

[0092] In one embodiment, a computer device is provided, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0093] Step S202, obtaining current motion state measurement data of the target in rectangular coordinates and a motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model, and a uniformly decelerated linear motion model;

[0094] Step S204, determining a measurement model of initial position parameters of the target in a polar coordinate system according to the current motion state measurement data and the motion model;

[0095] Step S206: Calculate first estimated values ​​of target initial position parameters by polynomial fitting according to the measurement model.

[0096] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following steps:

[0097] Step S202, obtaining current motion state measurement data of the target in rectangular coordinates and a motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model, and a uniformly decelerated linear motion model;

[0098] Step S204, determining a measurement model of initial position parameters of the target in a polar coordinate system according to the current motion state measurement data and the motion model;

[0099] Step S206: Calculate first estimated values ​​of target initial position parameters by polynomial fitting according to the measurement model.

[0100] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0101] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0102] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating the initial motion state of a target, characterized in that: The target initial motion state estimation method comprises: Acquire current motion state measurement data of the target in rectangular coordinates and a motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model, and a uniformly decelerated linear motion model; Determine a measurement model of initial position parameters of the target in a polar coordinate system according to the current motion state measurement data and the motion model; Calculating a first estimate of a target initial position parameter by polynomial fitting according to the measurement model; The target initial motion state estimation method further includes: Determining a current motion parameter estimate of the target in polar coordinates based on a first estimate of the target initial position parameter; Determine a log-likelihood function model of a replacement variable by variable substitution according to the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates, wherein the variable is a variable represented by the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates; A second estimated value of the target initial position parameter is calculated according to the log-likelihood function model.

2. A method for estimating the initial motion state of a target according to claim 1, characterized in that: When the motion model of the target is a uniform linear motion model, the measurement model of the initial position parameters of the target in the polar coordinate system is determined according to the current motion state measurement data and the motion model, specifically: Among them, (r0, α) is the initial position of the target in polar coordinates, (v0, β) is the initial velocity of the target in polar coordinates, η = cos (β-α), t n is the current moment, w(n) and s(n) are t n The measurement noise at the moment, σ r is the distance measurement accuracy of the radar system, σ v is the velocity measurement accuracy of the radar system, (r n , α) is the target in polar coordinates t n The position at the moment, (v n , β) is the target in polar coordinates t n The speed of time.

3. A method for estimating the initial motion state of a target according to claim 2, characterized in that: Calculating a first estimated value of an initial position parameter of a target in the measurement model by polynomial fitting includes: According to the measurement model, the following relationship is obtained: Where p(n) and q(n) are the noise diffusion components; right The curve is fitted with a second-order polynomial to determine the second fitting coefficient w2, where w2 = [w 20 ,w 21 ,w 22 ] is a 1×3 dimensional vector; t n -r n v n The curve is fitted with a first-order polynomial to determine the first fitting coefficient w1, where w1 = [w 10 ,w 11 ] is a 1×2 dimensional vector; The first estimated value of the target initial position parameter is determined according to the first fitting coefficient and the second fitting coefficient, specifically: in is the estimated value corresponding to r0, is the estimated value corresponding to v0, is the estimated value corresponding to η.

4. A method for estimating the initial motion state of a target according to claim 3, characterized in that: According to the current motion parameter estimation value of the target in the polar coordinates and the current motion parameter measurement value of the target in the polar coordinates, a log-likelihood function model of the replacement variable is determined by variable replacement, including: Determine the replacement variable S n and Q n , specifically: in is the target in polar coordinates determined by the first estimated value of the target initial position parameter n The distance parameter value at the time, w s (n) and s q (n) is the noise diffusion component, satisfying: Determine the replacement variable S n and Q n The corresponding log-likelihood function model is:

5. A method for estimating the initial motion state of a target according to claim 4, characterized in that: Calculating a second estimated value of the target initial position parameter according to the log-likelihood function model includes: The maximum likelihood estimate of the initial location parameter θ is determined according to the log-likelihood function model, specifically: Among them, θ=[r0,v0,η], and satisfies R min ≤r0≤R max , v min ≤v0≤v max , -1≤η≤1, R min , R max , v min , v max is the default value; The second estimated value is calculated by a numerical optimization algorithm based on the maximum likelihood estimate of the initial position parameter θ.

6. A device for estimating the initial motion state of a target, characterized in that: The target initial motion state estimation device comprises: A data acquisition module, used to acquire the current motion state measurement data of the target in rectangular coordinates and the motion model of the target, wherein the motion model includes a uniform linear motion model, a uniformly accelerated linear motion model and a uniformly decelerated linear motion model; A measurement model determination model, used to determine a measurement model of initial position parameters of a target in a polar coordinate system according to the current motion state measurement data and the motion model; and A first estimated value calculation module, used for calculating a first estimated value of a target initial position parameter by polynomial fitting according to the measurement model; The target initial motion state estimation device further includes: A current motion parameter estimation module, used to determine the estimated value of the current motion parameter of the target in polar coordinates according to the first estimated value of the initial position parameter of the target; A function model determination module, used to determine a log-likelihood function model of a replacement variable by variable substitution according to the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates, wherein the variable is a variable represented by the current motion parameter estimation value of the target in polar coordinates and the current motion parameter measurement value of the target in polar coordinates; The second estimation value calculation module is used to calculate the second estimation value of the target initial position parameter according to the log-likelihood function.

7. A computer device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the target initial motion state estimation method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the target initial motion state estimation method according to any one of claims 1 to 5.

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