Layout method and system of metamaterial tag in railway scenario

Abstract

This application relates to the field of rail transit technology and provides a method and system for deploying metamaterial tags in a railway scenario. The method includes: constructing an observation equation with the Euclidean distance between the train and the metamaterial tag as the observation distance, and constructing observation vectors for N metamaterial tags based on the observation equation; constructing a Fisher information matrix about the motion state vector based on the observation vectors and the train's motion state vector, and using the trace of the inverse of the Fisher information matrix as the Cramer-Rhodes lower bound; using the spatial coordinates of the N metamaterial tags as optimization variables, minimizing the Cramer-Rhodes lower bound as the objective, and introducing dynamic environmental constraints to form a constrained optimization problem; solving the optimization problem using a particle swarm optimization algorithm to obtain a set of target tag position coordinates; and deploying metamaterial tags at corresponding positions along the track based on the target tag position coordinates. This improves motion compensation accuracy.

Description

Technical Field

[0001] This application relates to the field of rail transit technology, specifically to a method and system for laying out metamaterial labels in a railway setting. Background Technology

[0002] In radar-based high-speed railway train precision positioning and motion compensation systems, a series of metamaterial tags are typically deployed along the track as position reference beacons. The train's radar calculates its precise motion state by detecting the echoes from these tags, thus compensating for motion errors in the radar image.

[0003] Currently, the industry generally adopts a uniform spacing layout scheme for metamaterial tags. However, this uniform spacing layout scheme is prone to generating periodic grid lobes in signal processing, which can lead to signal distortion at specific vibration frequencies of the train, thereby affecting the motion compensation accuracy of the radar system.

[0004] It is evident that the deployment schemes for metamaterial tags in related technologies suffer from low motion compensation accuracy. Summary of the Invention

[0005] This application aims to address the problem of low motion compensation accuracy in the deployment schemes of metamaterial tags in related technologies, and provides a method and system for deploying metamaterial tags in railway scenarios.

[0006] To solve the above problems, this application is implemented as follows:

[0007] Firstly, this application provides a method for laying out metamaterial labels in a railway setting, including: An observation equation is constructed using the Euclidean distance between the train and the metamaterial tag as the observation distance, and an observation vector for N metamaterial tags is constructed based on the observation equation. The N metamaterial tags are tags to be deployed in the feasible tag installation area, and N is an integer greater than 1. Based on the observation vector and the motion state vector of the train, a Fisher information matrix about the motion state vector is constructed, and the trace of the inverse matrix of the Fisher information matrix is ​​used as the Cramer-Rao lower bound. The Cramer-Rao lower bound is used to characterize the theoretical error lower bound for estimating the motion state of the train under a given label layout. The spatial coordinates of the N metamaterial tags within the feasible tag installation area are used as optimization variables. The goal is to minimize the Cramer-Rhodes lower bound, and dynamic environmental constraints are introduced to form a constrained optimization problem. The particle swarm optimization algorithm is used to solve the optimization problem to obtain the target tag position coordinate set. Based on the target label position coordinate set, metamaterial labels are deployed at corresponding positions along the track.

[0008] Secondly, this application provides a layout system for metamaterial labels in a railway setting, including: The first construction module is used to construct an observation equation with the Euclidean distance between the train and the metamaterial tag as the observation distance, and to construct observation vectors for N metamaterial tags based on the observation equation. The N metamaterial tags are tags to be deployed in the feasible tag installation area, and N is an integer greater than 1. The second construction module is used to construct a Fisher information matrix about the motion state vector based on the observation vector and the motion state vector of the train, and to use the trace of the inverse matrix of the Fisher information matrix as the Cramer-Rao lower bound, which is used to characterize the theoretical error lower bound for estimating the motion state of the train under a given label layout. The solution module is used to take the spatial position coordinates of the N metamaterial tags within the feasible tag installation area as optimization variables, with the goal of minimizing the Cramer-Rhodes lower bound, and introduces dynamic environmental constraints to form a constrained optimization problem; the particle swarm optimization algorithm is used to solve the optimization problem to obtain the target tag position coordinate set; The deployment module is used to deploy metamaterial tags at corresponding positions along the track based on the target tag position coordinate set.

[0009] Thirdly, this application provides a terminal device including a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0010] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0011] Fifthly, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the method described in the first aspect.

[0012] In a sixth aspect, this application provides a computer program product stored in a storage medium, the program product being executed by at least one processor to implement the steps of the method described in the first aspect.

[0013] Compared with the prior art, this application has the following beneficial effects: By constructing observation vectors and the train's motion state vectors, a Fisher information matrix about the motion state vectors is built, and the trace of the inverse of the Fisher information matrix is ​​used as the Cramer-Rhodes lower bound. Then, by using the spatial position coordinates of N metamaterial tags within the feasible tag installation area as optimization variables, the goal is to minimize the Cramer-Rhodes lower bound, and dynamic environmental constraints are introduced to form a constrained optimization problem. This allows the target tag position coordinate set obtained from solving the optimization problem to better match the train's multimodal complex motion. That is, by optimizing the non-uniform layout of the tags, it is possible to better match the train's multimodal complex motion, and it has stronger adaptability and stability to various dynamics of the train under high-speed operation.

[0014] Moreover, deploying metamaterial tags based on the target tag position coordinates can optimize the observation geometry of metamaterial tags, improve the accuracy of observation information, and effectively improve motion compensation accuracy by more than 30% compared to the traditional uniform layout. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for laying out metamaterial labels in a railway scenario according to an embodiment of this application. Figure 2 This is a schematic diagram of the layout system of metamaterial labels in a railway scenario provided in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Additionally, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: including A alone, B alone, C alone, and the presence of both A and B, both B and C, both A and C, and the presence of A, B, and C.

[0019] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0020] The layout method of metamaterial labels in the railway scenario provided in this application will be explained below.

[0021] See Figure 1 , Figure 1 This is a flowchart illustrating a method for laying out metamaterial labels in a railway scenario according to an embodiment of this application. Figure 1 The layout method of metamaterial labels in the railway scenario shown can be executed by terminal devices such as mobile phones and computers.

[0022] like Figure 1 As shown, the method for laying out metamaterial labels in a railway scenario provided in this application may include the following steps: Step 101: Construct an observation equation with the Euclidean distance between the train and the metamaterial tag as the observation distance, and construct observation vectors for N metamaterial tags based on the observation equation. The N metamaterial tags are tags to be deployed in the feasible tag installation area, and N is an integer greater than 1.

[0023] In some embodiments, the metamaterial tags can be passive metamaterial tags, and the metamaterial tags can be understood as position reference beacons deployed along the track. Train radar can calculate the train's precise motion state by detecting the echoes from these tags, thereby compensating for motion errors in the radar image.

[0024] The aforementioned train radar can be understood as radar installed on a train, which can be installed at the front of the train, the roof, or other locations, without any specific limitations.

[0025] Step 102: Based on the observation vector and the motion state vector of the train, construct the Fisher information matrix about the motion state vector, and use the trace of the inverse matrix of the Fisher information matrix as the Cramer-Rao lower bound. The Cramer-Rao lower bound is used to characterize the theoretical error lower bound for estimating the motion state of the train under a given label layout.

[0026] In some embodiments, an orbital coordinate system may be constructed and a feasible area for label installation may be defined.

[0027] In the orbital coordinate system, the origin of the orbital coordinate system can be defined as the center of the starting point of the orbit, the X-axis along the longitudinal direction of the orbit, the Y-axis along the transverse direction of the orbit, and the Z-axis vertically upward.

[0028] In some embodiments, the track length is set to L, which is 1000m in this embodiment; the track width is W, including the track and the safety areas on both sides, which is 4.5m in this embodiment.

[0029] In some embodiments, the label installation area is feasible. Defined as:

[0030] In the formula, Representing three-dimensional real space, This represents the coordinates of the metamaterial tag in three-dimensional space. , The feasible longitudinal range for tag installation is typically [50m, 950m]. , The lateral feasible range for label installation, typically [ [4.25m, 4.25m] , The feasible height range for label installation is typically [0.5m, 1.5m].

[0031] In some embodiments, the motion state vector of the train It can be represented as:

[0032] In the formula, , , They are respectively The position coordinates of the train in three-dimensional space; , , They are respectively The velocity component corresponding to the given moment; This is the transpose operator, used to convert a row vector into a column vector.

[0033] In some embodiments, it is assumed that the track is arranged along the line. The first metamaterial label, the first The fixed position coordinates of the metamaterial label are as follows .

[0034] Tag coordinate vectors can be defined based on the fixed position coordinates of the metamaterial tag. :

[0035] Among them, all The positions of the metamaterial tags constitute a set to be optimized:

[0036] The observation equation for the Euclidean distance between the train and the metamaterial tag, used as the observation distance, can be expressed as:

[0037] in, For train radar to the first The observation distance of a metamaterial tag For the train's position, It has a mean of zero and a variance of . Gaussian observation noise. At time... , Observation vectors of metamaterial tags .

[0038] In some embodiments, the parameter vector to be estimated is . In a given layout and parameters Under the condition of observation vector The joint probability density function (i.e., the likelihood function) is denoted as Then regarding the parameters Fisher Information Matrix Defined as:

[0039] in, Indicates that in a given The expectations below The log-likelihood function for the th Partial derivatives of each parameter, The log-likelihood function for the th Partial derivatives of each parameter, This is the Fisher information matrix, which is 6×6 in size and corresponds to a 6-dimensional state vector.

[0040] For a Gaussian observation noise model, the Fisher information matrix can be calculated by computeding the Jacobian matrix of the observation model with respect to the state parameters. To obtain, that is:

[0041] in, The Jacobian matrix of the observation model is of size N×6, and its elements are:

[0042] According to parameter estimation theory, any unbiased estimator The covariance matrix satisfies the Cramer-Rhodes lower bound:

[0043] therefore, It represents the theoretical lower bound of the estimation error.

[0044] in, For estimator The covariance matrix, The trace of the matrix is ​​the sum of its diagonal elements. It is the inverse of the Fisher information matrix.

[0045] The above observation model can be understood as a model built based on the observation equation, used to observe the Euclidean distance between the train and the metamaterial tag; the above state parameters can be understood as the motion state parameters of the train, including the position coordinates of the train in three-dimensional space and the corresponding velocity components.

[0046] Step 103: Use the spatial coordinates of the N metamaterial tags within the feasible tag installation area as optimization variables, with the goal of minimizing the Cramer-Rhodes lower bound, and introduce dynamic environmental constraints to form a constrained optimization problem; use the particle swarm optimization algorithm to solve the optimization problem to obtain the target tag position coordinate set.

[0047] In some embodiments, the optimal layout problem is addressed, namely, finding a set of N metamaterial labels. This minimizes the Cramer-Rhodes lower bound and satisfies the actual installation constraints.

[0048] The optimization problem described above can be expressed as:

[0049]

[0050]

[0051] in, Let be the objective function, representing the layout... The theoretical lower bound of the estimation error is as follows. Indicates layout Minimize, As constraints, It is the minimum safe installation distance between metamaterial labels.

[0052] In some embodiments, the dynamic environmental constraints include at least one of boundary constraints, spacing constraints, obstacle region constraints, Doppler effect constraints, signal-to-noise ratio variation constraints, and dynamic feasible region constraints.

[0053] In some embodiments, the step of solving the optimization problem using a particle swarm optimization algorithm to obtain the target label location coordinate set includes: M particles are set, each particle representing a layout scheme of the spatial position coordinates of the N metamaterial tags, where M is an integer greater than 1; Set a fitness function, the expression of which is:

[0054] In the formula, For the first The fitness value of each particle. , Let the objective function value be the lower bound of the Cramer-Rhodes test. For the penalty function, This is the penalty coefficient; Through algorithm iteration, the layout scheme corresponding to the particle with the highest fitness value is determined as the target label position coordinate group.

[0055] In some embodiments, the typical value of M is 30 to 50, and the value of M affects the search capability and computational complexity.

[0056] In some embodiments, each particle represents a layout scheme, the first The position vector of each particle is encoded as follows:

[0057] in, For the first Each particle's position vector has a dimension of 3N. Each metamaterial tag has 3 coordinates, and there are a total of N metamaterial tags. It is a 3N-dimensional real number space.

[0058] In some embodiments, The objective function value of the lower bound of the Cramer-Rhodes curve is, i.e., ; It is a penalty function that imposes a penalty on particles that violate constraints, such as when a particle's position violates a distance constraint. or regional constraints At that time, the term is a large positive number.

[0059] In some embodiments, the particle In iteration number The position at this time is The speed is Its individual historical optimal position is The optimal position of the population is The rules for updating speed and position are as follows:

[0060]

[0061] in, For the first The particle in the first The position vector at the next iteration. For the first The particle in the first The velocity vector at the next iteration For the first The individual best position in the history of each particle. This represents the globally optimal position in the entire particle swarm's history. For inertial weights, , These are random numbers uniformly distributed within the range [0,1]. The iteration continues until a termination condition is met, such as reaching the maximum number of iterations or fitness convergence, resulting in the final value. This is the optimal set of tag layouts. This yields the target label's location coordinates.

[0062] Among them, after each iteration Updated to , This is the inertia weight decay coefficient.

[0063] In some embodiments, the penalty function includes at least one of the following: The penalty term corresponding to the boundary constraint; The penalty term corresponding to the spacing constraint; The penalty item corresponding to the obstacle area constraint; The penalty term corresponding to the Doppler effect constraint; The penalty term corresponding to the signal-to-noise ratio variation constraint; The penalty term corresponding to the dynamic feasible region constraint.

[0064] To accommodate boundary constraints, the label location must be within the feasible label installation area. Inside:

[0065] The penalty function for violating boundary constraints is:

[0066] in, , , This is the penalty coefficient, with a typical value of 1000. To take the larger value, when The penalty value is 0.

[0067] Regarding spacing constraints, the minimum installation spacing constraint between any two labels is as follows:

[0068] The penalty function for violating the spacing constraint is:

[0069] in, This is the penalty coefficient, typically 1000.

[0070] To address obstacle region constraints, considering obstacle regions in actual engineering projects (such as bridges, tunnels, signal towers, etc.), a set of obstacle regions is defined. Labels should be avoided in these areas:

[0071] The penalty function for violating obstacle constraints is:

[0072] in, For the first An obstacle region is defined as a three-dimensional cuboid region. , The total number of obstacle areas. This is an indicator function; its value is 1 when the condition is true, and 0 otherwise. This is the penalty coefficient for the corresponding obstacle, with a typical value of 5000.

[0073] Due to the constraints of the Doppler effect, high-speed train operation will cause a Doppler frequency shift in the radar echo. The formula for calculating the Doppler frequency shift is:

[0074] in, for Moment Tags Doppler frequency shift at that location for Time Trains and Labels radial velocity between For radar carrier frequency, The speed of light. Radial velocity. The calculation formula is:

[0075] in, This is the train speed vector.

[0076] Excessive Doppler frequency shift can cause radar receiver detuning, affecting ranging accuracy. Therefore, it is necessary to limit the maximum Doppler frequency shift of each tag throughout the entire train operating range to not exceed the processing range of the radar receiver. :

[0077] in, This is the simulated train running time.

[0078] The penalty function for violating the Doppler effect constraint is:

[0079] in, This is the penalty coefficient, typically 1000. This represents the maximum allowable Doppler frequency shift for the radar system, typically ±1MHz.

[0080] Regarding the signal-to-noise ratio (SNR) variation constraint, the SNR of the received signal changes over time due to the varying distance between the train and the tag. The SNR calculation formula is as follows:

[0081] in, for Moment Tags Signal-to-noise ratio at the location. for The signal power received at any time, Noise power. Received signal power. The calculation formula is:

[0082] in, For radar transmission power, For radar transmitting antenna gain, For radar receiving antenna gain, For radar wavelength, The radar cross section of the tag. for Time Train to Tag Distance at location, L is the system loss factor.

[0083] Noise power The calculation formula is:

[0084] in, Boltzmann's constant, , Standard noise temperature, , For radar system bandwidth, This represents the radar noise figure.

[0085] To ensure that the tags can be effectively detected throughout the entire operating area, the minimum signal-to-noise ratio of each tag within the train operating area must not be lower than the minimum detection signal-to-noise ratio threshold of the radar system. :

[0086] Penalty function for violating signal-to-noise ratio constraints:

[0087] in, This is the penalty coefficient, typically 1000. This is the minimum detection signal-to-noise ratio required by the radar system, typically 10~15dB.

[0088] To address the dynamic feasible region constraint, considering the impact of dynamic factors such as weather conditions and visibility on signal propagation, the dynamic feasible region is defined. :

[0089] in, This refers to the feasible region that changes over time. In this embodiment, the dynamic feasible region is considered for the following two typical scenarios: Sunny day scene: That is, the static feasible region.

[0090] Rainy weather scenario: Due to the attenuation of radar signals by rain, especially higher frequency signals, the feasible area is limited in the vertical direction.

[0091] in, This is the maximum installation height under rainy conditions, with a typical value of 1.2m.

[0092] During optimization, the label location must simultaneously satisfy the feasible region constraints for both sunny and rainy day scenarios:

[0093] The penalty function is:

[0094] in, This is the penalty coefficient, typically 1000.

[0095] In some embodiments, the penalty function The expression is:

[0096] In the formula, The penalty function for violating boundary constraints. The penalty function for violating the spacing constraint. The penalty function for violating obstacle constraints. The penalty function for violating the Doppler effect constraint, The penalty function for violating the signal-to-noise ratio constraint. This is the penalty function for violating the dynamic feasible region constraint.

[0097] Step 104: Based on the target label position coordinate group, deploy metamaterial labels at the corresponding positions along the track.

[0098] In this application, a Fisher information matrix about the motion state vector is constructed based on the observation vector and the motion state vector of the train. The trace of the inverse of the Fisher information matrix is ​​used as the Cramer-Rhodes lower bound. Then, the spatial position coordinates of N metamaterial tags within the feasible tag installation area are used as optimization variables. The goal is to minimize the Cramer-Rhodes lower bound, and dynamic environmental constraints are introduced to form a constrained optimization problem. This allows the target tag position coordinate set obtained from solving the optimization problem to better match the multimodal complex motion of the train. That is, by optimizing the non-uniform layout of the tags, the multimodal complex motion of the train can be better matched, and the train has stronger adaptability and stability to various dynamics under high-speed operation.

[0099] Moreover, deploying metamaterial tags based on the target tag position coordinates can optimize the observation geometry of metamaterial tags, improve the accuracy of observation information, and effectively improve motion compensation accuracy by more than 30% compared to the traditional uniform layout.

[0100] In addition, the metamaterial label layout method in the railway scenario provided in this application is strictly guided by the Cramer-Rao lower bound in information theory. At the same time, the optimization model fully considers practical engineering constraints such as minimum installation spacing and dynamic feasible region, which effectively improves the motion compensation accuracy of the scheme and can be directly applied to engineering design and implementation.

[0101] In some embodiments, after deploying metamaterial tags at corresponding positions along the track based on the target tag position coordinate set, the method further includes: During the train's operation, the train radar detects the metamaterial tags deployed based on the target tag position coordinate group and obtains the corresponding observation distance data. The motion state of the train is estimated in real time based on the observed distance data, and the estimation result is obtained. Based on the estimation result, motion compensation parameters are determined and output.

[0102] In some embodiments, based on the optimized label layout It can implement the extended Kalman filter algorithm to estimate and compensate the train motion state in real time, and verify the performance of the optimized layout through simulation.

[0103] In some embodiments, to accurately describe the train's motion state and design an effective estimation algorithm, a state-space model is first established. This model consists of two parts: state equations and observation equations.

[0104] The state equations, used to describe the dynamic evolution of the train's motion state, can adopt a uniform motion model, and the state vector is defined as follows:

[0105] in, They are respectively The position coordinates of the train in three-dimensional space. This represents the corresponding velocity component.

[0106] The matrix form of the state equations is:

[0107] Wherein, the state transition matrix , It is a 3×3 identity matrix. It is a 3×3 zero matrix. This is the sampling time interval, typically 0.001s (corresponding to a 1000Hz sampling rate). Let be the noise vector of a zero-mean Gaussian process, representing the noise from... Time's up The noise vector of the state transition process at time t. Its covariance matrix is The size is 6×6, reflecting the model uncertainty and unmodeled dynamics.

[0108] The observation equation describes the radar's observation relationship with the tag, and the observation vector. Includes ranging information for N labels:

[0109] The observation equation is in nonlinear form:

[0110] in, For a nonlinear observation function, its first... The components are:

[0111] The zero-mean Gaussian observation noise vector. Its covariance matrix is , with a size of N×N, represents the measurement error of the radar system.

[0112] In some embodiments, for the aforementioned nonlinear state-space model, this application designs an extended Kalman filter algorithm for state estimation. This algorithm achieves recursive optimal estimation by linearizing the nonlinear observation model. The specific steps are as follows: Step 1: At the start of the algorithm, initialize the state estimate and the error covariance matrix:

[0113] in, For initial state estimation, it is usually set as the initial state of the train. The initial error covariance matrix reflects the uncertainty of the initial estimate.

[0114] Step 2, based on Information of time, prediction The state and error covariance at any given time.

[0115] State prediction:

[0116] in, Based on Time information Time-state prediction Indicated based on from the initial time to the... All observation data at time 1, for the 1st Train movement status at all times The optimal estimated vector.

[0117] Error covariance prediction:

[0118] in, The prediction error covariance matrix reflects the uncertainty of the predicted state. Representation and estimation The corresponding estimation error covariance matrix quantifies the uncertainty of the state estimate.

[0119] The prediction step uses a state transition model to propagate the optimal estimate from the previous time step forward to obtain the prior estimate for the current time step.

[0120] Step 3: Calculate the observation Jacobian matrix. Due to the nonlinearity of the observation model, it is necessary to calculate the Jacobian matrix of the observation function at the current state prediction value:

[0121] in, for The observation Jacobian matrix at time step linearizes the nonlinear observation model at the current state prediction.

[0122] No. The row corresponds to the first Each tag has the following elements:

[0123] The Jacobian matrix describes the sensitivity of an observation to changes in state and is key to calculating the Kalman gain.

[0124] Step 4: Correct the prediction results using actual observation data: Calculate the Kalman gain:

[0125] in, for The Kalman gain matrix at time 1 determines the correction weights of the observation information on the state estimation.

[0126] The Kalman gain matrix weighs the confidence level between the predicted and observed values, and determines the extent to which the observed information corrects the state estimate.

[0127] Status Update:

[0128] in, for Optimal state estimation at time t.

[0129] observation residuals The optimal state estimate is obtained by correcting the state prediction using Kalman gain.

[0130] Error covariance update: Update the error covariance matrix to reflect the improvement in estimation accuracy.

[0131]

[0132] for The covariance matrix of the estimation error at time t reflects the uncertainty of the optimal estimate. It is an identity matrix.

[0133] In some embodiments, to ensure the effectiveness and stability of the extended Kalman filter algorithm, the algorithm parameters need to be set appropriately: Process noise covariance matrix:

[0134] in, The standard deviation of the position process noise reflects the randomness of train position changes. The standard deviation of the speed process noise reflects the randomness of train speed changes. Represented by vector It is a diagonal matrix with diagonal elements.

[0135] Observation noise covariance matrix:

[0136] in, The standard deviation of the observed noise reflects the ranging accuracy of the radar system.

[0137] Initial covariance matrix:

[0138] The initial covariance matrix is ​​set to account for the uncertainty of the initial state estimation.

[0139] In some embodiments, a systematic simulation verification process is employed to verify the effectiveness of the optimized layout scheme. This method includes three main steps: train dynamics simulation, radar observation simulation, and performance index calculation.

[0140] Establish a train dynamics model that incorporates real-world factors such as vibration and noise to simulate the train's trajectory under specified operating conditions:

[0141] in, For process noise, simulate random disturbances and unmodeled dynamics of the train.

[0142] Radar observation data is generated based on optimized label layout and actual train trajectory:

[0143] in, To observe noise, the measurement error of the radar system is simulated.

[0144] By comparing the estimated state with the actual state, the following key performance indicators are calculated: Root mean square error of location estimation:

[0145] in, This represents the total number of simulation time steps. for Position estimation at time, for The actual location at any given moment.

[0146] Error components in each direction:

[0147]

[0148]

[0149] Maximum position error:

[0150] Average signal-to-noise ratio:

[0151] in, for Signal power at time, This represents noise power.

[0152] Observability index:

[0153] in, This is the steady-state estimation error covariance matrix, which reflects the overall observability of the system.

[0154] The present invention will be further described below with reference to specific embodiments, but the scope of protection of this application is not limited thereto.

[0155] This application can be verified using the MATLAB simulation platform, with simulation conditions based on actual high-speed railway scenarios. The embodiment focuses on a 300km / h high-speed railway scenario, using eight metamaterial tags. The effectiveness of this application is verified by comparing the motion compensation performance of a traditional uniform layout and the optimized layout of this application.

[0156] I. Simulation Condition Settings This application embodiment verifies the scenario of a high-speed railway with a speed of 300 km / h, and the specific parameter settings are as follows: Train operating parameters: initial speed is 300 km / h, running time is 10 seconds, initial position is [0, 0, 0] m, longitudinal vibration amplitude is ±1.0 cm and frequency is 1.5 Hz, lateral vibration amplitude is ±0.5 cm and frequency is 2.3 Hz, vertical vibration amplitude is ±0.2 cm and frequency is 3.1 Hz; Metamaterial label configuration: 8 labels are required, and the installation area is limited to within ±4.25m on both sides of the track, with a height of 0.5-1.5m. Radar system parameters: operating frequency 77GHz, pulse repetition frequency 1GHz, noise standard deviation 1.0cm, range resolution 0.15m; Optimized algorithm parameters: Particle swarm optimization algorithm, number of particles: 50, maximum number of iterations: 100, inertia weight: 0.7.

[0157] II. Label Layout Results The specific coordinates of the two layout schemes were obtained through MATLAB simulation: Traditional uniform layout coordinates (unit: m): Tag 1 - [50.00, 0.00, 1.0]; Tag 2 - [178.57, -0.00, 1.0]; Tag 3 - [307.14, 0.00, 1.0]; Tag 4 - [435.71, -0.00, 1.0]; Tag 5 - [564.29, 0.00, 1.0]; Tag 6 - [692.86, -0.00, 1.0]; Tag 7 - [821.43, 0.00, 1.0]; Tag 8 - [950.00, -0.00, 1.0]; The optimized layout coordinates in this application (unit: m) are as follows: Tag 1 - [92.34, -2.87, 1.18]; Tag 2 - [215.67, 2.93, 0.82]; Tag 3 - [348.91, -3.12, 1.42]; Tag 4 - [476.23, 2.51, 0.61]; Tag 5 - [618.74, -2.79, 1.28]; Tag 6 - [752.36, 3.24, 0.89]; Tag 7 - [887.49, -3.05, 1.11]; Tag 8 - [1019.82, 2.68, 0.73].

[0158] Based on the above layout coordinates, the following layout characteristics can be observed: Uniform layout, average spacing = 385.71m, minimum spacing = 128.57m; The layout was optimized, with an average spacing of 5.32m and a minimum spacing of 2.41m. III. Comparison of Positioning Accuracy The positioning accuracy comparison is shown in Table 1.

[0159] Table 1: Comparison of Positioning Accuracy

[0160] Based on the above comparison table of positioning accuracy, it can be seen that the root mean square error (RMSE) of the position estimation decreased from 154.17 cm for the traditional uniform layout to 34.20 cm for the optimized layout, with an accuracy improvement of 77.8%; the maximum position error decreased from 266.86 cm to 134.57 cm, with an error reduction of 49.6%, which significantly improved the stability and reliability of positioning.

[0161] See Figure 2 , Figure 2 This is a schematic diagram of the layout system for metamaterial labels in a railway scenario provided in one embodiment of this application. Figure 2 As shown, system 200 includes: The first construction module 201 is used to construct an observation equation with the Euclidean distance between the train and the metamaterial tag as the observation distance, and to construct an observation vector of N metamaterial tags based on the observation equation. The N metamaterial tags are tags to be deployed in the feasible tag installation area, and N is an integer greater than 1. The second construction module 202 is used to construct a Fisher information matrix about the motion state vector based on the observation vector and the motion state vector of the train, and to use the trace of the inverse matrix of the Fisher information matrix as the Cramer-Rao lower bound, which is used to characterize the theoretical error lower bound for estimating the motion state of the train under a given label layout. The solution module 203 is used to take the spatial position coordinates of the N metamaterial tags in the feasible tag installation area as optimization variables, with the goal of minimizing the Cramer-Rhodes lower bound, and introduce dynamic environmental constraints to form a constrained optimization problem; the particle swarm optimization algorithm is used to solve the optimization problem to obtain the target tag position coordinate set; The deployment module 204 is used to deploy metamaterial tags at corresponding positions along the track based on the target tag position coordinate group.

[0162] Optionally, the system 200 further includes: The acquisition module is used to detect metamaterial tags deployed based on the target tag position coordinate group by train radar during the train's operation, and to acquire the corresponding observation distance data. The compensation module is used to estimate the motion state of the train in real time based on the observed distance data, obtain the estimation result, and determine and output motion compensation parameters based on the estimation result.

[0163] Optionally, the dynamic environmental constraints include at least one of boundary constraints, spacing constraints, obstacle region constraints, Doppler effect constraints, signal-to-noise ratio variation constraints, and dynamic feasible region constraints.

[0164] Optionally, the solution module 203 is specifically used for: M particles are set, each particle representing a layout scheme of the spatial position coordinates of the N metamaterial tags, where M is an integer greater than 1; Set a fitness function, the expression of which is:

[0165] In the formula, For the first The fitness value of each particle. , Let the objective function value be the lower bound of the Cramer-Rhodes test. For the penalty function, This is the penalty coefficient; Through algorithm iteration, the layout scheme corresponding to the particle with the highest fitness value is determined as the target label position coordinate group.

[0166] Optionally, the penalty function includes at least one of the following: The penalty term corresponding to the boundary constraint; The penalty term corresponding to the spacing constraint; The penalty item corresponding to the obstacle area constraint; The penalty term corresponding to the Doppler effect constraint; The penalty term corresponding to the signal-to-noise ratio variation constraint; The penalty term corresponding to the dynamic feasible region constraint.

[0167] The metamaterial label layout system for railway scenarios provided in this application can achieve the functionality described in this application. Figure 1 The various processes in the method embodiments, and the ways to achieve the same beneficial effects, will not be repeated here to avoid repetition.

[0168] like Figure 3As shown, this application also provides a terminal device, including a processor 301 and a memory 302. The memory 302 stores a program or instructions that can run on the processor 301. When the program or instructions are executed by the processor 301, they implement the various steps of the above-described embodiment of the metamaterial label layout method in the railway scenario and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0169] It should be noted that the terminal device in this application can be a terminal or other devices besides a terminal. For example, the terminal device can be a mobile phone, tablet computer, laptop computer, etc., and this application does not make any specific limitation.

[0170] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described embodiment of the metamaterial label layout method in the railway scenario, and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0171] The processor is the processor in the terminal device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (Read-Only Memory). Only memory (ROM), random access memory (RAM), magnetic disks or optical disks, etc.

[0172] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the metamaterial label layout method in the railway scenario, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0173] It should be understood that the chip mentioned in this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0174] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described embodiment of the metamaterial label layout method in the railway scenario, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0175] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0176] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as a read-only memory). The device includes a number of instructions in a ROM (random access memory), RAM (magnetic disk), or optical disk to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0177] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for laying out metamaterial labels in a railway scenario, characterized in that, include: An observation equation is constructed using the Euclidean distance between the train and the metamaterial tag as the observation distance, and an observation vector for N metamaterial tags is constructed based on the observation equation. The N metamaterial tags are tags to be deployed in the feasible tag installation area, and N is an integer greater than 1. Based on the observation vector and the motion state vector of the train, a Fisher information matrix about the motion state vector is constructed, and the trace of the inverse matrix of the Fisher information matrix is ​​used as the Cramer-Rao lower bound. The Cramer-Rao lower bound is used to characterize the theoretical error lower bound for estimating the motion state of the train under a given label layout. The spatial coordinates of the N metamaterial tags within the feasible tag installation area are used as optimization variables. The goal is to minimize the Cramer-Rhodes lower bound, and dynamic environmental constraints are introduced to form a constrained optimization problem. The particle swarm optimization algorithm is used to solve the optimization problem to obtain the target tag position coordinate set. Based on the target label position coordinate set, metamaterial labels are deployed at corresponding positions along the track.

2. The method according to claim 1, characterized in that, After deploying metamaterial tags at corresponding positions along the track based on the target tag position coordinate set, the method further includes: During the train's operation, the train radar detects the metamaterial tags deployed based on the target tag position coordinate group and obtains the corresponding observation distance data. The motion state of the train is estimated in real time based on the observed distance data, and the estimation result is obtained. Based on the estimation result, motion compensation parameters are determined and output.

3. The method according to claim 1 or 2, characterized in that, The dynamic environmental constraints include at least one of the following: boundary constraints, spacing constraints, obstacle region constraints, Doppler effect constraints, signal-to-noise ratio variation constraints, and dynamic feasible region constraints.

4. The method according to claim 3, characterized in that, The optimization problem is solved using the particle swarm optimization algorithm to obtain the target label position coordinate set, including: M particles are set, each particle representing a layout scheme of the spatial position coordinates of the N metamaterial tags, where M is an integer greater than 1; Set a fitness function, the expression of which is: In the formula, For the first The fitness value of each particle. , Let the objective function value be the lower bound of the Cramer-Rhodes test. For the penalty function, This is the penalty coefficient; Through algorithm iteration, the layout scheme corresponding to the particle with the highest fitness value is determined as the target label position coordinate group.

5. The method according to claim 4, characterized in that, The penalty function includes at least one of the following: The penalty term corresponding to the boundary constraint; The penalty term corresponding to the spacing constraint; The penalty item corresponding to the obstacle area constraint; The penalty term corresponding to the Doppler effect constraint; The penalty term corresponding to the signal-to-noise ratio variation constraint; The penalty term corresponding to the dynamic feasible region constraint.

6. A layout system for metamaterial labels in a railway setting, characterized in that, include: The first construction module is used to construct an observation equation with the Euclidean distance between the train and the metamaterial tag as the observation distance, and to construct observation vectors for N metamaterial tags based on the observation equation. The N metamaterial tags are tags to be deployed in the feasible tag installation area, and N is an integer greater than 1. The second construction module is used to construct a Fisher information matrix about the motion state vector based on the observation vector and the motion state vector of the train, and to use the trace of the inverse matrix of the Fisher information matrix as the Cramer-Rao lower bound, which is used to characterize the theoretical error lower bound for estimating the motion state of the train under a given label layout. The solution module is used to take the spatial position coordinates of the N metamaterial tags within the feasible tag installation area as optimization variables, with the goal of minimizing the Cramer-Rhodes lower bound, and introduces dynamic environmental constraints to form a constrained optimization problem; the particle swarm optimization algorithm is used to solve the optimization problem to obtain the target tag position coordinate set; The deployment module is used to deploy metamaterial tags at corresponding positions along the track based on the target tag position coordinate set.

7. The system according to claim 6, characterized in that, The system also includes: The acquisition module is used to detect metamaterial tags deployed based on the target tag position coordinate group by train radar during the train's operation, and to acquire the corresponding observation distance data. The compensation module is used to estimate the motion state of the train in real time based on the observed distance data, obtain the estimation result, and determine and output motion compensation parameters based on the estimation result.

8. The system according to claim 6 or 7, characterized in that, The dynamic environmental constraints include at least one of the following: boundary constraints, spacing constraints, obstacle region constraints, Doppler effect constraints, signal-to-noise ratio variation constraints, and dynamic feasible region constraints.

9. The system according to claim 8, characterized in that, The solution module is specifically used for: M particles are set, each particle representing a layout scheme of the spatial position coordinates of the N metamaterial tags, where M is an integer greater than 1; Set a fitness function, the expression of which is: In the formula, For the first The fitness value of each particle. , Let the objective function value be the lower bound of the Cramer-Rhodes test. For the penalty function, This is the penalty coefficient; Through algorithm iteration, the layout scheme corresponding to the particle with the highest fitness value is determined as the target label position coordinate group.

10. A terminal device, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions executable on the processor, the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 5.

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