A method for tracking and locating the position of a vehicle

Abstract

A method for azimuth tracking and localization of a vehicle is disclosed. A base station is deployed with an acoustic array comprising M array elements. These elements acquire acoustic signals from the vehicle. The method includes: determining the initial azimuth of the target vehicle; initializing the initial states and weights of all particles, where each particle is a set of random samples representing the motion trajectory of the target vehicle, including velocity and spatial position trajectories; performing broadband beamforming based on the acoustic signals received by the array elements to determine the current direction finding output; updating the particle weights and candidate azimuth trajectory weights of the target vehicle based on the current direction finding output; wherein the candidate azimuth trajectories are determined according to the particle motion states; and determining the current azimuth and motion state of the target vehicle based on the particle weights and candidate azimuth trajectory weights. This method enables azimuth tracking and localization of a single target vehicle using a sonar system, providing target location information for relevant passive acoustic monitoring.

Description

Technical Field

[0001] This invention relates to the field of marine acoustics technology, and in particular to a method for azimuth tracking and positioning of a vehicle. Background Technology

[0002] Some unauthorized vehicles (including surface and underwater vehicles) pose a serious threat to the stability of important ports, military bases, and major waterways, and even bring serious security risks. To address this problem, sonar detection is one of the most effective means. Passive sonar systems most directly present target bearing information, but cannot directly provide target distance information; therefore, bearing tracking is their usual operating mode. This one-dimensional operating mode makes tracking weak targets more challenging. Many passive sonar localization methods adopt the classic "detect-track-localize" paradigm. With the development of vibration reduction and noise reduction technologies, sonar detection and tracking of quiet targets is becoming increasingly difficult, and the localization range of real-time systems is correspondingly decreasing. In the past 30-40 years, the radiated noise of some underwater vehicles (such as submarines) has decreased year by year, thus reducing the detection range. Therefore, a new method for vehicle bearing tracking and localization is urgently needed. Summary of the Invention

[0003] To address the problems existing in the prior art, embodiments of this application provide a method for azimuth tracking and positioning of a vehicle, a computing device, a computer storage medium, and a product containing a computer program, which can realize the azimuth tracking and positioning of a single target vehicle by a sonar system, and provide long-term stable target position information for relevant acoustic passive monitoring.

[0004] In a first aspect, embodiments of this application provide a method for azimuth tracking and positioning of a vehicle. A base station is deployed with an acoustic array, which includes M array elements, where M is greater than 1. The array elements are used to acquire acoustic signals from the vehicle. The method includes: determining the initial azimuth of the target vehicle; initializing the initial state and weights of all particles, where each particle is a set of random samples representing the motion trajectory of the target vehicle, including its velocity trajectory and spatial position trajectory; performing broadband beamforming based on the acoustic signals received by the array elements to determine the current direction finding output result; updating the particle weights and the candidate azimuth trajectory weights of the target vehicle based on the current direction finding output result; wherein the candidate azimuth trajectory is determined according to the motion state of the particles; and determining the current azimuth and current motion state of the target vehicle based on the particle weights and the candidate azimuth trajectory weights, and updating the candidate azimuth trajectory.

[0005] In some possible implementations, the initial state of all particles is initialized according to the following formula.

[0006]

[0007] In the formula, Characterizing the distance from the particle to the receiving array element, Characterizing the absolute velocity of the particle towards the receiving array element. Characterizing the heading angle of the particle, where, and It follows a uniform distribution.

[0008] In some possible implementations, determining the current direction finding output includes: dividing the acoustic signal received by each array element into multiple data frames, performing a Fourier transform on each data frame to obtain a frequency domain signal; determining the covariance matrix based on the frequency domain signal; and determining the current direction finding output based on the reference array element and the covariance matrix, wherein the reference array element is any one of the M array elements.

[0009] In some possible implementations, the covariance matrix is ​​determined by calculating the following formula.

[0010]

[0011] In the formula, K represents the number of superimposed beats. p represents the complex conjugate transpose of a frequency domain signal. k (ω) represents the frequency domain signal, and p k (ω)=[p1(ω),p2(ω),...,p M (ω)] T , where ω represents frequency, (·) T The transpose is represented by k, which represents the snapshot number.

[0012] In some possible implementations, the direction-finding output is calculated according to the following formula.

[0013]

[0014] In the formula, B represents the direction-finding output result, w represents the weighted vector of the array at different frequencies and directions relative to the reference array elements, w H The complex conjugate transpose of w represents the array, and α represents the array scanning angle; where w(ω,α)=[w1(ω,α),w2(ω,α),...,w M (ω,α)] T For each weighted vector w m (ω,α) all have In the formula, i represents the imaginary unit, τ m (α) represents the time delay of the m-th array element relative to the reference array element.

[0015] In some possible implementations, updating the weights of particles and the weights of candidate azimuth trajectories of the target vehicle includes: determining prior information of candidate azimuth trajectories; determining posterior information of current azimuth-related variables; and determining the weights of candidate azimuth trajectories of the target vehicle based on the prior information of candidate azimuth trajectories and the posterior information of current azimuth-related variables.

[0016] In some possible implementations, the prior information of the candidate orientation trajectory is calculated according to the following formula.

[0017]

[0018] In the formula, a \T The sequence of correlation variables between the target vehicle and the observed position, excluding the correlation variables at time T, is represented by p, which represents the prior probability of the candidate azimuth trajectory. Characterizing the initial state of a particle, z 1:T The characterization comprises a time series of all observations; where p satisfies

[0019]

[0020] In the formula, The average correlation coefficient F, which characterizes the historical observation features of the particle's trajectory, t F represents the sound field characteristics at time t. t' Characterizing the sound field features at time t' Characterizing the observations generated by the target vehicle at time t, The observations generated by the target vehicle at time t' are represented by θ0, which represents the initial bearing of the target vehicle. The first derivative of θ0 is represented by θ0, T is represented by the length of the time series, and σ is represented by the standard deviation of the measurement noise.

[0021] In some possible implementations, the posterior information of the current orientation-related variable is calculated according to the following formula.

[0022]

[0023] In the formula, a T The associated variables characterizing time T.

[0024] In some possible implementations, the candidate azimuth trajectory weights of the target vehicle are determined and calculated according to the following formula.

[0025]

[0026] In the formula, z \T The observations representing the target vehicle excluding time T.

[0027] Secondly, embodiments of this application provide a computer-readable storage medium including computer-readable instructions that, when read and executed by a computer, cause the computer to perform the method described in any of the first aspects.

[0028] Thirdly, embodiments of this application provide a computing device, including a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, perform the method as described in any of the first aspects.

[0029] Fourthly, embodiments of this application provide a product comprising a computer program that, when the computer program product is run on a processor, causes the processor to perform the method as described in any of the first aspects. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic flowchart of a vehicle orientation tracking and positioning method provided in an embodiment of this application;

[0032] Figure 2 This is a seawater sound velocity profile provided in an embodiment of this application;

[0033] Figure 3 This is the orientation tracking result provided in the embodiments of this application under the condition that the trajectory of the vehicle intersects during operation under the current seawater sound speed;

[0034] Figure 4 This is a schematic diagram illustrating the result of estimating the position of a target with intersecting trajectories, provided in an embodiment of this application.

[0035] Figure 5 This is a comparison diagram of the bearing tracking results and the actual trajectory of the aircraft provided in the embodiments of this application;

[0036] Figure 6 This is a comparison diagram of the vehicle position estimation result and the actual position provided in the embodiments of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0039] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0040] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0041] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0042] To facilitate understanding of the embodiments of this application, the following will provide further explanation and description with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0043] First, the technical terms involved in the embodiments of this application will be introduced:

[0044] 1. Direction finding refers to the description of the azimuth of a target in the field of underwater acoustics.

[0045] 2. The probability density of a state sequence refers to the probability that a system is in a specific state at a specific point in time during a random process.

[0046] 3. The probability density of state transitions, also known as the state transition rate or state transition probability matrix (for discrete state spaces), describes the probability of a stochastic process transitioning from one state to another. The state transition probability density is a dynamic description of the probability density of a state sequence. It focuses not only on the state of the system at a specific point in time, but also on how the system changes from one state to another.

[0047] Next, the technical solutions provided in the embodiments of this application will be introduced.

[0048] When tracking and locating the position of a vehicle, the target vehicle's trajectory can provide information for estimating its current motion state. Therefore, various multi-target tracking algorithms have been developed based on different model assumptions. These methods can be broadly classified into two categories: vector-based methods, such as probabilistic data association, joint probabilistic data association, multi-hypothesis tracking, and probabilistic multi-hypothesis tracking; and set-based methods, such as probability hypothesis density, cardinal probability hypothesis density, multi-Bernoulli filtering, and tag random finite sets. However, the problem of data association of measured targets is a key challenge in multi-target tracking. This means that if the above methods are deployed in a purely azimuth-based passive sonar system, frequent operator intervention is required, directly leading to engineering challenges in reliable multi-target azimuth tracking. Ensuring the reliability of single-target azimuth tracking and appropriately reducing operator intervention are also of practical significance. The difficulty in measuring target association during multi-target tracking is a consequence of the classic detection-tracking-localization paradigm. An alternative approach is pre-detection tracking, which requires operators to execute target generation / death commands. Compared to direction of arrival (DOA) estimation, DOA tracking based on raw data considers the strong correlation of azimuth trajectory information in adjacent time intervals, resulting in more robust and accurate results, but it also makes the tracking process very complex. Based on the target's azimuth trajectory after DOA tracking, the target's motion state can be estimated using pure azimuth target motion analysis, but this requires the observation platform to perform effective maneuvers. Several maneuvers by the observation platform may be ineffective because no new observations are provided. An effective maneuver strategy can improve the stability and accuracy of the observation results; therefore, a reasonable maneuver strategy needs to be designed, but this also depends on the prediction of the target's state.

[0049] However, for stationary or uniformly moving platform equipment, it is difficult to accurately measure the target's motion state by relying solely on orientation tracking and target motion analysis.

[0050] In view of this, embodiments of this application provide a method for azimuth tracking and positioning of a vehicle. Based on motion analysis, a positioning and tracking method combining an acoustic field model is developed, which can realize the azimuth tracking and positioning of a single target vehicle by a sonar system, and provide long-term stable target position information for relevant acoustic passive monitoring.

[0051] For example, Figure 1 This illustration shows a method for azimuth tracking and positioning of a vehicle according to an embodiment of this application. The base station is equipped with an acoustic array, which includes M array elements, where M is greater than 1. The acoustic array can be an underwater acoustic transducer array, etc. Figure 1 As shown, the vehicle's orientation tracking and positioning method includes the following steps:

[0052] S11: Determine the initial orientation of the target vehicle, initialize the initial state and weight of all particles, wherein the particles are a set of random samples representing the motion trajectory of the target vehicle, and the motion trajectory includes the velocity trajectory and spatial position trajectory of the target vehicle.

[0053] In this embodiment, the initial bearing of the target vehicle is obtained through an acoustic array, and the direction-finding output of the target vehicle is determined. Based on the direction-finding output of the target vehicle, the operator issues a target generation command to the computing device to determine the initial values ​​for bearing tracking of the target vehicle. The computing device characterizes the trajectory of the target vehicle through random samples (i.e., particles), including the target vehicle's velocity and its spatial position.

[0054] Specifically, the tracking bearing of the target vehicle is denoted as θ, and θ is... t =tan -1 (x x,t / x y,t )+v t In the formula, v t The observation noise follows a Gaussian distribution, and t represents time. Its initial orientation can then be represented as θ0. The initial states and weights of all particles are set.

[0055] The initial state of the particle is represented as:

[0056]

[0057] In the formula, Characterizing the distance from the particle to the receiving array element, Characterizing the absolute velocity of the particle towards the receiving array element. Characterizing the heading angle of a particle. Among them, and It follows a uniform distribution.

[0058] The weight of a particle is expressed as

[0059]

[0060] In the formula, I represents the total number of particles.

[0061] S12: Based on the acoustic signals received by the array elements, perform broadband beamforming to determine the current direction finding output result.

[0062] In this embodiment, the acoustic signal received by each element in the array can be divided into multiple data frames, and a Fourier transform can be performed on each data frame to obtain the frequency domain signal, denoted as p. k (ω), k represents the sequence number of the snapshot, and p k (ω)=[p1(ω),p2(ω),...,p M (ω)] T In the formula, ω represents the frequency, (·) T The transpose is represented. Based on the frequency domain signal, the covariance matrix in different frequency domains is determined and denoted as R(ω). Based on the covariance matrix and the reference array element, the minimum variance distortionless response (MVDR) direction-finding output of the array at different frequency points is determined. The reference array element can be any element in the acoustic array.

[0063] In some possible embodiments, the covariance matrix can be calculated according to the following formula.

[0064]

[0065] In the formula, K represents the number of superimposed beats. Characterizes the complex conjugate transpose of a frequency domain signal.

[0066] In some possible embodiments, the direction-finding output can be calculated according to the following formula.

[0067]

[0068] In the formula, B represents the direction-finding output result, w represents the weighted vector of the array at different frequencies and directions relative to the reference array elements, w H The complex conjugate transpose of w is represented by α, and the array scanning angle is represented by α.

[0069] Among them, w(ω,α)=[w1(ω,α),w2(ω,α),...,w M (ω,α)] T For each weighted vector w m (ω,α) all have In the formula, τ m (α) represents the time delay of the m-th array element relative to the reference array element. The formula for calculating this time delay is as follows:

[0070]

[0071] In the formula, c ref Let v represent the reference speed of sound, v represent the unit vector of the incident signal, and v(α) = -[cosα,sinα].T r m The two-dimensional coordinates of the array element are represented, and r is given. m =[r xm ,r ym ] T r xm For r m x-axis coordinate, r ym For r m The y-axis coordinate.

[0072] S13: Update the weights of the particles.

[0073] In this embodiment, a proposal distribution can be constructed based on the state transition equation to sample the importance of particles. During sampling, the probability density of particle state transitions is characterized by the state transition equation. Based on this probability density, the particle weights are updated.

[0074] Specifically, for particle i, its importance can be expressed by the following formula:

[0075]

[0076] The target state transition equation is x t =F t|t-1 x t-1 +Gw t In the formula, F t|t-1 The state transition matrix is ​​represented by G, which represents the noise driving force of the uniformly moving target, and w t The noise is characterized by a Gaussian distribution. Based on this target state transition equation, the probability density of the state transition can be calculated, and its formula is as follows:

[0077]

[0078] In the formula, x t Let x0 represent the target state at time t, x0 represent the target state at the current initial time, C represent the normalization constant, t represent time t in the time series, and v represent the target state at time t. x,0 The velocity v of the target along the x-axis at the initial moment is represented by the following value. y,0 The initial velocity of the target along the y-axis is represented by x0, and the initial x-coordinate of the target is represented by x. t Let y0 represent the target's x-axis coordinate at time t, and y0 represent the target's y-axis coordinate at the initial time. t The y-axis coordinate of the target at time t, v x,t The velocity v of the target along the x-axis at time t is represented by v. y,t σ represents the velocity of the target along the y-axis at time t. v The standard deviation characterizing one-dimensional velocity noise.

[0079] When updating particle weights, the following formula can be used for calculation.

[0080]

[0081] In the formula, The likelihood function characterizes the probability that an observation can be generated from a particle's state. a represents the observations generated by the target at time t. t The associated variables represent the relationships between the target vehicle and multiple observations at time t. The likelihood function satisfies...

[0082]

[0083] In the formula, sigmoid{B(θ)} represents the probability of the existence of a target based on the beam output energy, and beta{ρ(F(θ),F(x)}} represents the probability of the target existing. t The sound field characteristics F(θ) representing any orientation and the sound field characteristics F(x) copied to the target location. t The correlation coefficient ρ follows a beta distribution, the observed azimuth follows a Gaussian distribution with the actual azimuth of the target as the mean, and σ represents the standard deviation of the measurement noise.

[0084] In some possible implementations, the particle weights can also be renormalized. Specifically, normalization can be performed according to the following formula.

[0085]

[0086] S14: Update the candidate orientation trajectory weights of the target vehicle.

[0087] In this embodiment, prior information of candidate azimuth trajectories and posterior information of the current azimuth-related variables can be determined. The target azimuth trajectory weight is then updated based on the prior information of the candidate azimuth trajectories and the posterior information of the current associated azimuth. Specifically, the prior information of the candidate azimuth trajectories can be calculated using the following formula.

[0088]

[0089] In the formula, a \T The sequence of correlation variables between the target vehicle and the observed position, excluding the correlation variables at time T, is represented by p, which represents the prior probability of the candidate azimuth trajectory, and z. 1:T The representation includes a time series containing all observations. Where p satisfies...

[0090]

[0091] In the formula, The average correlation coefficient (i.e., the average value of the correlation coefficient) characterizes the historical observation characteristics of the particle's trajectory. F represents the first derivative of θ0.t F represents the sound field characteristics at time t. t' Characterizing the sound field features at time t' Characterizing the observations generated by the target vehicle at time t, Characterize the observations generated by the target vehicle at time t'.

[0092] The posterior information of the preceding associated variable is represented as follows:

[0093]

[0094] In the formula, a T The associated variables characterizing time T.

[0095] The weight of the current candidate azimuth trajectory can then be calculated as follows:

[0096]

[0097] In the formula, z \T The observations representing the target vehicle excluding time T.

[0098] In some possible implementations, the weights of the azimuth trajectory can be normalized. Specifically, normalization can be calculated according to the following formula.

[0099]

[0100] In the formula, J represents the number of azimuth trajectories.

[0101] S15: Based on the particle weights and the candidate orientation trajectory weights of the target vehicle, determine the current orientation and current motion state of the target vehicle, and update the candidate orientation trajectory.

[0102] In this embodiment, the target's current orientation is calculated using maximum a posteriori probability estimation based on the weights of each particle and each candidate orientation trajectory. The target's motion state parameters at any given time are estimated using minimum mean square error based on the target's orientation trajectory and the received sound field information. The latest associated variables for each candidate orientation trajectory are updated, and the disappearance, generation, and overlap of candidate trajectories are determined.

[0103] Specifically, the current bearing of the target vehicle can be calculated using the maximum a posteriori probability estimation according to the following formula.

[0104]

[0105] In the formula, a T The variables that characterize the correlation between the target and the observed data at time T.

[0106] The minimum mean square error can be used to estimate the target's motion state at any given time using the following formula.

[0107]

[0108] The update formula for the current orientation of the candidate orientation trajectory can be expressed as:

[0109]

[0110] In the formula, The associated variables characterize the j-th candidate trajectory at time T.

[0111] In some possible embodiments, the disappearance, generation, and overlap of candidate trajectories are determined, including:

[0112] When the prior information of the candidate azimuth trajectory is less than the threshold or the estimated current azimuth... When the current trajectory is located at the boundary of the search area, it is added to the set to be eliminated. If the waiting time for a candidate trajectory to disappear exceeds T1, it is considered eliminated; if the repetition time of a candidate trajectory exceeds T2, it is considered overlapping, and eliminated and overlapping trajectories are deleted. Trajectories with a high probability of containing a target and not associated with it are added to the set to be generated. When the waiting time for a trajectory to be generated exceeds T3, the trajectory is considered to be officially generated. The specific times T1, T2, and T3 can be adjusted according to actual needs.

[0113] The above describes the underwater vehicle orientation tracking and positioning method provided in this application. Based on the target generation command, an initial position is determined. Then, a joint posterior probability density function is constructed by combining the target motion model and the physical field model, overcoming the difficulties of the physical field model being easily affected by environmental mismatch and the target motion analysis being constrained by maneuverability. When there is strong interference with azimuth intersection, weak targets are tracked based on the predicted information of the target motion state and the candidate azimuth trajectory information to ensure that the target can be automatically corrected after deviation. The posterior probability is approximated by sequential importance sampling to solve for the target positioning and tracking results. This method can realize the orientation tracking and positioning of a single target vehicle by a sonar system, providing long-term stable target position information for related acoustic passive monitoring.

[0114] To verify the reliability of this method, it has been validated; please refer to [link / reference]. Figures 2-6In a simulation data verification, 10 vehicles were selected for operation. An acoustic array consisting of 100 elements was deployed on the seabed. The initial positions of the 10 targets were randomly distributed within a 30km radius of the array center, with target heading angles randomly distributed between 0 and 360°, and sailing speeds uniformly distributed between 0 and 10 m / s. The sailing time was 1800 s. Each target had 5-10 line spectrum features, with the line spectrum source level 3-6 dB higher than the continuous spectrum source level. The continuous spectrum range was 30-150 Hz, with a continuous spectrum source level of 100-130 dB, and a noise spectrum level of 80 dB. The search distance range was 0-40 km, and the speed range was -15 to 15 m / s. Figure 2 A seawater sound velocity profile is shown during the verification of this method. Figure 3 This shows the position tracking results for 10 vehicles operating under current sea surface sound speeds with overlapping trajectories. For example... Figure 3 As shown, the trajectories of seven vehicles intersect, and different colors are used to represent the trajectories of the vehicles. Figure 4 A schematic diagram showing the results of position estimation for seven targets using this method is presented. Figure 4 As shown, dots of different colors represent the estimated positions of the seven vehicles at different times, and the corresponding straight lines represent the actual trajectories of the vehicles. It can be seen that this method has strong reliability. However, when the azimuths of weak and strong targets overlap for a long period, such as... Figure 4 For target 3, the influence of the strong target is still unavoidable during distance estimation, causing the distance of the weak target to gradually shift towards that of the strong target. However, when the targets separate again, this method can still automatically and correctly track the weak target. These results further verify the feasibility and reliability of this invention. Data from a certain sea trial was processed... Figure 5 A comparison chart showing the orientation tracking results of the aircraft with its actual trajectory is displayed. For example... Figure 5 As shown, red represents the tracking azimuth, and blue represents the azimuth determined by the Automatic Identification System (AIS). The analysis frequency band is the same as the simulation. Figure 6 A comparison diagram showing the vehicle's estimated position and its actual position is displayed. For example... Figure 6 As shown, the red dots represent the estimated position of the aircraft, and the black lines represent the actual position of the aircraft obtained through AIS.

[0115] It is understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. Furthermore, in some possible implementations, each step in the above embodiments may be selectively executed according to actual circumstances; it may be partially or fully executed, without limitation here. All or part of any feature of any embodiment of this application can be freely and arbitrarily combined without contradiction. The combined technical solutions are also within the scope of this application.

[0116] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0117] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0118] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0119] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0120] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0121] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application.

Claims

1. A method of position location by bearing tracking of a vehicle, characterized by, The base station is provided with an acoustic array, the acoustic array comprises array elements, greater than 1, the array elements are used to acquire acoustic signals of the vehicle, and the method comprises: Determine the initial orientation of the target vehicle, initialize the initial state and weight of all particles, where each particle is a set of random samples representing the motion trajectory of the target vehicle, which includes the velocity trajectory and spatial position trajectory of the target vehicle. Based on the acoustic signals received by the array elements, broadband beamforming is performed to determine the current direction finding output result; Based on the current direction finding output, update the weights of the particles and the candidate azimuth trajectory weights of the target vehicle; wherein, the candidate azimuth trajectory is determined according to the motion state of the particles; updating the weights of the particles and the candidate azimuth trajectory weights of the target vehicle includes: determining the prior information of the candidate azimuth trajectory, and calculating it according to the following formula. In the formula, The sequence of correlation variables between the target vehicle and the observation position, excluding the correlation variables at time T. Characterize the prior probability of candidate orientation trajectories. Characterizing the initial state of a particle, The characterization comprises a time series of all observations; where, satisfy In the formula, The average correlation coefficient characterizing the historical observation features of the particle's trajectory. Characterizing the sound field features at time t, Characterizing the sound field features at time t' Characterizing the observations generated by the target vehicle at time t, Characterize the observations generated by the target vehicle at time t'. Indicates the initial bearing of the target vehicle. Characterization The first derivative, Characterizing the length of a time series, The standard deviation characterizing measurement noise; Determine the posterior information of the current orientation-related variables, and calculate it according to the following formula. In the formula, characteristic variable representing the time t; Based on the prior information of the candidate azimuth trajectories and the posterior information of the current azimuth-related variables, the weights of the candidate azimuth trajectories of the target vehicle are determined, and calculated according to the following formula. In the formula, characterizing the target vehicle at the time of observation T; Based on the weights of the particles and the weights of the candidate azimuth trajectories, the current orientation and current motion state of the target vehicle are determined, and the candidate azimuth trajectories are updated.

2. The method of claim 1, wherein, The initial state of all particles is initialized according to the following formula. wherein characterizing the distance of the particle to the receiving array element, characterizing the absolute velocity of the particle to the receiving array element, characterizing the heading angle of the particle, wherein with subject to a uniform distribution.

3. The method of claim 1, wherein, Determining the current direction finding output result includes: The acoustic signals received by each array element are divided into multiple data frames, and a Fourier transform is performed on each data frame to obtain the frequency domain signal. Based on the frequency domain signal, determine the covariance matrix; determining a current direction finding output result based on a reference array element and the covariance matrix, wherein the reference array element is any one of the array elements.

4. The method of claim 3, wherein, The covariance matrix is ​​calculated according to the following formula. In the formula, Characterizes the number of frames stacked. Characterizes the complex conjugate transpose of a frequency domain signal. Characterizes frequency domain signals, and has ,in, Characterizing frequency, Representation transpose, Represents the sequence number of the snapshot.

5. The method of claim 4, wherein, The direction-finding output result is calculated according to the following formula. In the formula, Characterize the direction finding output results. Weighted vectors representing arrays at different frequencies and in different directions relative to the reference array elements. Characterization The complex conjugate transpose Characterizing the array scanning angle; where, For each weighted vector All have In the formula, Representing the imaginary unit, The time delay of the m-th array element relative to the reference array element is represented.

6. A computing device comprising a processor and a memory, wherein, The memory stores computer program instructions, which, when executed by the processor, perform the method as described in any one of claims 1-5.

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