Research On Dynamic Target Localization Method Based On Underwater Sensor Network Node Collaboration

With the rapid development of underwater acoustic communication and underwater acoustic sensor technology,accurate location of underwater dynamic target has become a hot issue in the current research of underwater acoustic sensor network,which has a position that can not be ignored in the dual-use of military and civilian.Therefore,it is of certain application value to carry out research on underwater dynamic target positioning method.However,there are noise factors in the complex and changeable underwater environment,which makes it more difficult to locate the dynamic target.This paper makes full use of the advantages of distributed underwater acoustic sensor network nodes working together.From the perspective of improving positioning accuracy and saving energy consumption of sensor network,the nonlinear filtering optimization algorithm is designed to effectively integrate target information,so as to reduce the impact of measurement errors and noise,improve the positioning accuracy of underwater dynamic targets,and reduce the calculation amount of sensors.In this way,the network life is improved.Firstly,the special influence of different structures of underwater acoustic sensor network on target location is analyzed,and a model of underwater acoustic sensor network suitable for underwater dynamic target location is proposed.Considering the problem of improving the positioning accuracy of dynamic targets while saving computing resources and communication energy,the collaborative positioning process of nodes is designed by using the collaborative mode of underwater acoustic sensor network nodes.Then,in view of the inevitable measurement error of absolute positioning method,the two commonly used nonlinear filtering algorithms are compared and analyzed.The unscented Kalman filter algorithm was selected as the basis to improve the positioning accuracy.Secondly,due to the existence of noise in complex underwater environment and the error in the measurement process,an unscented Kalman information filtering algorithm based on maximum consistency is proposed.By combining the maximum consistency protocol with the unscented Kalman information filtering algorithm,the existing average consistent unscented Kalman filtering algorithm which can only achieve approximate consistency is improved.By integrating the measurement information of multiple sensor nodes into the proposed algorithm,more accurate target location estimation results are obtained.Simulation results show that the proposed algorithm is effective,and analyzed the node number of how many have any impact on the positioning accuracy..Finally,Because the sensor node is powered by the battery,the process of replacing the battery after the battery runs out is complicated and difficult.Therefore,a minimum point unscented Kalman filtering algorithm based on maximum consistency is proposed.By combining the minimum Sigma point set with the maximum consistency unscented Kalman filter algorithm,the traditional 2n+1 Sigma point calculation method is improved,and the localization accuracy of dynamic target and the robustness of node work are guaranteed on the basis of saving node energy.Simulation results show that the proposed algorithm is feasible,and analyzed the target motion characteristics and measuring noise level have any impact on the positioning accuracy.