Motion track determination method and device and electronic equipment

Abstract

The invention discloses a method and a device for determining a motion track and electronic equipment. The method comprises the following steps: acquiring an echo signal of a target object acquired by a radar system, and performing time-frequency analysis on the echo signal to obtain a radial speed and a direction of arrival of the target object; performing staged processing on the radial speed and the direction of arrival of the target object to obtain a first fitness equation and a second fitness equation; determining a first target optimization function according to the first fitness equation, and determining a second target optimization function according to the second fitness equation; and solving the first target optimization function and the second target optimization function to obtain a motion track of the target object. According to the invention, the technical problem of poor positioning precision caused by mutual interference of emission components during positioning of a traditional Doppler radar is solved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, device and electronic device for determining a motion trajectory. Background Art

[0002] In recent years, indoor target positioning technology has attracted widespread attention due to its broad application prospects. Among various positioning technologies, Doppler radar has become a research hotspot due to its relatively simple structure and excellent clutter suppression capabilities. However, in practical applications, traditional Doppler radars suffer from large scale, high cost, and interference between transmission components.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, and electronic device for determining a motion trajectory, so as to at least solve the technical problem that the emission components of a traditional Doppler radar interfere with each other during positioning, resulting in poor positioning accuracy.

[0005] According to one aspect of an embodiment of the present application, a method for determining a motion trajectory is provided, including: obtaining an echo signal of a target object collected by a radar system, and performing time-frequency analysis on the echo signal to obtain a radial velocity and an angle of arrival of the target object; processing the radial velocity and the angle of arrival of the target object in stages to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent the absolute value of the error between an ideal value and an estimated value of the motion state of the target object in the first stage, and the second fitness equation is used to represent the absolute value of the error between an ideal value and an estimated value of the motion state of the target object in the second stage, and the second stage is after the first stage; determining a first objective optimization function based on the first fitness equation, and determining a second objective optimization function based on the second fitness equation; solving the first objective optimization function and the second objective optimization function to obtain the motion trajectory of the target object.

[0006] Optionally, the radial velocity and angle of arrival of the target object are processed in stages to obtain a first fitness equation, including: in a first stage, obtaining a first Y-axis coordinate of the lth data of the target object in the j1th time window at the k1th time, a first X-axis coordinate of the lth data of the target object in the j1th time window at the k1th time, and a first angle of arrival between the position of the lth data of the target object in the j1th time window at the k1th time and the positive direction of the X-axis, where k, j, and l are positive integers; determining a first numerical value based on the first Y-axis coordinate, the first X-axis coordinate, and the first angle of arrival; obtaining a first motion velocity of the first data of the target object in the j1th time window at the k1th time, a first angle of the first data of the target object in the j1th time window at the k1th time, and a first radial velocity of the lth data of the target object in the j1th time window at the k1th time; determining a second numerical value based on the first motion velocity, the first angle of arrival, the first angle, and the first radial velocity; and determining the first fitness equation based on the first numerical value and the second numerical value.

[0007] Optionally, the radial velocity and the angle of arrival of the target object are processed in stages to obtain a second fitness equation, including: in the second stage, obtaining multiple sub-fitness equations, wherein the multiple sub-fitness equations are conditional functions of the second fitness equation; and determining the second fitness function based on the multiple sub-fitness equations.

[0008] Optionally, determining a second fitness function based on a plurality of sub-fitness equations includes: obtaining a second Y-axis coordinate of the lth data of the target object in the j2th time window at the k2th moment, a second X-axis coordinate of the l2th data of the target object in the j2th time window at the k2th moment, and a second angle of arrival between the position of the lth data of the target object in the j2th time window at the k2th moment and the positive direction of the X-axis; determining the first sub-fitness equation based on the second Y-axis coordinate, the second X-axis coordinate and the second angle of arrival; obtaining a second motion speed of the first data of the target object in the j2th time window at the k2th moment, a second angle of the first data of the target object in the j2th time window at the k2th moment, and a second radial velocity of the lth data of the target object in the j2th time window at the k2th moment; determining the first sub-fitness equation based on the second motion speed, the second angle of arrival, the second angle and the second radial velocity. Determine a second sub-fitness equation; obtain the third motion velocity of the first data of the target object in the (j-1)2th time window at the k2th time, the sampling time interval of the echo signal, and the acceleration of the first data of the target object in the j2th time window at the k2th time; determine the third sub-fitness equation based on the second motion velocity, the third motion velocity, the sampling time interval, and the acceleration; obtain the third X-axis coordinate of the first data of the target object in the j2th time window at the k2th time, and the fourth X-axis coordinate of the first data of the target object in the (j-1)2th time window at the k2th time; determine the fourth sub-fitness equation based on the third X-axis coordinate, the second motion velocity, the second angle, the sampling time interval, the acceleration, and the fourth X-axis coordinate; determine the second fitness equation based on the first sub-fitness equation, the second sub-fitness equation, the third sub-fitness equation, and the fourth sub-fitness equation.

[0009] Optionally, the first objective optimization function is determined based on the first fitness equation, and the second objective optimization function is determined based on the second fitness equation, including: obtaining the first fitness equation corresponding to all time windows in the first stage and the first quantity of all data in the first stage; determining the first objective optimization function based on the first fitness equation and the first quantity corresponding to all time windows in the first stage; obtaining the second fitness equation corresponding to all time windows in the second stage and the second quantity of all data in the second stage; determining the second objective optimization function based on the second fitness equation and the second quantity corresponding to all time windows in the second stage.

[0010] Optionally, solving the first objective optimization function and the second objective optimization function to obtain the motion trajectory of the target object includes: step 1: according to the first objective optimization function, determining the individual with the smallest fitness function value corresponding to all time windows in the first stage as the first optimal individual of the current stage, wherein the first optimal individual represents the optimal solution of the first parameter in the corresponding time window of the first stage, and the first parameter includes the X-axis coordinate, movement speed and the angle between the forward direction of the target object and the positive direction of the X-axis in the corresponding time window; step 2: updating the first optimal individual according to the first optimal individual, the first fitness function value, the second fitness function value, the first number corresponding to the first parameter, and the second number of time windows in the first stage, and repeating steps 1 to 2, wherein the first fitness function value and the second fitness function value are the fitness function values ​​corresponding to any two time windows in the first stage; step 3: stopping the iteration when the first optimal individual reaches the preset accuracy or reaches the maximum number of iterations, and obtaining the first target optimal individual, wherein the first target optimal individual represents the result obtained after solving the first objective optimization function; step 4: The best individual is used as a reference for solving the second objective optimization function, and according to the second objective optimization function, the individual with the smallest fitness function value corresponding to all time windows in the second stage is determined as the second optimal individual in the current stage, wherein the second optimal individual represents the optimal solution of the second parameter in the corresponding time window of the second stage, and the second parameter includes the X-axis coordinate, movement speed, angle between the forward direction of the target object and the positive direction of the X-axis, and acceleration of the target object in the corresponding time window; Step 5: Based on the second optimal individual, the third fitness function value, the fourth fitness function value, the third number corresponding to the multiple second parameters, and the fourth number of time windows in the second stage, the second optimal individual is updated, and steps 4 to 5 are repeated, wherein the third fitness function value and the fourth fitness function value are the fitness function values ​​corresponding to any two time windows in the second stage; Step 6: When the second optimal individual reaches a preset accuracy or reaches the maximum number of iterations, the iteration is stopped to obtain the second objective optimal individual, wherein the second objective optimal individual represents the result obtained after solving the second objective optimization function; and the motion trajectory of the target object is determined based on the second objective optimal individual.

[0011] Optionally, the method also includes: when the first optimal individual or the second optimal individual is less than or equal to the individual upper limit and greater than or equal to the individual lower limit, updating the first optimal individual or the second optimal individual based on the individual upper limit, the individual lower limit and the random number; when the first optimal individual is greater than the individual upper limit or less than the individual lower limit, not updating the first optimal individual or the second optimal individual.

[0012] According to another aspect of an embodiment of the present application, a device for determining a motion trajectory is also provided, including: an acquisition module, used to obtain an echo signal of a target object collected by a radar system, and perform time-frequency analysis on the echo signal to obtain a radial velocity and an angle of arrival of the target object; a processing module, used to perform stage-by-stage processing on the radial velocity and the angle of arrival of the target object to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent the absolute value of the error between an ideal value and an estimated value of the motion state of the target object in the first stage, and the second fitness equation is used to represent the absolute value of the error between an ideal value and an estimated value of the motion state of the target object in the second stage, and the second stage is after the first stage; a determination module, used to determine a first objective optimization function based on the first fitness equation, and to determine a second objective optimization function based on the second fitness equation; a solution module, used to solve the first objective optimization function and the second objective optimization function to obtain the motion trajectory of the target object.

[0013] According to another aspect of the embodiments of the present application, an electronic device is provided, including: a memory and a processor, the memory being used to store program instructions; the processor being connected to the memory and being used to execute the method for determining the motion trajectory.

[0014] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned method for determining the motion trajectory by running the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including computer instructions, which implement the above-mentioned method for determining the motion trajectory when executed by a processor.

[0016] In an embodiment of the present application, the radial velocity and angle of arrival of the target object are obtained by acquiring the echo signal of the target object collected by the radar system and performing time-frequency analysis on the echo signal; the radial velocity and angle of arrival of the target object are processed in stages to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent the absolute value of the error between the ideal value and the estimated value of the motion state of the target object in the first stage, and the second fitness equation is used to represent the absolute value of the error between the ideal value and the estimated value of the motion state of the target object in the second stage, and the second stage is after the first stage; a first objective optimization function is determined based on the first fitness equation, and a second objective optimization function is determined based on the second fitness equation; the first objective optimization function and the second objective optimization function are solved to obtain the motion trajectory of the target object, thereby achieving the purpose of efficient and high-precision positioning of human targets using a single-frequency Doppler radar system, thereby achieving the technical effect of improving positioning accuracy, and further solving the technical problem that the traditional Doppler radar has interference with the emission components during positioning and poor positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a hardware structure block diagram of a computer terminal for implementing a method for determining a motion trajectory according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for determining a motion trajectory according to an embodiment of the present application;

[0020] Figure 3 is a two-dimensional planar structural diagram of a single-frequency radar system according to an embodiment of the present application;

[0021] Figure 4 is a schematic diagram of an experimental test scenario according to an embodiment of the present application;

[0022] Figure 5 is a schematic diagram of a Doppler radar device according to an embodiment of the present application;

[0023] Figure 6 4 is a structural diagram of a motion trajectory determination device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] First, some nouns or terms that appear in the process of explaining the embodiments of this application are subject to the following explanations:

[0027] Subtraction-Average-Based Optimizer (SABO): SABO is a new metaheuristic algorithm for finding optimal solutions to optimization problems. The core of the algorithm is to update the positions of group members in the search space by subtracting the average value of the individuals in the group.

[0028] The Short-Time Fourier Transform (STFT) method is a transformation method that connects the time domain and frequency domain of a signal. The STFT performs local processing on the signal by using window functions of different forms and lengths.

[0029] Hough Transform: A commonly used image detection algorithm, Hough Transform utilizes the information exchange between image space and parameter space. Specifically, for every point in image space, there is a corresponding line in parameter space. Similarly, lines in image space are mapped to points in parameter space. This dualistic information exchange allows the information in parameter space to be supplemented, aiding in the determination of image relationships.

[0030] Bezier curve: A method for drawing a curve based on set points in space. By adjusting the positions of the points, the curve can be made to "stretch" and "bend" in space.

[0031] Doppler radar can be divided into single-frequency Doppler radar (SF-DR), multi-frequency Doppler radar (MF-DR) and linear frequency modulation Doppler radar (FM-DR) according to the transmission frequency.

[0032] Among these, single-frequency Doppler radar, with a single transmitter and receiver, is the simplest, most basic, and lowest-power detection system. It can measure Doppler frequency shift information with high precision. However, this radar system typically only measures the target's velocity and lacks the ability to estimate its range. For example, a single-frequency continuous-wave radar is used to measure the velocity of particles released after an explosion.

[0033] To better extract moving target information, some methods propose increasing the number of receiver groups and using single-frequency array processing to obtain direction-of-arrival (DOA) information from multiple targets, enabling single-frequency continuous-wave radar positioning of moving targets. While these methods can achieve target position estimation at a low cost and power consumption, their positioning accuracy and noise immunity are less than ideal.

[0034] Some methods have proposed increasing the number of transmitter groups and using array processing techniques to estimate target motion. This approach can more accurately obtain target positioning results from single-frequency continuous-wave radars. However, the array format requires large antenna groups as hardware support, which will significantly increase the size and power consumption of the radar system.

[0035] Multi-frequency Doppler radars can achieve better positioning accuracy within limited bandwidth and processing resources by increasing the transmission frequency. However, as the frequency increases, the radio frequency interference between frequency components also increases. Therefore, dual-frequency Doppler radar (DF-DR) systems are currently widely used in target detection.

[0036] A target positioning method based on a dual-frequency continuous-wave radar system can locate human targets using a continuous-wave radar system consisting of a single transmitter and two receivers, effectively optimizing the system's antenna configuration. However, the phase estimation of the echoes received by the two receivers is highly sensitive to noise interference, resulting in unstable algorithm performance.

[0037] Some methods propose using the target's continuous wave frequency integral instead of direct phase estimation, successfully achieving more stable target estimation results. Furthermore, combining array processing techniques with time-frequency analysis methods to extract target information from the range-Doppler space can more accurately locate human motion trajectories.

[0038] Compared to single-frequency and multi-frequency Doppler radars, linear frequency modulation Doppler radar can more easily obtain target range, velocity, and multi-target resolution information. However, its signal processing and system structure are more complex, and the cost of operation is also higher.

[0039] In summary, existing research in the field of human target positioning primarily focuses on single-frequency Doppler radars using dual-frequency, linear frequency modulation, or array configurations. However, traditional single-frequency positioning solutions require a large number of sensors and suffer from insufficient accuracy. Traditional multi-frequency and frequency modulation positioning methods consume high power and cost, and are subject to radio frequency interference issues. Therefore, there is room for improvement in both system architecture and economic efficiency.

[0040] In order to solve the problems existing in the related art, the embodiment of the present application provides a method for determining a motion trajectory, which can be run on Figure 1 In the computer terminal shown, the computer terminal is explained below.

[0041] The motion trajectory determination method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for determining a motion trajectory. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated by 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0042] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0043] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the motion trajectory in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned method for determining the motion trajectory. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0044] The transmission module 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission module 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission module 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0045] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0046] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0047] In the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a method for determining a motion trajectory. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0048] Figure 2 is a flow chart of a method for determining a motion trajectory according to an embodiment of the present application, such as Figure 2 As shown, the method includes the following steps:

[0049] Step S202 : Acquire the echo signal of the target object collected by the radar system, and perform time-frequency analysis on the echo signal to obtain the radial velocity and arrival angle of the target object.

[0050] In the above step S202, the working principle of the radar system is to transmit electromagnetic waves and then receive the electromagnetic waves reflected by the target object, that is, the echo signal. The echo signal carries information such as the target distance, speed and direction. For a single-frequency continuous wave radar (SF-CW radar), it transmits a continuous wave of a fixed frequency, and the echo signal reflected by the target will produce a Doppler frequency shift due to the movement of the target. In order to extract useful information from the echo signal, such as radial velocity and angle of arrival, it is necessary to perform time-frequency analysis on the echo signal. The time-frequency analysis method enables the radar system to identify how the frequency components of the signal change over time. In an embodiment of the present application, a short-time Fourier transform (STFT) is used to perform time-frequency analysis on the echo signal to obtain the radial velocity and angle of arrival of the target object.

[0051] In order to simplify the radar system structure and reduce the operating power consumption of the radar system, the embodiment of the present application uses a single-frequency radar system. Figure 3 is a two-dimensional planar structure diagram of a single-frequency radar system according to an embodiment of the present application, such as Figure 3 As shown in the figure, the single-frequency radar system structure consists of a transmitter (T X ) and two receivers ( and ), where the human target moves at a speed of V k , acceleration a k Move along the dotted line. Σ is an adder. In radar systems, the adder combines signals from different receivers to enhance signal strength or perform preliminary calculations of phase differences. LPF is a low-pass filter, used to remove high-frequency noise and retain the signal's low-frequency components. HPF is a high-pass filter. Radial V is radial velocity, and DOA is angle of arrival.

[0052] Because Doppler radar echo signals are typically non-stationary, they are difficult to directly analyze and extract the corresponding Doppler features. Therefore, a short-time Fourier transform (STFT) method is used for time-frequency analysis to obtain the instantaneous frequency of the target. Based on the Doppler frequency estimate, a mathematical model is constructed based on the spatial relationship between the receiver and the target, thereby inferring the target's motion information.

[0053] Step S204, the radial velocity and the angle of arrival of the target object are processed in stages to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent the absolute value of the error between the ideal value and the estimated value of the motion state of the target object in the first stage, and the second fitness equation is used to represent the absolute value of the error between the ideal value and the estimated value of the motion state of the target object in the second stage, and the second stage is after the first stage.

[0054] In step S204 above, the primary goal of the first phase is to quickly estimate the target object's initial motion state using the radial velocity and angle of arrival within a relatively short time window. This phase processes the data within the first time window. The second phase utilizes data from a longer time window, taking factors such as acceleration into account, to update and refine the target object's motion trajectory. This phase processes data starting from the second time window.

[0055] The first and second fitness equations are minimized as optimization objectives in optimization algorithms (such as ISABO), guiding the algorithm to search for the optimal solution in the decision space. In each iteration, the algorithm attempts to find a set of parameters that minimizes the error expressed by the two equations, thereby obtaining an estimate that is closest to the target's true state. The first fitness equation helps to quickly converge to the target's approximate position and motion state in the early stages, laying the foundation for subsequent precise positioning. The second fitness equation, based on the first stage, further optimizes the accuracy of target positioning and trajectory tracking by gradually introducing more details and complex motion models. This phased processing strategy enables the radar system to significantly improve positioning accuracy while maintaining real-time performance and response speed.

[0056] Specifically, the radial velocity and direction of arrival (DOA) information of the target object obtained by time-frequency analysis are used as input, and a set of nonlinear overdetermined equations are established based on the kinematic theory change trend within the selected time window. Figure 3As shown, data is selected along the sampling time axis in the form of a sliding window of length L and interval ΔT, where ΔT = 1 / 2·L. The available data within the window are s(l), l = 1, 2, …, L. Each s(l) corresponds to a nonlinear equation. By combining these L equations, the target motion state corresponding to the first data point l = 1 in time window j can be solved. To ensure the accuracy of the target positioning results and the efficiency of the calculation process, the solution is divided into two parts: the initial information estimation stage (the first stage below) and the trajectory information update stage (the second stage below). The first stage determines the first fitness equation, and the second stage determines the second fitness equation.

[0057] Step S206: determining a first objective optimization function according to the first fitness equation, and determining a second objective optimization function according to the second fitness equation.

[0058] In step S206, the target optimization function is defined as the mean square error of all fitness equations within a set of time windows. In the first stage, the target optimization function determined by the first fitness equation is the first target optimization function. In the second stage, the target optimization function determined by the second fitness equation is the second target optimization function.

[0059] Step S208 : solving the first objective optimization function and the second objective optimization function to obtain the motion trajectory of the target object.

[0060] In the above step S208, the single-frequency Doppler radar target optimization function (including the first target optimization function and the second target optimization function) is solved based on the improved subtraction average optimization algorithm, so as to obtain the motion trajectory of the target object.

[0061] Through steps S202 to S208 described above, the goal of efficiently and accurately locating a human target using a single-frequency Doppler radar system is achieved, thereby achieving the technical effect of improving positioning accuracy. This further solves the technical problem of conventional Doppler radars, which suffer from interference between their transmission components during positioning and result in poor positioning accuracy. This is described below.

[0062] In step S204 of the method for determining the motion trajectory, the radial velocity and angle of arrival of the target object are processed in stages to obtain a first fitness equation, including: in a first stage, obtaining a first Y-axis coordinate of the lth data of the target object in the j1th time window at the k1th time, a first X-axis coordinate of the lth data of the target object in the j1th time window at the k1th time, and a first angle of arrival between the position of the lth data of the target object in the j1th time window at the k1th time and the positive direction of the X-axis, where k, j, and l are positive integers; determining a first value based on the first Y-axis coordinate, the first X-axis coordinate, and the first angle of arrival; obtaining a first motion velocity of the first data of the target object in the j1th time window at the k1th time, a first angle of the first data of the target object in the j1th time window at the k1th time, and a first radial velocity of the lth data of the target object in the j1th time window at the k1th time; determining a second value based on the first motion velocity, the first angle of arrival, the first angle, and the first radial velocity; and determining the first fitness equation based on the first value and the second value.

[0063] Specifically, in the first stage (i.e., the initial information estimation stage) of the embodiment of the present application, the processed data only involves the data within the first time window (i.e., radial velocity and wave arrival angle), and the window length L1 of the first time window (or the number of data within the first time window) is set short enough so that the influence of acceleration a on the velocity in a very short time can be ignored, that is, the target object can be regarded as uniform motion. By simplifying the motion state to reduce the influencing parameters, the initial position and motion state of the target object can be obtained more accurately. Then according to Figure 3 The geometric relationship in can be used to obtain the position coordinates of the target object in the lth data (l=1,2,…,L1) in the jth time window (j=1). It can be expressed as:

[0064]

[0065] Wherein, j=1 in formula (1), that is, formula (1) can be written as:

[0066]

[0067] Among them, t l The corresponding formula is as follows:

[0068] t l =(l-1)·Δt

[0069] in, The corresponding formula is as follows:

[0070]

[0071] In the above formulas (1) to (2), X k is the X-axis coordinate of the target object at the kth moment on the time axis, represents the first X-axis coordinate of the target object in the lth data in the j1th time window (here, in order to distinguish the different time windows of the first and second stages, k1 is used in the first stage) at the k1th time (here, in order to distinguish the different time windows of the first and second stages, j1 is used in the first stage, j1=1); Y k is the Y-axis coordinate of the target object at the kth moment on the time axis, V represents the first Y-axis coordinate of the lth data in the j1th time window at the k1th moment of the target object; k is the target object's speed, Indicates the movement speed of the target object in the first time window at the first moment, Δt is the sampling time interval, θ k It is the angle between the target object's forward direction and the positive direction of the X axis. Indicates the angle between the forward direction of the first data of the target object in the first time window at the first moment and the positive direction of the X axis, φ k It is the angle between the target object's position and the positive direction of the X axis. represents the angle between the position of the lth data of the target object in the j1th time window at the k1th moment and the positive direction of the X axis (i.e. the first arrival angle mentioned above), α k The angle represented by Figure 3 shown.

[0072] At the same time, the numerical relationship between k, l and j follows the following:

[0073] k=l+(j-1)·L1 / 2 (3)

[0074] Radial velocity of the target object It can be expressed as:

[0075]

[0076] Therefore, for solving The nonlinear overdetermined equations can be expressed as:

[0077]

[0078] In the second stage (i.e. trajectory information update stage), the known initial information obtained in the previous step (i.e. ) to quickly process and estimate subsequent data. At the same time, in order to improve computational efficiency, the time window length L2 and the time window interval are increased. The impact of acceleration on the target object's motion trajectory is taken into account to improve the accuracy of the trajectory estimation result and its adaptability to complex trajectories. The coordinate position of the lth target in the jth time window is It can be expressed as:

[0079]

[0080] In the above formula (6), It represents the X-axis coordinate of the target object at the k2th moment (in order to distinguish the different moments of the first and second stages, k2 is used in the second stage) and the j2th moment (in order to distinguish the different time windows of the first and second stages, j2 is used in the second stage) in the time window. Represents the Y-axis coordinate of the lth data in the j2th time window of the target object at the k2th moment, Indicates the movement speed of the target object in the first data of the j2th time window at the k2th moment, a k is the acceleration of the target object, is the acceleration of the first data in the j2th time window of the target object at the k2th moment, It represents the angle between the forward direction of the first data in the j2th time window of the target object at the k2th moment and the positive direction of the X axis. It represents the angle between the position of the first data of the target object in the j2-th time window at the k2-th time and the positive direction of the X-axis (i.e., the second arrival angle mentioned above).

[0081] Similarly, we can use it to solve The nonlinear overdetermined equations can be expressed as:

[0082]

[0083] Solving complex, nonlinear, overdetermined equations using traditional analytical methods is extremely difficult and inefficient. To address this problem, the present embodiment uses the error in the target object's motion information as the optimization objective, and uses the motion and mathematical laws of the target object within a given sliding window as constraints to establish an optimization model for the positioning problem. This transforms the problem of solving the overdetermined equations into the problem of finding the minimum value of the optimization function.

[0084] Specifically, each optimization problem has a solution space whose dimension is equal to the number of decision variables of the given problem. Here, the matrix K is used to represent the overall solution vector of the algorithm, as shown in Equation (8).

[0085]

[0086] Where N is the number of components and D is the spatial dimension. N is a set of components of the solution vector, containing information about a set of decision variables. In the initial information estimation stage, K n =[X n ,V n ,θ n ]. In the trajectory update phase, K n =[X n ,V n ,θ n ,a n ].

[0087] The optimization goal of the algorithm is the error of motion information, and the ideal value of the parameter to be solved is x ) and estimated value (real x ) is expressed as the absolute value of the difference between them, that is, Fitness(x)=|idea x -real x |. Then, in the initial information estimation stage, the fitness equation for the target information at the kth moment (i.e., the first fitness equation) is solved as follows:

[0088]

[0089] Among them, in the initial information estimation stage, represents the first Y-axis coordinate mentioned above, represents the first X-axis coordinate mentioned above, represents the first arrival angle mentioned above, represents the first value mentioned above, represents the first movement speed mentioned above, represents the first angle mentioned above, represents the first radial velocity mentioned above, represents the second value mentioned above, Represents the first fitness equation above.

[0090] In step S204 of the above-mentioned method for determining the motion trajectory, the radial velocity and the angle of arrival of the target object are processed in stages to obtain a second fitness equation, including: in the second stage, obtaining multiple sub-fitness equations, wherein the multiple sub-fitness equations are conditional functions of the second fitness equation; and determining the second fitness function based on the multiple sub-fitness equations.

[0091] In the above steps, determining the second fitness function based on multiple sub-fitness equations includes: obtaining the second Y-axis coordinate of the lth data of the target object in the j2th time window at the k2th time, the second X-axis coordinate of the l2th data of the target object in the j2th time window at the k2th time, and the second angle of arrival between the position of the lth data of the target object in the j2th time window at the k2th time and the positive direction of the X-axis; determining the first sub-fitness equation based on the second Y-axis coordinate, the second X-axis coordinate, and the second angle of arrival; obtaining the second motion speed of the first data of the target object in the j2th time window at the k2th time, the second angle of the first data of the target object in the j2th time window at the k2th time, and the second radial velocity of the lth data of the target object in the j2th time window at the k2th time; and based on the second motion speed, the second angle of arrival, the second angle, and the second radial velocity, Determine the second sub-fitness equation; obtain the third motion velocity of the first data of the target object in the (j-1)2th time window at the k2th time, the sampling time interval of the echo signal, and the acceleration of the first data of the target object in the j2th time window at the k2th time; determine the third sub-fitness equation based on the second motion velocity, the third motion velocity, the sampling time interval, and the acceleration; obtain the third X-axis coordinate of the first data of the target object in the j2th time window at the k2th time, and the fourth X-axis coordinate of the first data of the target object in the (j-1)2th time window at the k2th time; determine the fourth sub-fitness equation based on the third X-axis coordinate, the second motion velocity, the second angle, the sampling time interval, the acceleration, and the fourth X-axis coordinate; determine the second fitness equation based on the first sub-fitness equation, the second sub-fitness equation, the third sub-fitness equation, and the fourth sub-fitness equation.

[0092] In some embodiments of the present application, in the trajectory update phase, the fitness equation for solving the target information at the k-th time point (i.e., the second fitness equation) can be expressed as:

[0093]

[0094] Among them, in the trajectory update stage, represents the second Y-axis coordinate mentioned above, represents the second X-axis coordinate mentioned above, represents the second arrival angle mentioned above, fitness1 represents the first sub-fitness equation mentioned above; represents the second movement speed mentioned above, represents the second angle mentioned above, represents the second radial velocity, fitness2 represents the second sub-fitness equation; represents the third motion speed, ΔT represents the sampling time interval, represents the above acceleration (i.e., the acceleration of the first data of the target object in the j2-th time window at the k2-th moment), and fitness3 represents the above third sub-fitness equation; represents the third X-axis coordinate mentioned above, represents the fourth X-axis coordinate, and fitness4 represents the fourth sub-fitness equation; Represents the second fitness equation above.

[0095] In step S206 of the above-mentioned method for determining the motion trajectory, the first objective optimization function is determined based on the first fitness equation, and the second objective optimization function is determined based on the second fitness equation, including: obtaining the first fitness equation corresponding to all time windows in the first stage and the first quantity of all data in the first stage; determining the first objective optimization function based on the first fitness equation and the first quantity corresponding to all time windows in the first stage; obtaining the second fitness equation corresponding to all time windows in the second stage and the second quantity of all data in the second stage; determining the second objective optimization function based on the second fitness equation and the second quantity corresponding to all time windows in the second stage.

[0096] According to the first fitness equation of formula (9) and the second fitness equation of formula (11), the first objective optimization function and the second objective optimization function are determined, wherein the first objective optimization function is shown in formula (12) and the second objective optimization function is shown in formula (13):

[0097]

[0098] In step S208 of the above-mentioned method for determining the motion trajectory, the first objective optimization function and the second objective optimization function are solved to obtain the motion trajectory of the target object, including: step 1: according to the first objective optimization function, the individual with the smallest fitness function value corresponding to all time windows in the first stage is determined as the first optimal individual in the current stage, wherein the first optimal individual represents the optimal solution of the first parameter in the corresponding time window of the first stage, and the first parameter includes the X-axis coordinate, the motion speed and the angle between the forward direction of the target object and the positive direction of the X-axis in the corresponding time window; step 2: based on the first optimal individual, the first fitness function value, the second fitness function value, the first number corresponding to the first parameter, and the second number of time windows in the first stage, the first optimal individual is updated, and steps 1 to 2 are repeated, wherein the first fitness function value and the second fitness function value are the fitness function values ​​corresponding to any two time windows in the first stage; step 3: when the first optimal individual reaches the preset accuracy or reaches the maximum number of iterations, the iteration is stopped to obtain the first target optimal individual, wherein the first target optimal individual represents the result obtained after the first objective optimization function is solved; step Step 4: Use the first target optimal individual as a reference for solving the second target optimization function, and according to the second target optimization function, determine the individual with the smallest fitness function value corresponding to all time windows in the second stage as the second optimal individual in the current stage, wherein the second optimal individual represents the optimal solution of the second parameter in the corresponding time window of the second stage, and the second parameter includes the X-axis coordinate, movement speed, angle between the forward direction of the target object and the positive direction of the X-axis, and acceleration of the target object in the corresponding time window; Step 5: Update the second optimal individual based on the second optimal individual, the third fitness function value, the fourth fitness function value, the third number corresponding to the multiple second parameters, and the fourth number of time windows in the second stage, and repeat steps 4 to 5, wherein the third fitness function value and the fourth fitness function value are the fitness function values ​​corresponding to any two time windows in the second stage; Step 6: Stop iteration when the second optimal individual reaches the preset accuracy or reaches the maximum number of iterations, and obtain the second target optimal individual, wherein the second target optimal individual represents the result obtained after solving the second target optimization function; determine the motion trajectory of the target object based on the second target optimal individual.

[0099] In the above-mentioned method for determining the motion trajectory, the method also includes: when the first optimal individual or the second optimal individual is less than or equal to the individual upper limit and greater than or equal to the individual lower limit, updating the first optimal individual or the second optimal individual based on the individual upper limit, the individual lower limit and the random number; when the first optimal individual is greater than the individual upper limit or less than the individual lower limit, not updating the first optimal individual or the second optimal individual.

[0100] In some embodiments of the present application, the target optimization function is solved based on an improved subtraction mean optimization algorithm. The main principle of the SABO algorithm is to update the position of the individuals in the population by utilizing the arithmetic mean of all individuals in the population, thereby improving the ability of the algorithm to jump out of the local optimum and achieving accurate solution of the optimization function. It provides a new idea for solving the optimization problem. However, the undifferentiated group update method in the traditional SABO algorithm will lead to a waste of computing resources in the optimization process, which is not conducive to the computational efficiency of the overall algorithm. In order to enhance the search capability of the algorithm, the embodiment of the present application combines the leader learning strategy to improve the population position update mechanism of the SABO algorithm. The formula is as follows:

[0101]

[0102] Among them, x best is the best individual in the current iteration round. is a D-dimensional vector whose components are random numbers generated between (2,3), and n represents the total number of individuals in the population. represents the position of the individual after displacement, x n and x m Respectively represent the first fitness function value and the second fitness function value mentioned above.

[0103] In formula (14), the embodiment of the present application takes the starting point x of the original population individual as n Replaced with x best , using the information of the best individuals in the population to improve the global search capability. At the same time, on this basis, increase the random factor The weight of is used to ensure the development capability of individuals in the exploration stage and avoid falling into local optimality.

[0104] In addition, the traditional SABO algorithm does not process individuals that exceed the threshold range, which will affect the accuracy and efficiency of the calculation results when processing complex optimization functions. Therefore, the embodiment of the present application adds a step of resetting individuals that exceed the threshold range after the position update step, as shown below:

[0105]

[0106] Among them, r represents a random number, lb d Represents the individual lower limit, ub d Represents the individual upper limit.

[0107] The single-frequency Doppler radar target positioning algorithm based on the improved SABO method is as follows:

[0108] 1) Initialize ISABO parameters: population size N, maximum number of iterations, population dimension D, individual positions in the population (decision variables), upper and lower limits of the decision variables (i.e., individual upper and lower limits);

[0109] 2) Calculate the fitness function value corresponding to each individual in the population according to formula (12), where the individual with the smallest fitness function value is the first optimal individual in the current stage;

[0110] 3) Calculate the position of the individual after displacement according to formula (14);

[0111] 4) Update the population individuals according to formula (15) and reset the individuals that exceed the threshold range. Keep the optimal individuals and corresponding parameter values;

[0112] 5) Determine whether to terminate the loop. When the number of iterations reaches the maximum or the parameter value corresponding to the first target optimal individual reaches the preset accuracy, end the ISABO calculation process for this round and output the optimal individual and the corresponding parameter value. Otherwise, return to step 2);

[0113] 6) Based on the output of round j (j=1), the result is used as a reference to input into the j+1th round of ISABO calculation (trajectory information update phase). j=j+1, update the ISABO parameter settings: population size, maximum number of iterations, and population dimension. The upper and lower limits of the individual positions of the population are set to the adjacent area range of the previous round results.

[0114] 7) Replace the target fitness function in step 2) from equation (12) to equation (13). Repeat steps 2) to 5) until the global optimal individual and its corresponding parameter values ​​are obtained;

[0115] 8) Record the output results. Repeat steps 6) to 7) until all available data are calculated, and finally obtain the motion trajectory of the target object.

[0116] In order to verify the performance of the method proposed in the embodiment of the present application, verification was carried out in an 8m×8m experimental test scenario, such as Figure 4 As shown, the Doppler radar device used is as follows Figure 5 The radar parameter settings are shown in Table 1.

[0117] Table 1 Radar system parameter settings

[0118]

[0119]

[0120] The radar system used in the experiment is improved based on the traditional compact dual-frequency continuous wave radar system. Therefore, one transmission carrier frequency is reduced on the basis of the original single-transmitter dual-receiver system. The radar system collects the echo signal every 0.005 seconds and sends the signal to the terminal for processing. During the experimental data acquisition process, the human target moves according to the tag on the ground to achieve the effect of moving approximately along the trajectory. In the experiment, the human target moves along the path at a constant step frequency of 2Hz. At the same time, the embodiment of the present application uses the traditional short-time Fourier transform (STFT) method, the Bezier-Based Hough Transforms (Bezier-Hough) method based on the Bessel model, and the single-frequency positioning method based on the SABO algorithm as comparison algorithms to carry out tests together, and compares and analyzes the performance differences between them and the algorithms proposed in the embodiment of the present application.

[0121] The improved optimization method proposed in this application demonstrates excellent convergence performance when solving high-dimensional multimodal optimization functions. It not only performs well in single-target scenarios but also effectively estimates human motion trajectories in multi-target environments. Compared to traditional comparison methods, the proposed method achieves superior positioning performance at a lower system cost, making it more suitable for the current trend of miniaturization, low cost, and low power consumption of radar systems.

[0122] Figure 6 is a structural diagram of a motion trajectory determination device according to an embodiment of the present application, such as Figure 6 As shown, the device includes:

[0123] An acquisition module 40 is configured to acquire an echo signal of a target object collected by a radar system and perform time-frequency analysis on the echo signal to obtain a radial velocity and an angle of arrival of the target object.

[0124] a processing module 42 configured to process the radial velocity and the angle of arrival of the target object in stages to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent the absolute value of the error between the ideal value and the estimated value of the motion state of the target object in the first stage, and the second fitness equation is used to represent the absolute value of the error between the ideal value and the estimated value of the motion state of the target object in the second stage, the second stage being subsequent to the first stage;

[0125] a determination module 44, configured to determine a first objective optimization function according to the first fitness equation, and to determine a second objective optimization function according to the second fitness equation;

[0126] The solving module 46 is used to solve the first objective optimization function and the second objective optimization function to obtain the motion trajectory of the target object.

[0127] It should be noted that Figure 6 The motion trajectory determination device shown is used to perform Figure 2 The method for determining the motion trajectory shown in the figure, therefore, the relevant explanations in the above method for determining the motion trajectory are also applicable to the device for determining the motion trajectory, and will not be repeated here.

[0128] An embodiment of the present application further provides an electronic device, which includes a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the method for determining the above-mentioned motion trajectory.

[0129] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned method for determining the motion trajectory by running the computer program.

[0130] An embodiment of the present application further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method for determining the motion trajectory in each embodiment of the present application.

[0131] The embodiments of the present application further provide a computer program, which, when executed by a processor, implements the steps of the method for determining the motion trajectory in each embodiment of the present application.

[0132] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0133] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0136] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0138] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a motion trajectory, characterized in that: include: Acquire an echo signal of a target object collected by a radar system, and perform time-frequency analysis on the echo signal to obtain a radial velocity and an angle of arrival of the target object; Processing the radial velocity and the angle of arrival of the target object in stages to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent an absolute value of an error between an ideal value and an estimated value of a motion state of the target object in the first stage, and the second fitness equation is used to represent an absolute value of an error between an ideal value and an estimated value of a motion state of the target object in the second stage, the second stage being after the first stage; Determining a first objective optimization function according to the first fitness equation, and determining a second objective optimization function according to the second fitness equation; Solving the first objective optimization function and the second objective optimization function to obtain a motion trajectory of the target object.

2. The method according to claim 1, characterized in that The radial velocity and the angle of arrival of the target object are processed in stages to obtain a first fitness equation, including: In the first stage, the first Y-axis coordinate of the lth data of the target object at the k1th time in the j1th time window, the first X-axis coordinate of the lth data of the target object at the k1th time in the j1th time window, and the first arrival angle between the position of the lth data of the target object in the k1th time window and the positive direction of the X-axis are obtained, where k, j, and l are positive integers; Determining a first value based on the first Y-axis coordinate, the first X-axis coordinate, and the first angle of arrival; Obtain the first motion speed of the first data of the target object in the j1-th time window at the k1-th time, the first angle of the first data of the target object in the j1-th time window at the k1-th time, and the first radial speed of the l-th data of the target object in the j1-th time window at the k1-th time; determining a second value according to the first motion speed, the first angle of arrival, the first angle, and the first radial speed; The first fitness equation is determined according to the first value and the second value.

3. The method according to claim 2, characterized in that The radial velocity and the angle of arrival of the target object are processed in stages to obtain a second fitness equation, including: In the second stage, a plurality of sub-fitness equations are obtained, wherein the plurality of sub-fitness equations are conditional functions of the second fitness equation; The second fitness function is determined according to the multiple sub-fitness equations.

4. The method according to claim 3, characterized in that Determining the second fitness function according to the multiple sub-fitness equations includes: Obtain the second Y-axis coordinate of the lth data of the target object in the j2th time window at the k2th time, the second X-axis coordinate of the l2th data of the target object in the j2th time window at the k2th time, and the second angle of arrival between the position of the lth data of the target object in the j2th time window at the k2th time and the positive direction of the X-axis; Determining a first sub-fitness equation according to the second Y-axis coordinate, the second X-axis coordinate, and the second angle of arrival; Obtain the second motion speed of the first data of the target object in the j2-th time window at the k2-th time, the second angle of the first data of the target object in the j2-th time window at the k2-th time, and the second radial speed of the l-th data of the target object in the j2-th time window at the k2-th time; determining a second sub-fitness equation according to the second motion speed, the second angle of arrival, the second angle, and the second radial speed; Obtaining the third motion speed of the first data of the target object in the (j-1)2th time window at the k2th time, the sampling time interval of the echo signal, and the acceleration of the first data of the target object in the j2th time window at the k2th time; determining a third sub-fitness equation according to the second movement speed, the third movement speed, the sampling time interval, and the acceleration; Obtain the third X-axis coordinate of the first data of the target object in the j2-th time window at the k2-th time, and the fourth X-axis coordinate of the first data of the target object in the (j-1)2-th time window at the k2-th time; Determining a fourth sub-fitness equation based on the third X-axis coordinate, the second motion speed, the second angle, the sampling time interval, the acceleration, and the fourth X-axis coordinate; The second fitness equation is determined according to the first sub-fitness equation, the second sub-fitness equation, the third sub-fitness equation, and the fourth sub-fitness equation.

5. The method according to claim 1, wherein Determining a first objective optimization function according to the first fitness equation, and determining a second objective optimization function according to the second fitness equation, including: Obtaining a first fitness equation corresponding to all time windows in the first stage and a first quantity of all data in the first stage; Determining the first objective optimization function according to the first fitness equation corresponding to all time windows in the first stage and the first quantity; Obtaining a second fitness equation corresponding to all time windows in the second stage and a second quantity of all data in the second stage; Determine the second objective optimization function based on the second fitness equation corresponding to all time windows in the second stage and the second quantity.

6. The method according to claim 5, characterized in that Solving the first objective optimization function and the second objective optimization function to obtain the motion trajectory of the target object includes: Step 1: According to the first objective optimization function, the individual with the smallest fitness function value corresponding to all time windows in the first stage is determined as the first optimal individual in the current stage, wherein the first optimal individual represents the optimal solution of the first parameter in the corresponding time window of the first stage, and the first parameter includes the X-axis coordinate of the target object in the corresponding time window, the movement speed, and the angle between the forward direction of the target object and the positive direction of the X-axis; Step 2: Update the first optimal individual based on the first optimal individual, the first fitness function value, the second fitness function value, the first number corresponding to the first parameter, and the second number of time windows in the first stage, and repeat steps 1 to 2, wherein the first fitness function value and the second fitness function value are the fitness function values ​​corresponding to any two time windows in the first stage; Step 3: When the first optimal individual reaches a preset accuracy or reaches a maximum number of iterations, the iteration is stopped to obtain a first target optimal individual, wherein the first target optimal individual represents the result obtained after solving the first target optimization function; Step 4: Use the first target optimal individual as a reference for solving the second target optimization function, and according to the second target optimization function, determine the individual with the smallest fitness function value corresponding to all time windows in the second stage as the second optimal individual in the current stage, wherein the second optimal individual represents the optimal solution of the second parameter in the corresponding time window of the second stage, and the second parameter includes the X-axis coordinate of the target object in the corresponding time window, the movement speed, the angle between the forward direction of the target object and the positive direction of the X-axis, and the acceleration of the target object; Step 5: Update the second optimal individual according to the second optimal individual, the third fitness function value, the fourth fitness function value, the third number corresponding to the second parameter of multiple orders, and the fourth number of time windows in the second stage, and repeat steps 4 to 5, wherein the third fitness function value and the fourth fitness function value are the fitness function values ​​corresponding to any two time windows in the second stage; Step 6: When the second optimal individual reaches a preset accuracy or reaches a maximum number of iterations, the iteration is stopped to obtain a second target optimal individual, wherein the second target optimal individual represents the result obtained after solving the second target optimization function; The motion trajectory of the target object is determined according to the second target optimal individual.

7. The method according to claim 6, characterized in that The method further comprises: When the first optimal individual or the second optimal individual is less than or equal to the individual upper limit and greater than or equal to the individual lower limit, updating the first optimal individual or the second optimal individual according to the individual upper limit, the individual lower limit and the random number; In the case where the first optimal individual is greater than the individual upper limit or less than the individual lower limit, the first optimal individual or the second optimal individual is not updated.

8. A device for determining a motion trajectory, characterized in that: include: An acquisition module is used to acquire an echo signal of a target object collected by a radar system, and perform time-frequency analysis on the echo signal to obtain a radial velocity and an angle of arrival of the target object; a processing module, configured to process the radial velocity and the angle of arrival of the target object in stages to obtain a first fitness equation and a second fitness equation, wherein the first fitness equation is used to represent an absolute value of an error between an ideal value and an estimated value of a motion state of the target object in the first stage, and the second fitness equation is used to represent an absolute value of an error between an ideal value and an estimated value of a motion state of the target object in the second stage, the second stage being after the first stage; a determination module, configured to determine a first objective optimization function according to the first fitness equation, and determine a second objective optimization function according to the second fitness equation; A solution module is used to solve the first target optimization function and the second target optimization function to obtain the motion trajectory of the target object.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the method for determining the motion trajectory according to any one of claims 1 to 7.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the method for determining the motion trajectory according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the method for determining the motion trajectory according to any one of claims 1 to 7 is implemented.

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