Vehicle cluster cooperative positioning method based on MDS-IDBO algorithm

Abstract

The invention discloses a vehicle cluster cooperative positioning method based on an MDS-IDBO algorithm. The method comprises the following steps: step 1, constructing a multi-node cooperative positioning model; step 2, calculating relative coordinates by using an MDS-MAP algorithm; step 3, anchor node positioning correction is carried out by using an IDBO algorithm; 4, recalculating the distance square matrix and the relative coordinates of the nodes according to the step 1 and the step 2 by using the anchor nodes positioned and corrected in the step 3; 5, the distance square matrix and the node relative coordinates are utilized, a positioning error function serves as a fitness function to be substituted into an IDBO algorithm, an optimal coordinate transformation matrix is obtained, and absolute coordinates are calculated. The method is suitable for the technical field of communication, node positioning correction is carried out based on the MDS-MAP algorithm, errors caused by BDS / GPS positioning can be corrected to a great extent, and the accuracy of node positioning correction is improved. The provided IDBO algorithm is used for optimizing the MDS-MAP algorithm, the algorithm complexity is low, the calculation time is short, and the positioning precision is high.

Description

Technical Field

[0001] The invention belongs to the field of communication technology, and in particular is a vehicle cluster collaborative positioning method based on an MDS-IDBO algorithm. Background Art

[0002] Vehicle networking has become a technology that improves the quality of our lives through various safety and non-safety applications. The applications of vehicle networking in our lives include: traffic safety and efficiency, infotainment, parking assistance, roadside assistance, remote diagnosis, telematics, intelligent logistics and transportation, and autonomous vehicles. It is a complex and multidisciplinary platform, and the key is high-precision and high-reliability positioning technology. Under different driving and environmental conditions, the information outside the vehicle obtained through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems has shown the potential to improve the accuracy, robustness and reliability of vehicle positioning. In such a system, the vehicle can broadcast its status information to other vehicles, including speed, heading, location, and information related to the environment, and can also obtain information about severe weather conditions or obstacles from the infrastructure.

[0003] Collaborative positioning technology exchanges information through the use of wireless communication devices, such as Ultra WideBand (UWB), Wi-Fi, and cellular. Generally speaking, the main limitations affecting positioning accuracy are its limited field of view, out-of-range environmental information, and limited operation in harsh environments. Collaborative positioning technology offers great potential for addressing this limitation. Therefore, when information from a sufficient number of connected vehicles can be obtained, the accuracy of positioning can be significantly improved. In general scenarios, by equipping the vehicle with a BDS / GPS module, high-precision location information can be dynamically obtained. However, when the environment in which the vehicle is located is unstable or there are some abnormal weather conditions, the BDS / GPS signals of some vehicles may be interrupted or the positioning results may have large errors, which will greatly increase the positioning error of the target to be located.

[0004] The multi-dimensional scaling positioning algorithm (MDS-MAP) has become a popular choice for people to choose collaborative positioning algorithms due to its advantages such as adaptability to multiple scenarios, low requirements for anchor nodes, and low hardware cost. The MDS-MAP algorithm has a large positioning error when the node network topology is poor. At the same time, during the coordinate conversion process, the accuracy of the parameters will directly affect the positioning effect. Therefore, the present invention improves it from two aspects: anchor node deployment and parameter optimization.

[0005] At present, most of the research work on optimizing the deployment of anchor nodes focuses on ensuring that the nodes can be located, but less consideration is given to the impact of the geometric shape of the anchor node deployment on the positioning accuracy.

[0006] Intelligent optimization algorithm is a meta-heuristic algorithm established by simulating some phenomena in nature. Compared with traditional mathematical programming methods, it relies on simple rules and randomness to solve complex multi-peak extreme value problems. Since this method has a weak dependence on the initial solution and does not require gradient information, it successfully makes up for the shortcomings of traditional optimization algorithms and provides a new way to deal with complex optimization problems. The Dung Beetle Swarm Algorithm (DBO) is a type of intelligent optimization algorithm. It calculates the optimal value of the fitness function and its corresponding optimal solution by simulating the rolling, foraging, stealing and reproduction behaviors of dung beetles in nature.

[0007] On this basis, the present invention combines the Tent chaotic mapping and the reverse learning algorithm to improve it, and proposes an improved dung beetle swarm optimization algorithm. GDOP is the ratio between the ranging measurement error and the positioning estimation error, which is often used to estimate the expected positioning estimation accuracy and shows excellent performance in wireless sensor network applications. In order to improve the positioning accuracy, the present invention searches for the minimum GDOP of the vehicle cluster collaborative positioning wireless sensor network through the IDBO algorithm, optimizes the geometric layout of the anchor nodes, and uses the IDBO algorithm to optimize the conversion parameters in the coordinate conversion stage to effectively reduce the impact of parameter uncertainty on positioning. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the prior art and provide a vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm comprises the following steps:

[0011] Step 1: Build a multi-node collaborative positioning model;

[0012] Step 2: Calculate relative coordinates using the MDS-MAP algorithm;

[0013] Step 3: Use IDBO algorithm to correct the anchor node positioning;

[0014] Step 4: Use the anchor nodes that have been positioned and corrected in step 3 to recalculate the distance square matrix and node relative coordinates according to steps 1 and 2;

[0015] Step 5: Using the distance square matrix and the relative coordinates of the nodes, substitute the positioning error function into the IDBO algorithm as the fitness function to obtain the optimal coordinate transformation matrix and calculate the absolute coordinates.

[0016] Preferably, the step 1 specifically includes:

[0017] Step 11: Multi-node collaborative positioning scenario, in which the nodes are equipped with BDS / GPS module anchors, the nodes are not fixed but in a mobile state, and the nodes are all equipped with communication equipment;

[0018] Step 12: Build a multi-node collaborative positioning model:

[0019] In the UWB system of time synchronization and networking ranging system, a total of N nodes are added to the network equipment, and communication is carried out in the k-dimensional space, k is selected as 2, and the node p i The coordinates of i =(x i ,y i ), the coordinate vector corresponding to the position information is X = [p 1 ,p 2 ,...,p n ] T , let d ij Represents the Euclidean distance value between nodes i and j, and all measured Euclidean distance values ​​form an N-dimensional symmetric distance matrix D;

[0020] The Euclidean distance calculation formula is:

[0021]

[0022] The N-dimensional symmetric distance matrix D is:

[0023]

[0024] Preferably, the step 2 specifically includes: assuming that the coordinates of all nodes are at the origin, setting the distance measurement value between the nodes to the difference between the two, using the distance measurement information to form a distance matrix, solving the relative coordinates of each node through linear transformation and singular value decomposition, and performing singular value decomposition on the centralized distance square matrix:

[0025] The distance matrix Centering and calculating the inner product matrix yields:

[0026]

[0027] Among them, D em represents the mth Hadamard power of D, J is a central matrix and can be expressed as:

[0028]

[0029] Pair Matrix Perform singular value decomposition, retain two or three largest eigenvalues ​​and eigenvectors, and construct the relative coordinate X:

[0030]

[0031] Where Q represents the matrix composed of eigenvectors; Λ represents the diagonal matrix composed of corresponding eigenvalues.

[0032] Preferably, the step 3 specifically includes:

[0033] Step 31: Calculate GDOP;

[0034] Step 32: Substitute GDOP as the fitness function into the IDBO algorithm to improve the layout of the anchor nodes.

[0035] Preferably, the step 31 specifically includes:

[0036] Assume that there are N nodes in the area, including L anchor nodes and M nodes to be located. Among all these anchor node combinations, the anchor node combination that makes the wireless sensor network have the minimum geometric precision dilution GDOP can be found. Given the information of anchor nodes with known positions, the shortest estimated adjacent information matrix between the anchor nodes and the unknown nodes under the kth combination is:

[0037]

[0038] Assuming t=1,2,...,M,a=1,2,...,L,then the calculation of GDOP needs to use the direction cosines to form the geometric matrix G of the unknown nodes k for:

[0039]

[0040] in, and It is calculated by the node using the direction cosines on the x-axis and y-axis. and It is the initial estimated relative position coordinates of the unknown nodes obtained using MDS.

[0041] Using this matrix to calculate the GDOP of the network is defined as:

[0042]

[0043] in:

[0044]

[0045] Preferably, in step 32, GDOP is substituted into the IDBO algorithm as the fitness function to improve the layout of the anchor nodes, specifically including:

[0046] Step 3.2.1: Population initialization, given initial parameters;

[0047] Step 3.2.2: Calculate the fitness value of each individual through the existing objective function;

[0048] Step 3.2.3: Update the individual's position;

[0049] Step 3.2.4: Update the individual best position and the group best position, and determine whether the termination conditions are met. If so, stop and output the final result; if not, return to continue executing the algorithm.

[0050] Preferably, the step 3.2.1 specifically includes:

[0051] (1) Use Tent Mapping to generate the initial positions x of N dung beetles i ;

[0052]

[0053] It should be noted that when Θ is 0.5, the value of x should avoid falling into (0.2, 0.4, 0.6, 0.8) to cause the system to present a short-period state, or falling into (0, 0.25, 0.5, 0.75) to cause a fixed point to appear later;

[0054] (2) The N initial solutions x generated by the above formula i Generate the corresponding N reverse initial solutions nx according to the formula i ;

[0055] nx i =rand(Ub-Lb)-x i ;

[0056] Where Ub and Lb are the upper and lower limits of the current dung beetle location;

[0057] (3) In solving the optimization problem of finding the minimum value, 2N x will be generated. i and nx i The function fitness values ​​of the dung beetle's location are arranged in order, and the first N values ​​are selected as the initial population.

[0058] Preferably, the steps 3.2.2 and 3.2.3 specifically include:

[0059] (1) Rolling ball operator

[0060] When the dung beetle moves forward without obstacles, the solution of its individual position update equation, that is, the fitness function, is as follows:

[0061] x i (t+1)=xi (t)+α×k×x i (t-1)+b×Δx

[0062] Δx=|x i (t)-X w |;

[0063] Among them, t represents the current iteration number, x i (t) represents the position information of the i-th dung beetle in the t-th iteration, k∈(0,0.2] is the perturbation coefficient, b is a random number between (0,1), α is -1 or 1, X w represents the global worst position;

[0064] When the dung beetle encounters an obstacle, it needs to adjust its direction and obtain a new route. This paper uses the tangent function to obtain a new rolling direction, and the rolling direction is only considered between [0,π]. The position update is as follows:

[0065] x i (t+1)=x i (t)+tan(θ)x i (t)-x i (t-1)|;

[0066] Among them, θ is the deflection angle, and its value is [0,π];

[0067] (2) Reproduction operator

[0068] Inspired by nature, a boundary selection strategy is proposed to simulate the area where female dung beetles lay eggs:

[0069] Lb * =max(X * ×(1-R),Lb)

[0070] Ub * =min(X * ×(1+R),Ub);

[0071] Among them, X * Indicates the current optimal position, which is also the optimal solution in the region of the optimization problem. Lb * and Ub * Indicates the lower and upper limits of the spawning area, R = 1-t / T max ,T max represents the maximum number of iterations, Lb and Ub represent the lower and upper limits of the optimization problem;

[0072] Once the female dung beetle selects an egg-laying area, it will produce a brood ball in the area to lay eggs; each female dung beetle only produces one egg in each iteration; the boundary range of the egg-laying area is dynamic and is mainly determined by the R value; therefore, the position of the brood ball is also dynamic during the iteration process, and is defined as follows:

[0073] B i (t+1)=X * +b 1 ×(B i (t)-Lb * )+b 2 ×(B i (t)-Ub * );

[0074] Among them, B i (t) represents the position information of the i-th brooding ball in the t-th iteration, b 1 and b 2 are all random vectors of D dimension, where D represents the dimension of the optimization problem;

[0075] (3) Foraging operator

[0076] The eggs laid by the female dung beetle will gradually hatch into daughter dung beetles, which will come out from underground to look for food. The optimal foraging area is modeled as follows:

[0077] Lb b =max(X b ×(1-R),Lb)

[0078] Ub b =min(X b ×(1+R),Ub);

[0079] Among them, X b represents the global optimal position, Lb b and Ub b denote the lower and upper boundaries of the optimal foraging area, respectively;

[0080] The position of the dung beetle has been updated as follows:

[0081] x i (t+1)=x i (t)+C 1 ×(x i (t)-Lb b )+C 2 ×(x i (t)-Ub b );

[0082] Among them, x i (t) represents the position of the i-th dung beetle in the t-th iteration, C 1is a random number that follows a normal distribution, C 2 is a random vector of (0,1);

[0083] (4) Stealing Operator

[0084] The position of the dung beetle executing the stealing operator is updated as follows:

[0085] x i (t+1)=X b +S×g×(x i (t)-X * |+x i (t)-X b );

[0086] Among them, x i (t) represents the position of the i-th thief in the t-th iteration, g is a D-dimensional random vector that follows a normal distribution, and S is a constant;

[0087] In order to balance the algorithm's global optimization and local development capabilities, a dynamic weight strategy is introduced in the stealing operator. The formula is as follows:

[0088]

[0089] Among them, a 1 It is relatively large in the early stage of iteration, which increases the global optimization ability of the algorithm; a 2 It is gradually increased in the later stages of iterations to enable the operator to escape from the local optimum;

[0090] After the improvement, the position of the dung beetle executing the stealing operator is updated as follows:

[0091] x i (t+1)=a 1 ×X b +a 2 ×S×g×(x i (t)-X * |+x i (t)-X b ).

[0092] Preferably, the step 4 specifically includes:

[0093] Use the updated anchor nodes to recalculate the distance square matrix and node relative coordinates according to steps 1 and 2.

[0094] Preferably, the step 5 specifically includes:

[0095] Using the anchor node coordinates and relative coordinate matrix, the positioning error function is substituted into the IDBO algorithm as the fitness function to obtain the optimal coordinate transformation matrix and calculate the absolute coordinates:

[0096] The absolute coordinate calculation is as follows:

[0097]

[0098] Where (x 1 ,x 2 ), (y 1 ,y 2 ) are the relative coordinates and absolute coordinates of the anchor node, Δx 1 ,Δx 2 is the translation vector, α and m are the rotation angle and control parameter respectively; in the coordinate transformation process, the accuracy of the parameters will directly affect the positioning effect. To ensure the positioning accuracy, the best parameters should be calculated as much as possible;

[0099] Set the error function f(Δx 1 ,Δx 2 ,α,m) is used to measure the difference between the actual coordinates of the anchor node and the estimated results, and N is the number of anchor nodes;

[0100]

[0101] f(Δx 1 ,Δx 2 The smaller the f(Δx,α,m), the better the parameter optimization effect and the smaller the final positioning error. 1 ,Δx 2 ,α,m) as the fitness function and substitute it into the IDBO algorithm to solve argmin‖f(Δx 1 ,Δx 2 ,α,m)‖ to obtain the optimal coordinate transformation parameters to improve positioning accuracy.

[0102] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0103] In the present invention, the node positioning correction is performed based on the MDS-MAP algorithm, which can correct the error caused by BDS / GPS positioning to a large extent. The MDS-MAP algorithm is optimized using the proposed IDBO algorithm, and the algorithm has low algorithm complexity, short calculation time and high positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 The algorithm flow of the present invention is Figure 1 ;

[0105] Figure 2 The algorithm flow of the present invention is Figure 2 ;

[0106] Figure 3 It is a positioning system implementation diagram of the present invention. DETAILED DESCRIPTION

[0107] The specific embodiments of the present invention are described in detail below.

[0108] The "range" disclosed in the present invention is defined in the form of a lower limit and an upper limit, and a given range is defined by selecting a lower limit and an upper limit, and the selected lower limit and upper limit define the boundaries of the particular range. The range defined in this way can be inclusive or exclusive of the end values, and can be arbitrarily combined, that is, any lower limit can be combined with any upper limit to form a range. For example, if a range of 10 to 50 is listed for a specific parameter, it is understood that the range of 10 to 40 and 20 to 50 is also expected. In addition, if the minimum range values ​​1 and 2 are listed, and if the maximum range values ​​3, 4 and 5 are listed, the following ranges can all be expected: 1 to 3, 1 to 4, 1 to 5, 2 to 3, 2 to 4 and 2 to 5. In this application, unless otherwise specified, the range of values ​​"a to b" represents an abbreviation of any real number combination between a and b, where a and b are both real numbers. For example, the range of values ​​"0 to 5" means that all real numbers between "0 to 5" have been fully listed in this article, and "0 to 5" is only an abbreviation of these numerical combinations.

[0109] Unless otherwise specified, all embodiments and optional embodiments of the present application can be combined with each other to form a new technical solution.

[0110] Unless otherwise specified, all technical features and optional technical features of this application can be combined with each other to form a new technical solution.

[0111] If there is no special explanation, all steps of the present application can be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (a) and (b), which means that the method may include steps (a) and (b) performed sequentially, or may include steps (b) and (a) performed sequentially. For example, the method may further include step (c), which means that step (c) may be added to the method in any order. For example, the method may include steps (a), (b) and (c), or may include steps (a), (c) and (b), or may include steps (c), (a) and (b), etc.

[0112] If there is no special explanation, the "include" and "comprising" mentioned in this application are open-ended or closed-ended. For example, the "include" and "comprising" may mean that other components not listed may also be included or only the listed components may be included or only the listed components may be included.

[0113] Unless otherwise specified, the reaction is carried out at room temperature and pressure.

[0114] Unless otherwise specified, all parts or percentages are by weight.

[0115] In the present invention, all substances used are known substances and can be purchased or synthesized by known methods.

[0116] In the present invention, the devices or equipment used are all conventional devices or equipment known in the art and are commercially available.

[0117] The following is a further description of a specific implementation of a vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm of the present invention in conjunction with an embodiment. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm of the present invention is not limited to the description of the following embodiment.

[0118] Reference Figures 1 to 3 , the present invention mainly comprises the following steps:

[0119] Step 1) Build a multi-node collaborative positioning model:

[0120] (1) Multi-node positioning scenario:

[0121] The multi-node collaborative positioning scenario includes multiple nodes, among which the nodes are equipped with BDS / GPS modules, the anchor nodes are not fixed and are in a mobile state, and the nodes are all equipped with communication equipment.

[0122] (2) Constructing a multi-node positioning model:

[0123] It is known that the total number of network devices added to the UWB system of time synchronization and networking ranging system is N nodes, and communication is carried out in k-dimensional space. This paper only considers the relative position solution in two-dimensional space, so k is selected as 2. Node p i The coordinates of can be expressed as p i =(x i ,y i ), the coordinate vector corresponding to the position information is X = [p 1 ,p 2 ,...,p n ] T , and let d ij Represents the Euclidean distance between nodes i and j, as follows:

[0124]

[0125] All measured distances form an N-dimensional symmetric distance matrix D, which is expressed as follows:

[0126]

[0127] Step 2) Calculate the relative coordinates using the MDS-MAP algorithm:

[0128] The distance matrix Centering and calculating the inner product matrix yields:

[0129]

[0130] Among them, D em represents the mth Hadamard power of D, J is a central matrix and can be expressed as:

[0131]

[0132] Pair Matrix Perform singular value decomposition, retain two or three largest eigenvalues ​​and eigenvectors, and construct the relative coordinate X:

[0133]

[0134] Where Q represents the matrix composed of eigenvectors; Λ represents the diagonal matrix composed of corresponding eigenvalues.

[0135] Step 3) Use IDBO algorithm to correct anchor node positioning:

[0136] Calculate GDOP:

[0137] Assuming that there are N nodes in the area, including L anchor nodes and M nodes to be located, among all these anchor node combinations, the anchor node combination that makes the wireless sensor network have the minimum geometric precision dilution GDOP can be found. Given the information of anchor nodes with known positions, the shortest estimated neighbor information matrix between the anchor nodes and the unknown nodes under the kth combination is:

[0138]

[0139] Assuming t=1,2,...,M,a=1,2,...,L,then the calculation of GDOP needs to use the direction cosines to form the geometric matrix G of the unknown nodes k for:

[0140]

[0141] in, and It is calculated by the node using the direction cosines on the x-axis and y-axis. and It is the initial estimated relative position coordinates of the unknown nodes obtained using MDS.

[0142] Using this matrix to calculate the GDOP of the network is defined as:

[0143]

[0144] GDOP is used as the fitness function to substitute into the IDBO algorithm to improve the layout of anchor nodes. The IDBO algorithm process is as follows:

[0145] Population initialization, given initial parameters:

[0146] (a.1) Use Tent mapping to generate the initial positions x of N dung beetles i :

[0147]

[0148] It should be noted that when Θ is 0.5, the value of x should avoid falling into (0.2, 0.4, 0.6, 0.8) to cause the system to present a short-period state, or falling into (0, 0.25, 0.5, 0.75) to cause a fixed point to appear later.

[0149] (a.2) The N initial solutions x generated by the above formula i Generate the corresponding N reverse initial solutions nx according to the formula i ;

[0150] nx i =rand(Ub-Lb)-x i ;

[0151] Where Ub and Lb are the upper and lower limits of the current dung beetle location.

[0152] (a.3) In solving the optimization problem of finding the minimum value, 2N x will be generated. i and nx i The function fitness values ​​of the dung beetle's location are arranged in order, and the first N values ​​are selected as the initial population.

[0153] According to the following four operators, the fitness value of each individual is calculated through the existing objective function and the position of the individual is updated (i.e. the solution of the fitness function)

[0154] (b.1) Rolling ball operator

[0155] When the dung beetle moves forward without obstacles, the solution of its individual position update equation, that is, the fitness function, is as follows:

[0156] x i (t+1)=x i (t)+α×k×x i (t-1)+b×Δx

[0157] Δx=|x i (t)-X w |;

[0158] Among them, t represents the current iteration number, xi (t) represents the position information of the i-th dung beetle in the t-th iteration, k∈(0,0.2] is the perturbation coefficient, b is a random number between (0,1), α is -1 or 1, X w Represents the global worst position. The value of α is determined using Algorithm 1.

[0159]

[0160] When the dung beetle encounters an obstacle, it needs to adjust its direction and obtain a new route. This paper uses the tangent function to obtain a new rolling direction, and the rolling direction is only considered between [0,π]. The position update is as follows:

[0161] x i (t+1)=x i (t)+tan(θ)x i (t)-x i (t-1), θ∈[0,π];

[0162] (b.2) Rolling ball operator

[0163] Use a boundary selection strategy to simulate the area where female dung beetles lay eggs:

[0164] Lb * =max(X * ×(1-R),Lb)

[0165] Ub * =min(X * ×(1+R),Ub);

[0166] Among them, X * Indicates the current optimal position, which is also the optimal solution in the region of the optimization problem. Lb * and Ub * Indicates the lower and upper limits of the spawning area, R = 1-t / T max ,T max represents the maximum number of iterations, Lb and Ub represent the lower and upper limits of the optimization problem.

[0167] Once the female dung beetle selects an egg-laying area, it will produce a brood ball in the area to lay eggs. Each female dung beetle only lays one egg in each iteration. The boundary range of the egg-laying area is dynamic and is mainly determined by the R value. Therefore, the position of the brood ball is also dynamic during the iteration process, and is defined as follows:

[0168] B i (t+1)=X * +b 1 ×(B i (t)-Lb * )+b 2 ×(Bi (t)-Ub * );

[0169] Among them, B i (t) represents the position information of the i-th brooding ball in the t-th iteration, b 1 and b 2 are all D-dimensional random vectors, where D represents the dimension of the optimization problem.

[0170] (b.3) Foraging operator

[0171] The eggs laid by the female dung beetle will gradually hatch into daughter dung beetles, which will come out from underground to look for food. The optimal foraging area is modeled as follows:

[0172] Lb b =max(X b ×(1-R),Lb)

[0173] Ub b =min(X b ×(1+R),Ub);

[0174] Among them, X b represents the global optimal position, Lb b and Ub b Represent the lower and upper boundaries of the optimal foraging area, respectively.

[0175] The position of the dung beetle has been updated as follows:

[0176] x i (t+1)=x i (t)+C 1 ×(x i (t)-Lb b )+C 2 ×(x i (t)-Ub b );

[0177] Among them, x i (t) represents the position of the i-th dung beetle in the t-th iteration, C 1 is a random number that follows a normal distribution, C 2 is a random vector in (0,1).

[0178] (b.4) Stealing Operator

[0179] The position of the dung beetle executing the stealing operator is updated as follows:

[0180] x i (t+1)=X b +S×g×(x i (t)-X * |+x i(t)-X b );

[0181] Among them, x i (t) represents the position of the i-th thief in the t-th iteration, g is a D-dimensional random vector obeying the normal distribution, and S is a constant.

[0182] In order to balance the algorithm's global optimization and local development capabilities, a dynamic weight strategy is introduced in the stealing operator. The formula is as follows:

[0183]

[0184] Among them, a 1 It is relatively large in the early stage of iteration, which increases the global optimization ability of the algorithm; a 2 It is gradually increased in the later stages of iteration to enable the operator to escape from the local optimum.

[0185] After the improvement, the position of the dung beetle executing the stealing operator is updated as follows:

[0186] x i (t+1)=a 1 ×X b +a 2 ×S×g×(x i (t)-X * |+x i (t)-X b );

[0187] Update the individual best position and the group best position, and determine whether the termination iteration conditions are met. If so, stop and output the final result; if not, return to continue executing the algorithm.

[0188] Step 4) Use the anchor nodes with updated positions to recalculate the distance square matrix and node relative coordinates according to steps 1) and 2).

[0189] Step 5) Using the anchor node coordinates and relative coordinate matrix, the positioning error function is substituted into the IDBO algorithm as the fitness function to obtain the optimal coordinate transformation matrix and calculate the absolute coordinates:

[0190] The calculation of absolute coordinates is as follows:

[0191]

[0192] Where (x 1 ,x 2 ), (y 1 ,y 2 ) are the relative coordinates and absolute coordinates of the anchor node, Δx 1 ,Δx 2is the translation vector, α and m are the rotation angle and control parameter respectively. In the process of coordinate transformation, the accuracy of the parameters will directly affect the positioning effect. In order to ensure the positioning accuracy, the best parameters should be calculated as much as possible.

[0193] Set the error function f(Δx 1 ,Δx 2 ,α,m) is used to measure the difference between the actual coordinates of the anchor nodes and the estimated results, and N is the number of anchor nodes.

[0194]

[0195] f(Δx 1 ,Δx 2 The smaller the value of f(Δx,α,m), the better the parameter optimization effect is and the smaller the final positioning error is. 1 ,Δx 2 ,α,m) as the fitness function and substitute it into the IDBO algorithm to solve argmin‖f(Δx 1 ,Δx 2 ,α,m)‖ to obtain the optimal coordinate transformation parameters to improve positioning accuracy.

[0196] Combined with the experimental data, Figure 3 The positioning system implementation diagram shown in the figure further illustrates the technical effect of the present invention:

[0197] 1. Experimental conditions:

[0198] The experimental environment is: area of ​​1000m 2 Continuous outdoor track and field areas on the left and right.

[0199] The hardware equipment is: an Intel 12700 Lenovo laptop; 5 car models, each of which is equipped with a UWB module and a GPS positioning module using the STM32F103T8U6 chip.

[0200] The software platform is: Windows 11 operating system and Keil uVision 5 software.

[0201] 2. Experimental content and results analysis:

[0202] The present invention and the prior art were compared in positioning root mean square error experiment, and the results are shown in Table 1.

[0203] Table 1. Error table of the method of the present invention and the existing positioning method

[0204]

[0205] The positioning error calculation formula is:

[0206]

[0207] Wherein, σ represents the error between the estimated position obtained by the present invention and the true position, (x R ,y R ) T represents the estimated position of the target node measured by the method of the present invention, (x T ,y T ) T Indicates the actual position of the target node.

[0208] It can be seen from the numerical expression given in Table 1 that the positioning error of the present invention is smaller than the positioning error of the prior art method when the OFDM signal-to-noise ratio is the same.

[0209] In summary, the present invention deploys BDS / GPS modules on nodes to obtain positioning information, and at the same time, realizes positioning correction of dynamic nodes through MDS-IDBO algorithm, making the positioning of dynamic nodes more accurate. Through the above methods, the positioning accuracy is improved, providing new ideas and methods for the research of future collaborative positioning algorithms.

[0210] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A vehicle cluster collaborative positioning method based on MDS-IDBO algorithm, characterized in that: The following steps are involved: Step 1: Build a multi-node collaborative positioning model; Step 2: Calculate relative coordinates using the MDS-MAP algorithm; Step 3: Use IDBO algorithm to correct the anchor node positioning; Step 4: Use the anchor nodes that have been positioned and corrected in step 3 to recalculate the distance square matrix and node relative coordinates according to steps 1 and 2; Step 5: Using the distance square matrix and the relative coordinates of the nodes, substitute the positioning error function into the IDBO algorithm as the fitness function to obtain the optimal coordinate transformation matrix and calculate the absolute coordinates.

2. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The step 1 specifically includes: Step 11: Multi-node collaborative positioning scenario, in which the nodes are equipped with BDS / GPS module anchors, the nodes are not fixed but in a mobile state, and the nodes are all equipped with communication equipment; Step 12: Build a multi-node collaborative positioning model: In the UWB system of time synchronization and networking ranging system, a total of N nodes are added to the network equipment, and communication is carried out in the k-dimensional space, k is selected as 2, and the node p i The coordinates of i =(x i ,y i ), the coordinate vector corresponding to the position information is X=[p1,p2,...,p n ] T , let d ij Represents the Euclidean distance value between nodes i and j, and all measured Euclidean distance values ​​form an N-dimensional symmetric distance matrix D; The Euclidean distance calculation formula is: The N-dimensional symmetric distance matrix D is:

3. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The step 2 specifically includes: assuming that the coordinates of all nodes are at the origin, setting the distance value between the nodes to the difference between the two, using the distance information to form a distance matrix, solving the relative coordinates of each node through linear transformation and singular value decomposition, and performing singular value decomposition on the centralized distance square matrix: The distance matrix Centering and calculating the inner product matrix yields: Among them, D em represents the mth Hadamard power of D, J is a central matrix and can be expressed as: Pair Matrix Perform singular value decomposition, retain two or three largest eigenvalues ​​and eigenvectors, and construct the relative coordinate X: Where Q represents the matrix composed of eigenvectors; Λ represents the diagonal matrix composed of corresponding eigenvalues.

4. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The step 3 specifically includes: Step 31: Calculate GDOP; Step 32: Substitute GDOP as the fitness function into the IDBO algorithm to improve the layout of the anchor nodes.

5. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 4, characterized in that: The step 31 specifically includes: Assume that there are N nodes in the area, including L anchor nodes and M nodes to be located. Among all these anchor node combinations, the anchor node combination that makes the wireless sensor network have the minimum geometric precision dilution GDOP can be found. Given the information of anchor nodes with known positions, the shortest estimated adjacent information matrix between the anchor nodes and the unknown nodes under the kth combination is: Assuming t=1,2,...,M,a=1,2,...,L,then the calculation of GDOP needs to use the direction cosines to form the geometric matrix G of the unknown nodes k for: in, and It is calculated by the node using the direction cosines on the x-axis and y-axis. and It is the initial estimated relative position coordinates of unknown nodes obtained by MDS; Using this matrix to calculate the GDOP of the network is defined as: in:

6. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 4, characterized in that: In step 32, GDOP is used as a fitness function to substitute into the IDBO algorithm to improve the layout of the anchor nodes, specifically including: Step 3.2.1: Population initialization, given initial parameters; Step 3.2.2: Calculate the fitness value of each individual through the existing objective function; Step 3.2.3: Update the individual's position; Step 3.2.4: Update the individual best position and the group best position, and determine whether the termination conditions are met. If so, stop and output the final result; if not, return to continue executing the algorithm.

7. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The step 3.2.1 specifically includes: (1) Use Tent Mapping to generate the initial positions x of N dung beetles i ; It should be noted that when Θ is 0.5, the value of x should avoid falling into (0.2, 0.4, 0.6, 0.8) to cause the system to present a short-period state, or falling into (0, 0.25, 0.5, 0.75) to cause a fixed point to appear later; (2) The N initial solutions x generated by the above formula i Generate the corresponding N reverse initial solutions nx according to the formula i ; nx i =rand(Ub-Lb)-x i ; Where Ub and Lb are the upper and lower limits of the current dung beetle location; (3) In solving the optimization problem of finding the minimum value, 2N x will be generated. i and nx i The function fitness values ​​of the dung beetle's location are arranged in order, and the first N values ​​are selected as the initial population.

8. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The steps 3.2.2 and 3.2.3 specifically include: (1) Rolling ball operator When the dung beetle moves forward without obstacles, the solution of its individual position update equation, that is, the fitness function, is as follows: x i (t+1)=x i (t)+α×k×x i (t-1)+b×Δx Δx=|x i (t)-X w |; Among them, t represents the current iteration number, x i (t) represents the position information of the i-th dung beetle in the t-th iteration, k∈(0,0.2] is the perturbation coefficient, b is a random number between (0,1), α is -1 or 1, X w represents the global worst position; When the dung beetle encounters an obstacle, it needs to adjust its direction and obtain a new route. The tangent function is used to obtain a new rolling direction, and the rolling direction is only considered to be between [0,π]. The position is updated as follows: x i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)|| Among them, θ is the deflection angle, and its value is [0,π]; (2) Reproduction operator Inspired by nature, a boundary selection strategy is proposed to simulate the area where female dung beetles lay eggs: Lb * =max(X * ×(1-R),Lb) Ub * =min(X * ×(1+R),Ub); Among them, X * Indicates the current optimal position, which is also the optimal solution in the region of the optimization problem. Lb * and Ub * Indicates the lower and upper limits of the spawning area, R = 1-t / T max ,T max represents the maximum number of iterations, Lb and Ub represent the lower and upper limits of the optimization problem; Once the female dung beetle selects an egg-laying area, it will produce a brood ball in the area to lay eggs; each female dung beetle only produces one egg in each iteration; the boundary range of the egg-laying area is dynamic and is mainly determined by the R value; therefore, the position of the brood ball is also dynamic during the iteration process, and is defined as follows: B i (t+1)=X * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * ); Among them, B i (t) represents the position information of the i-th brooding ball in the t-th iteration, b1 and b2 are both D-dimensional random vectors, and D represents the dimension of the optimization problem; (3) Foraging operator The eggs laid by the female dung beetle will gradually hatch into daughter dung beetles, which will come out from underground to look for food. The optimal foraging area is modeled as follows: Lb b =max(X b ×(1-R),Lb) Ub b =min(X b ×(1+R),Ub); Among them, X b represents the global optimal position, Lb b and Ub b denote the lower and upper boundaries of the optimal foraging area, respectively; The position of the dung beetle has been updated as follows: x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b ); Among them, x i (t) represents the position of the i-th dung beetle in the t-th iteration, C1 is a random number that follows a normal distribution, and C2 is a random vector of (0,1); (4) Stealing Operator The position of the dung beetle executing the stealing operator is updated as follows: x i (t+1)=X b +S×g×(x i (t)-X * |+x i (t)-X b ); Among them, x i (t) represents the position of the i-th thief in the t-th iteration, g is a D-dimensional random vector that follows a normal distribution, and S is a constant; In order to balance the algorithm's global optimization and local development capabilities, a dynamic weight strategy is introduced in the stealing operator. The formula is as follows: Among them, a1 is larger in the early stage of iteration, which increases the global optimization ability of the algorithm; a2 gradually increases in the later stage of iteration, so that the operator has the ability to jump out of the local optimum; After the improvement, the position of the dung beetle executing the stealing operator is updated as follows: x i (t+1)=a1×X b +a2×S×g×(x i (t)-X * |+x i (t)-X b )。 9. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The step 4 specifically includes: Use the updated anchor nodes to recalculate the distance square matrix and node relative coordinates according to steps 1 and 2.

10. The vehicle cluster collaborative positioning method based on the MDS-IDBO algorithm as claimed in claim 1, characterized in that: The step 5 specifically includes: Using the anchor node coordinates and relative coordinate matrix, the positioning error function is substituted into the IDBO algorithm as the fitness function to obtain the optimal coordinate transformation matrix and calculate the absolute coordinates: The absolute coordinate calculation is as follows: Where (x1, x2), (y1, y2) are the relative coordinates and absolute coordinates of the anchor node, Δx1, Δx2 are the translation vectors, α, m are the rotation angle and control parameters, respectively. In the coordinate transformation process, the accuracy of the parameters will directly affect the positioning effect. To ensure the positioning accuracy, the best parameters should be calculated as much as possible. The error function f(Δx1, Δx2, α, m) is set to measure the difference between the actual coordinates of the anchor node and the estimated results, where N is the number of anchor nodes; The smaller f(Δx1,Δx2,α,m) is, the better the parameter optimization effect is, and the smaller the final positioning error is. By substituting f(Δx1,Δx2,α,m) as the fitness function into the IDBO algorithm to solve argmin‖f(Δx1,Δx2,α,m)‖, the optimal coordinate transformation parameters are obtained to improve the positioning accuracy.

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