Search

CN-121997712-A - Long baseline positioning array optimal deployment method, equipment and medium

CN121997712ACN 121997712 ACN121997712 ACN 121997712ACN-121997712-A

Abstract

The invention discloses a method, equipment and medium for optimizing and deploying a long baseline positioning array, which relate to the technical field of underwater positioning and comprise the steps of firstly establishing a TOA or TDOA observation equation between a target and a transponder, and constructing a dynamic range error model by combining with underwater sound propagation characteristics; the method comprises the steps of constructing a Fisher information matrix and inverting the Fisher information matrix to obtain a Kelarmilo lower bound representing a theoretical precision lower bound, generating visual prediction information of positioning precision spatial distribution by gridding a target area and calculating the lower bound point by point, finally constructing an optimization model based on the Kelarmilo lower bound by taking the position of a transponder as a decision variable with the positioning precision of the optimization area as a target, and solving an optimal deployment scheme by adopting an intelligent algorithm. The invention realizes the theoretical precision prediction before deployment and the automatic optimization of the array configuration, thereby improving the positioning performance and reducing the deployment cost and risk.

Inventors

  • CUI JUNHONG
  • WANG GUOLIN
  • ZHU JIFENG

Assignees

  • 云洋智海产业科技(深圳)有限公司

Dates

Publication Date
20260508
Application Date
20251225

Claims (10)

  1. 1. The method for optimally deploying the long baseline positioning array is characterized by comprising the following steps of: Aiming at a long baseline positioning system comprising a plurality of transponders, an observation equation between a target position and the transponder position is established in a target area, a dynamic range error model which changes with distance and environment is established based on a water sound propagation model, and the observation equation is an arrival time TOA observation equation or an arrival time difference TDOA observation equation; constructing a Fisher information matrix for representing the positioning parameter estimation precision based on the observation equation and the dynamic range error model; obtaining a Kramer lower bound of an estimated error of the target position by solving an inverse matrix of the Fisher information matrix; Generating prediction information reflecting the spatial distribution of positioning accuracy by gridding a target area and calculating the lower boundary of the Kramer of each grid point; and taking the positioning precision in the optimization target area as a target, taking the deployment position of the transponder as a decision variable, constructing and solving an optimization problem based on the Kramer lower bound, and obtaining an optimized transponder deployment scheme.
  2. 2. The method of claim 1, wherein the ranging error variance of the dynamic ranging error model is a sum of a signal-to-noise ratio based ranging error variance and a sound velocity error based ranging error variance, The standard deviation of the ranging error based on the signal to noise ratio is according to the formula Calculating, wherein sigma t is a ranging error standard deviation based on a signal-to-noise ratio, c is sound velocity, B is signal bandwidth, and SNR is a receiving signal-to-noise ratio; The received signal-to-noise ratio SNR is calculated according to a sonar equation SNR = SL-TL-NL+DI-DT, wherein SL is a sound source level, TL is propagation loss, NL is an ambient noise level, DI is a receiving array directivity gain, and DT is a detection threshold; The propagation loss TL is calculated according to the formula Calculating, wherein K is an expansion type coefficient, r is a propagation distance, and alpha is an absorption coefficient; The ambient noise level NL is determined based on the marine ambient noise spectrum model.
  3. 3. The method of claim 2, wherein the variance of range error based on speed of sound error is according to the formula And (3) calculating, wherein sigma c is the standard deviation of the distance measurement error based on the sound velocity error, t is the signal propagation time, Is the standard deviation of the sound velocity measurement error.
  4. 4. The method for optimizing deployment of long baseline positioning arrays according to claim 1, the method is characterized in that the process for constructing the Fisher information matrix comprises the following steps: for TOA observation mode, according to the formula Calculating a Fisher information matrix, wherein N is the number of transponders, sigma i 2 is the range error variance of the ith transponder, and g i is the directional cosine vector pointing from the target to the ith transponder; for the TDOA observation mode, the first transponder is taken as a reference transponder according to the formula A fischer information matrix is calculated, wherein σ i1 2 is the error variance of the ranging difference between the ith transponder and the reference transponder.
  5. 5. The method for optimizing deployment of long baseline positioning matrix according to claim 1, wherein the lower bound of the cladmerol is a positioning error covariance matrix, and the standard deviation of horizontal positioning accuracy is according to the formula And calculating, wherein sigma xy is a horizontal positioning accuracy standard deviation, and sigma x 2 and sigma y 2 are an east position estimation error variance and a north position estimation error variance extracted from the Clamet lower bound matrix respectively.
  6. 6. The method for optimizing and deploying a long baseline positioning array according to claim 1, wherein the optimization problem is expressed as searching a set P of position coordinates of a transponder to minimize a statistical index J (P) based on the lower bound of the Kramer on all grid points in a target area omega and meet P E D, wherein D is a preset feasible deployment area, and the statistical index J (P) is an average value or a maximum value of standard deviation of horizontal positioning accuracy of all grid points in the target area omega.
  7. 7. The method of claim 6, wherein the optimization problem is solved by a particle swarm optimization algorithm, wherein the position vector of each particle encodes the coordinates of all transponders, and wherein in the iterative process, the speed v i,d and the position x i,d of the particle in the d-th dimension at the kth iteration are updated according to the following formula: Wherein w is the inertial weight, c 1 and c 2 are learning factors, For the component of the individual historic optimal position of the particles in the d-th dimension, For the component of the global history optimal position of the group in the d-th dimension, r 1 、r 2 is a random function.
  8. 8. The method for optimizing deployment of a long baseline positioning matrix according to claim 7, wherein the inertial weight w adopts a linear decreasing strategy according to the formula And calculating, wherein w max and w min are respectively preset maximum and minimum inertia weights, K is the current iteration number, and K max is the maximum iteration number.
  9. 9. A computer device, characterized in that it comprises a memory on which a computer program is stored and a processor which, when executing the computer program, implements the method according to any of claims 1-8.
  10. 10. A computer readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any of claims 1-8.

Description

Long baseline positioning array optimal deployment method, equipment and medium Technical Field The invention relates to the technical field of underwater positioning, in particular to a long baseline positioning array optimizing deployment method, equipment and medium. Background A long baseline positioning system in the technical field of underwater acoustic positioning generally realizes high-precision positioning by arranging a transponder array on the seabed and performing ranging by utilizing acoustic signals between the array and a target. At present, the actual deployment work of a long baseline positioning array depends greatly on the practical experience of engineering personnel or adopts simple geometric rules such as square, triangle and the like to carry out layout. The method lacks a prejudgment means for quantifying and theorizing the reachable positioning accuracy of the system before deployment. In the ranging mode, the arrival time and the arrival time difference are two common techniques, and the ranging estimation accuracy is comprehensively influenced by various complex factors such as array geometric configuration, underwater sound ray propagation error, signal to noise ratio and the like. The prior art has mainly the following limitations due to the above-mentioned current situation. Firstly, the array deployment process is strong in experience and lacks quantitative theoretical basis. Often, the actual positioning accuracy can be obtained after deployment is completed and actual measurement is carried out. Once the accuracy is not satisfactory, subsequent adjustments will result in extremely high time and economic costs. Secondly, prior to physical deployment, an effective method is lacking to accurately predict and compare the theoretical optimal precision achieved by different array types in a specific working area, so that scheme selection is lack of foresight. Further, there is also a lack of systematic optimization methods directed at theoretical performance boundaries for how to purposefully improve the positioning accuracy of a particular region of interest by adjusting the specific location of the transponder. In addition, for specific application scenarios, it is difficult to determine whether to select the arrival time mode or the arrival time difference mode before deployment, so that superior positioning accuracy can be obtained. Disclosure of Invention The embodiment of the invention provides a long baseline positioning array optimizing deployment method, equipment and medium, which aim to solve the technical problem of providing an effective solution for carrying out theoretical prediction and quantitative evaluation on the positioning accuracy of a long baseline positioning system in a specific water area before actual seabed deployment of the long baseline positioning system and guiding array configuration optimization. In a first aspect, an embodiment of the present invention provides a method for optimizing deployment of a long baseline positioning array, including: Aiming at a long baseline positioning system comprising a plurality of transponders, an observation equation between a target position and the transponder position is established in a target area, a dynamic range error model which changes with distance and environment is established based on a water sound propagation model, and the observation equation is an arrival time TOA observation equation or an arrival time difference TDOA observation equation; constructing a Fisher information matrix for representing the positioning parameter estimation precision based on the observation equation and the dynamic range error model; obtaining a Kramer lower bound of an estimated error of the target position by solving an inverse matrix of the Fisher information matrix; Generating prediction information reflecting the spatial distribution of positioning accuracy by gridding a target area and calculating the lower boundary of the Kramer of each grid point; and taking the positioning precision in the optimization target area as a target, taking the deployment position of the transponder as a decision variable, constructing and solving an optimization problem based on the Kramer lower bound, and obtaining an optimized transponder deployment scheme. Optionally, the ranging error variance of the dynamic ranging error model is the sum of the ranging error variance based on signal-to-noise ratio and the ranging error variance based on sound velocity error, wherein, The standard deviation of the ranging error based on the signal to noise ratio is according to the formulaCalculating, wherein sigma t is a ranging error standard deviation based on a signal-to-noise ratio, c is sound velocity, B is signal bandwidth, and SNR is a receiving signal-to-noise ratio; The received signal-to-noise ratio SNR is calculated according to a sonar equation SNR = SL-TL-NL+DI-DT, wherein SL is a sound source level, TL is propagation loss, NL i