Research On Long Baseline Location Method Combining Sound Velocity Correction And Parallel Group Intelligent Algorithm

Underwater target localization is the basic premise of the development of Marine high-tech and Marine exploitation activities.Due to the propagation characteristics of sound waves underwater,underwater acoustic localization is the main means of underwater target localization and tracking.Among many underwater acoustic localization methods,Long Baseline localization is widely used for underwater high-precision operations due to its good localization performance.At present,most of the common Long Baseline localization solutions set the sound speed as a single sound speed value,ignoring the influence of the time-varying and space-varying characteristics of the sound speed profile on the positioning accuracy.At the same time,the conventional analytical method and iterative method have some errors in solving the nonlinear problem of Long Baseline localization in low signal-to-noise ratio environment.Therefore,this paper studies the sound speed correction and model solution method in the Long Baseline localization problem.Aiming at the problem of sound velocity correction in Long Baseline localization,this paper studies the sound velocity correction method based on effective sound velocity.On the basis of summarizing the spatial distribution law of effective sound velocity,this paper proposes an iterative estimation method of effective sound velocity based on BELLHOP.Simulation results show that the method has good convergence and accuracy of sound velocity estimation.Compared with several sound speed correction methods based on fixed sound speed and the linear interpolation effective sound speed look-up table method commonly used in engineering,the proposed method has higher accuracy of sound speed estimation.At present,the sound speed correction method based on the effective sound speed is basically limited to the premise that the target depth is known.For the scene of unknown target depth,this paper further improves the effective sound speed method,and proposes a method of effective sound speed table lookup under unknown target depth.The distance estimation of horizontal dimension is performed after matching to the optimal depth.Simulation results show that the proposed method can accurately estimate the depth and slant range of the targetAiming at the nonlinear problem of Long Baseline localization,this paper introduces swarm intelligence algorithm to solve it,and designs a Long Baseline localization method combining sound speed correction and parallel swarm intelligence algorithm.On the basis of the original swarm intelligence algorithm,the chaotic map was embedded to initialize it,so that the population individuals were evenly distributed in the solution space and the global convergence ability of the algorithm was improved.By introducing the Gauss-Cauchy mutation factor to the excellent individuals of the population,the global search ability in the early stage and the local development ability in the later stage of the algorithm were balanced.For the ordinary individuals of the population,the orthogonal opposition-based learning mechanism was introduced to make up for the defects of the traditional opposition-based learning,and enhance the global search ability and the ability to jump out of local optimal solutions.By establishing a parallel structure,the influence of random errors on the convergence results of the algorithm is reduced.For the specific application scenario of Long Baseline localization,a fitness function was established,and an effective sound velocity correction was introduced to jointly estimate the effective sound velocity and the target position.The simulation and actual experimental data show that the positioning accuracy of the proposed method is better than that of the traditional method in low SNR environment,and it can better realize the positioning of the target.Aiming at various data processing scenarios in Long Baseline positioning,a set of Long Baseline positioning data processing software is developed in this paper.The software follows the principle of portability and scalability,and based on the idea of modular function design,the required functions of each stage of the Long Baseline positioning experiment are developed.In the early stage of the Long Baseline localization test,the software function can provide guidance for the Long Baseline localization test.Software functions can provide visual data processing services in the late data processing stage of Long Baseline positioning trials.In this paper,the SWell Ex96 test data and Qiandao Lake test data are processed by using the software function,and the performance of the above algorithm and the function of the software are verified.