An underwater target localization method based on ranging error matching
By constructing a ranging error probability model and fitness function in a traditional long baseline positioning system, and using a particle swarm optimization algorithm to correct the positioning results, the problem of ranging error influence in the marine environment is solved, and high-precision underwater target positioning is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA STATE SHIPBUILDING CORP LTD
- Filing Date
- 2023-05-17
- Publication Date
- 2026-05-26
AI Technical Summary
Traditional long baseline positioning systems suffer from large ranging errors and reduced positioning accuracy in marine environments due to the time delay of underwater acoustic signal propagation caused by sea surface and seabed reflections. Existing methods are also unable to improve positioning accuracy when real-time sound velocity profile information is unknown.
A positioning method based on ranging error matching is adopted. By constructing a ranging error probability model and a fitness function, the particle swarm optimization algorithm is used to correct the initial positioning results and improve the positioning accuracy.
In situations where environmental information is unknown, by constructing a ranging error model and a fitness function, and using a particle swarm optimization algorithm to correct the positioning results, the positioning accuracy is significantly improved and the positioning error is reduced.
Smart Images
Figure CN116660911B_ABST
Abstract
Description
Technical fields:
[0001] This invention belongs to the field of underwater target acoustic positioning technology, specifically relating to an underwater target positioning method based on ranging error matching. Background technology:
[0002] Underwater acoustic positioning systems typically consist of multiple positioning base stations connected by a baseline. Based on baseline length, they can be categorized into long baseline systems, short baseline systems, and ultra-short baseline systems. Among these, long baseline positioning systems are characterized by their long operating range and high positioning accuracy, and are widely used in various fields of marine operations.
[0003] In traditional long baseline positioning systems, the target emits an underwater acoustic positioning signal. After multiple positioning base stations receive the signal, they analyze the propagation delay of the signal, multiply the delay by the speed of sound to obtain the distance, and then use a least squares algorithm combined with a spatial intersection model to estimate the target's position. However, due to the presence of upper and lower boundaries between the sea surface and seabed in the marine environment, the underwater acoustic signal is reflected by the sea surface and seabed during propagation. This affects the measurement results of the underwater acoustic signal propagation delay, increases ranging errors, and consequently affects positioning accuracy.
[0004] In existing technologies, methods such as effective sound velocity tables and ray fitting are commonly used to correct ranging results. However, these methods all rely on acquiring real-time sound velocity profile information and then using ray propagation theory to fit the distance corresponding to the current propagation delay through a search algorithm. But the marine environment is complex and variable, making it difficult to obtain real-time sound velocity profile information, which affects the applicability and accuracy of existing technologies. Summary of the Invention:
[0005] This invention addresses the problem of improving the positioning accuracy of long baseline systems in situations where environmental information is unknown. It proposes an underwater target long baseline positioning method based on ranging error matching, which can improve the positioning accuracy of underwater long baseline systems even when sound velocity profile information is unknown.
[0006] The technical solution of this invention is to provide an underwater target long baseline positioning method based on ranging error matching, comprising the following steps:
[0007] Step 1: Utilize the traditional long baseline positioning method combined with a spatial intersection model to calculate the initial position of the target to be located. Preferably, in this step, the initial position of the target is calculated by having the target emit a positioning signal, and after multiple positioning base stations receive the signal, analyzing the positioning signal propagation time t. i ,i∈[1,...,N], where N is the number of positioning base stations, and the estimated distance d between the base station and the target to be located is... i =t i*c, where c is a fixed speed of sound. Establish a system of N ranging equations. Solve the system of equations to determine the position (x, y) of the target to be located, where (x... i ,y i ,z i )i∈[1,N] represents the location of different positioning base stations, and z represents the target depth, which can be directly obtained by the depth sensor.
[0008]
[0009] Step 2: Based on the underwater acoustic ranging results, construct a ranging error probability model. Preferably, in this step, the ranging error probability model is a Gaussian probability model, but the mean and standard deviation of the error are determined by the measured distance d between the positioning base station and the target. i The decision, specifically expressed as follows:
[0010]
[0011] Wherein, where ε i σ is the mean ranging error between the target and the i-th positioning base station. i It is the standard deviation of the ranging error between the positioning target and the i-th positioning base station;
[0012] Step 3: Establish a ranging error probabilistic model and construct a ranging error fitness function. Preferably, in this step, the ranging error fitness function is:
[0013]
[0014] Where e i_s The estimated ranging error represents the positioning result (x). s ,y s The distance to the i-th buoy and the actual ranging result d i Errors between them.
[0015] Step 4: Correct the initial positioning position using a positioning algorithm based on ranging error matching, and output the positioning result. Preferably, the positioning algorithm based on ranging error matching in this step is implemented based on particle swarm optimization algorithm, and specifically includes the following steps:
[0016] Step 1) Based on the initial positioning results calculated by the traditional long baseline method, initialize the particle positions. The total number of particles is M. The initial particle positions are randomly distributed near the initial positioning results. Set the number of iterations k = 1.
[0017] Step 2) Calculate the fitness function of all particles based on the ranging error fitness function, take the initial position of each particle as its individual historical best value, and calculate and save the individual historical best fitness function P. best and corresponding position (x) s _p ,y s _ p ), calculate the fitness function value G of the globally optimal particle. best and corresponding position (x) s_g ,y s_g The specific calculation formula is as follows:
[0018]
[0019] Where e i_t Let x be the ranging error of the current estimated position of the t-th particle, and let x represent the position of the t-th particle in the k-th iteration (x). t_k ,y t_k The distance to the i-th buoy and the actual distance measurement result d i Error between;
[0020] Step 3) Determine if the particle swarm has converged. If it has converged, output the position (x). s_g ,y s_g If the corrected localization result does not converge, proceed to step 4.
[0021] Step 4) Update the particle's position and velocity using the following formula:
[0022]
[0023] Where w is the inertial weight, c1 and c2 are the acceleration constants, and r i i∈[1,2,3,4] is a random number between 0 and 1.
[0024] Step 5) Calculate the fitness function of the new particle. The specific calculation formula is as follows:
[0025]
[0026] Step 6) Calculate and save the individual's historical best fitness function P best and corresponding position (x) s_p ,y s_p ), calculate and save the fitness function value G of the globally optimal particle. best With the corresponding position (x) s_g ,y s_g The calculation formula is shown in equation (7).
[0027]
[0028] Among them, (x t_o ,y t_o (x) represents the position of the t-th particle after the o-th iteration. t_k+1 ,y t_k+1 () represents the position of the t-th particle after the (k+1)-th iteration;
[0029] Step 7) Set the iteration count k = k + 1, then go to step 3).
[0030] Furthermore, the method for determining whether the particle swarm has converged in step 3) is to compare it with the currently output global optimal fitness function value V. best (t) is compared with the global optimal fitness function value of the previous 30 iterations. If they are exactly the same, the particle swarm is considered to have converged; otherwise, it is considered not to have converged.
[0031] Compared with the prior art, the present invention has the following advantages:
[0032] This method fully utilizes the positive correlation between underwater acoustic ranging error and actual distance when real-time environmental information is unknown. It constructs a ranging error fitness function to correct the traditional long baseline positioning results and improve positioning accuracy. Attached image description:
[0033] Figure 1 This is a flowchart illustrating the implementation of the method proposed in this invention;
[0034] Figure 2 This is a schematic diagram of a simulation scene;
[0035] Figure 3 This is a schematic diagram illustrating the iterative changes of the fitness function.
[0036] Figure 4 This is a schematic diagram illustrating the iterative changes in ranging error;
[0037] Figure 5 A comparison of the localization results between traditional long baseline system methods and the method proposed in this paper;
[0038] Figure 6 for Figure 5 A magnified schematic diagram;
[0039] Figure 7 The simulation results of positioning errors are compared between the traditional long baseline method and the method proposed in this paper. Detailed implementation method:
[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:
[0041] An underwater target localization method based on ranging error matching includes the following steps:
[0042] Step 1: Calculate the initial position of the target to be located using the traditional long baseline positioning method combined with a spatial intersection model;
[0043] Step 2: Combine the underwater acoustic ranging results to construct a ranging error probability model;
[0044] Step 3: Ranging error probability model and constructing the ranging error fitness function;
[0045] Step 4: Correct the initial positioning position using a positioning algorithm based on ranging error matching, and output the positioning result.
[0046] For detailed implementation procedures, please refer to Figure 1 The process includes the following steps: First, the initial position of the target to be located is calculated using the traditional long baseline positioning method; then, based on the real-time ranging results, a ranging error probability and fitness function model is constructed; finally, the initial positioning position is corrected using a positioning algorithm based on ranging error matching, and a high-precision positioning result is output. In this embodiment, the method for calculating the initial position of the target to be located in step 1 involves the target transmitting a positioning signal. After multiple positioning base stations receive the positioning signal, they analyze the positioning signal propagation time t... i ,i∈[1,...,N], where N is the number of positioning base stations, and the estimated distance d between the base station and the target to be located is... i =t i *c, where c is a fixed speed of sound. Establish a system of N ranging equations. Solve the system of equations to determine the position (x, y) of the target to be located, where (x... i ,y i ,z i )i∈[1,N] represents the location of different positioning base stations, and z represents the target depth, which can be directly obtained by the depth sensor.
[0047] The ranging error probability model in step 2 is a Gaussian probability model, but the mean and standard deviation of the error are determined by the measured distance d between the positioning base station and the target. i The decision, specifically expressed as follows:
[0048]
[0049] The localization algorithm based on ranging error matching in step 4 is implemented using the particle swarm optimization algorithm, and specifically includes the following steps:
[0050] Step 1) Based on the initial positioning results calculated by the traditional long baseline method, initialize the particle positions. The total number of particles is M. The initial particle positions are randomly distributed near the initial positioning results. Set the number of iterations k = 1.
[0051] Step 2) Calculate the fitness function of all particles based on the ranging error fitness function, take the initial position of each particle as its individual historical best value, and calculate and save the individual historical best fitness function P. best and corresponding position (x) s _ p ,y s _ p ), calculate the fitness function value G of the globally optimal particle.best and corresponding position (x) s_g ,y s_g The specific calculation formula is as follows:
[0052]
[0053] Step 3) Determine if the particle swarm has converged. If it has converged, output the position (x). s_g ,y s_g If the corrected localization result does not converge, proceed to step 4.
[0054] Step 4) Update the particle's position and velocity using the following formula:
[0055]
[0056] Where w is the inertial weight, c1 and c2 are the acceleration constants, and r i i∈[1,2,3,4] is a random number between 0 and 1.
[0057] Step 5) Calculate the fitness function of the new particle. The specific calculation formula is as follows:
[0058]
[0059] Step 6) Calculate and save the individual's historical best fitness function P best and corresponding position (x) s_p ,y s_p ), calculate and save the fitness function value G of the globally optimal particle. best With the corresponding position (x) s_g ,y s_g The calculation formula is shown in equation (7).
[0060]
[0061] Step 7) Set the iteration count k = k + 1, then go to step 3).
[0062] Furthermore, the method for determining whether the particle swarm has converged in step 3) is to compare it with the currently output global optimal fitness function value V. best (t) is compared with the global optimal fitness function value of the previous 30 iterations. If they are exactly the same, the particle swarm is considered to have converged; otherwise, it is considered not to have converged.
[0063] Figure 2This is a schematic diagram of the simulation scenario for this embodiment, showing four positioning base stations and one underwater node to be positioned. In the simulation scenario, the four positioning base stations are deployed in a square topology, with a side length of 6 km and a deployment depth of 5 m. The underwater node is at a depth of 50 m and moves horizontally along a parabolic trajectory, with its starting coordinates at (-3000 m, -1175 m), inflection point coordinates at (500 m, 2500 m), and ending point coordinates at (3000 m, 625 m). The positioning cycle of the underwater node is 10 seconds, the underwater node's movement lasts for 6000 seconds, and the positioning process is repeated 600 times.
[0064] The following analysis examines the positioning results of the underwater target at (-2960m, -1091.5m). Figure 1 The initial positioning result using the traditional long baseline method is (-2986.2m, -1100.6m), with an initial positioning error of 27.7m, marked by the × symbol. The positioning result is then corrected according to the particle swarm optimization algorithm proposed in this invention. First, a Gaussian ranging error model is established based on the long baseline ranging result and formula (2). Then, the particle swarm parameters are initialized, with inertia weight w set to 1, acceleration constants c1 and c2 both set to 1.5, and the particle swarm size M set to 200. Initial particle positions are randomly generated near the initial positioning result. The global optimal particle for the first iteration is calculated according to formula (4), and the corresponding particle position is (-2982.5m, -1098.6m), with a positioning error of 23.6m.
[0065] Then, multiple iterations are performed according to the method of this invention. Figure 3 The graph shows the correspondence between the number of iterations and the global optimal fitness function value. It can be seen that as the number of iterations increases, the global optimal fitness value increases sequentially. When the iteration reaches 10, the global optimal fitness value reaches its maximum value. At this time, the corresponding global optimal particle position is (-2966.5m, -1095.2m), and the corresponding ranging error is 7.5m.
[0066] Figure 4 To illustrate the relationship between the number of iterations and the iteration error, the initial positioning error was 27.7m, which decreased to 7.5m in the tenth iteration. In the fortieth iteration, since the global optimal fitness function value did not change, the convergence condition was met, and the algorithm ended, outputting the corrected positioning results (-2966.5m, -1095.2m).
[0067] The following section analyzes the positioning process of the underwater node throughout its entire movement. Monte Carlo simulation was used, with 300 simulations performed. Gaussian ranging error was added for each simulation based on the actual distance. The positioning results from the 300 simulations were then averaged to analyze the positioning error. Figure 5The simulation results of the traditional long baseline method and the method of this invention are compared, and it can be seen that both methods can estimate the target position according to the underwater node motion trajectory.
[0068] Figure 6 for Figure 5 The magnified results near (-1360m, 1460m) show that, compared with the traditional long baseline method, the underwater node position estimated by the method of this invention is closer to the actual position and the positioning error is smaller.
[0069] Figure 7 The results show the positioning error of the underwater node at different times (different locations). It can be seen that the positioning error of the traditional long baseline method is greater than 25m, while the positioning error of the method proposed in this invention is around 4m. The positioning error is significantly reduced, which verifies the effectiveness of the method of this invention.
[0070] In other words, the advantage of the method of the present invention is that, in the case of unknown real-time environmental information, by utilizing the positive correlation between underwater acoustic ranging error and the actual distance, a ranging error probability model and fitness function are constructed to correct the traditional long baseline positioning results, thereby improving the positioning accuracy.
[0071] The above description only illustrates preferred embodiments of the present invention and should not be construed as limiting the scope of the claims. Any equivalent procedural modifications made using this specification are included within the patent protection scope of this invention.
Claims
1. An underwater target localization method based on ranging error matching, characterized in that: Includes the following steps, Step 1: Calculate the initial position of the target to be located using the traditional long baseline positioning method combined with a spatial intersection model; Step 2: Combine the underwater acoustic ranging results to construct a ranging error probability model; Step 3: Ranging error probability model and constructing the ranging error fitness function; Step 4: Correct the initial positioning position using a positioning algorithm based on ranging error matching, and output the positioning result; In step 2, the ranging error probability model is a Gaussian probability model, and its mean and standard deviation are determined by the measured distance between the positioning base station and the target. The decision, specifically expressed as follows: Among them, Is it the positioning target and the first Mean distance measurement error between positioning base stations Is it the positioning target and the first Standard deviation of ranging error between positioning base stations; In step 3, the ranging error fitness function is: in The estimated ranging error represents the positioning result. To the The distance of each buoy and the actual distance measurement result Error between; In step 4, the positioning algorithm for ranging error matching includes the following steps: Step 1), based on the initial positioning results calculated using the traditional long baseline method, initialize the particle positions, with a total number of particles of [value missing]. The initial particle positions are randomly distributed near the initial positioning result, and the number of iterations is set. ; Step 2): Based on the ranging error fitness function, calculate the fitness function of all particles, taking the initial position of each particle as its individual historical best value, and calculate and save the individual historical best fitness function. and corresponding position Calculate the fitness function value of the globally optimal particle. and corresponding position The specific calculation formula is as follows: in For the first The ranging error of the current estimated position of the i-th particle represents the distance error of the i-th particle. The first particle Next iteration position To the The distance between the buoys and the actual distance measurement results Error between; Step 3) Determine if the particle swarm has converged. If it has converged, output the position. If the corrected localization result does not converge, proceed to step 4). Step 4), update the particle's position and velocity, using the following formula: in, For inertial weights, , The acceleration constant, A random number between 0 and 1; Step 5), calculate the fitness function of the new particle, the specific calculation formula is as follows: Step 6) Calculate and save the individual's historical best fitness function. and corresponding position Calculate and save the fitness function value of the globally optimal particle. and corresponding position The calculation formula is shown below. in, Indicates the first The particle in the first Position after the next iteration For the first The particle in the first The position after the next iteration; Step 7), set the number of iterations. Proceed to step 3). The method for determining whether the particle swarm has converged is to compare it with the currently output global optimal fitness function value. The value of the global optimal fitness function is compared with that of the previous 30 iterations. If they are exactly the same, the particle swarm is considered to have converged; otherwise, it is considered not to have converged.
2. The underwater target localization method based on ranging error matching according to claim 1, characterized in that: In step 1, the traditional long baseline positioning method is as follows: the target to be located transmits a positioning signal, and after multiple positioning base stations receive the positioning signal, they analyze the propagation time of the positioning signal. , To determine the number of base stations, estimate the distance between them and the target to be located. , To maintain a constant speed of sound, establish A set of ranging equations is used to solve for the position of the target to be located. .