Research On Tracking Technology Of Underwater Target Based On Bearing And Time Delay Measurement

With the world gradually realize the importance of maritime rights and interests,underwater target tracking technology has become an important research field.Due to the improvement of underwater concealment technology such as submarine,the performance of passive detection system is required to be higher.Therefore,active sonar systems are widely used in civil products and other civilian industry product due to their controllable gain and high detection accuracy.In sonar system based on bearing and time delay measurement information,the motion state of the measurement equation relative to the target is nonlinear,common linear filtering algorithms cannot directly achieve target tracking,and in the noise interfered environment,the measurement data set needs to be processed by data association.Under this background,the underwater target technology based on bearing and time delay measurement is studied in this paper.Firstly,the motion model and measurement model of the target are constructed based on the sonar system with bearing and time delay measurement information.The filtering effects of Extended Kalman Filter(EKF),Conversion Measurement Kalman Filter(CMKF)and Unscented Kalman Filter(UKF)are compared and analyzed,and the advantages of choosing EKF as nonlinear filter are proved.The tracking performance of Nearest Neighbor Data Association(NNDA)and Probabilistic Data Association(PDA)is simulated and compared under the condition of measurement set with clutter interference.The Interactive Multiple Model-Probabilistic Data Association(IMM-PDA)maneuvering target tracking in clutter environment is simulated and compared with the filtering algorithm based on linear motion model.Secondly,the multi-target tracking technology based on bearing and time delay measurement information is studied.Joint Probabilistic Data Association(JPDA)and Gaussian Mixture Probability Hypothesis Density(GMPHD)are simulated to compare the tracking performance,advantages and disadvantages of the algorithms.Optimal Sub-patten Assignment(OSPA)was used to evaluate the multi-target tracking performance.The tracking errors and running time of the two tracking algorithms are analyzed under the condition that the number of multi-target motion is known.In addition,the estimation and tracking performance of GMPHD algorithm for the number of motions of different trajectories are analyzed.Finally,aiming at the problem that target tracking performance decreases when unknown excitation and measurement noise covariance,a target tracking algorithm based on Variational Bayesian is proposed.System model is combined with the Variational Bayesian theory to complete the theoretical derivation of the optimization algorithm,and the iterative convergence and stability of the optimization algorithm are simulated and analyzed.The simulation analyses the tracking effect of combining the optimization algorithm with PDA and GMPHD algorithm.The results show that the optimization algorithm can effectively achieve the estimation of the covariance of excitation and measurement noise,and obtain the tracking trajectory with higher accuracy.By combining doppler information of echo signal,particle swarm optimization algorithm is used to estimate vector velocity of target,and Covariance Convex is fused with filter estimation state to obtain higher track quality.