Research On The Theory And Application Oftarget Tracking Based On Knowledge

Nowadays,science and technology are developing rapidly,and the terrain and geomorphology are becoming more and more complex,which leads to higher and higher requirements for target tracking technology.The high clutter density,low signal-to-noise ratio,and complex target motion modes reduce the performance of traditional tracking based on location information.The development of technology has also driven information mining technology,which can extract more prior information and apply it to the target tracking process.The knowledge-based tracking technology integrates more useful information of the target,and optimizes the performance of the traditional tracking algorithm using only the target kinematic parameters for tracking.With the assistance of prior knowledge,the target tracking accuracy in the complex environment is improved.This paper mainly researches the tracking algorithm using amplitude information and introducing radial velocity,and applies the algorithm to the special phased array radar tracking system.The specific work is as follows:Firstly,the target tracking system is briefly described,including track management,data association and tracking filtering,and the commonly used algorithms in each part are introduced.It lays a theoretical foundation for the knowledge-based target tracking algorithm.Secondly,the amplitude likelihood ratios of Rayleigh clutter and K-distribution clutter are derived,and the amplitude information is applied to the traditional probabilistic data association(PDA)algorithm.Respectively in the background of Rayleigh clutter and K distribution clutter,the performance before and after the introduction of amplitude information is compared in the simulation.For the Gaussian background,the probabilistic data association algorithm based on Bayesian detection is researched,and the amplitude information is introduced on this basis.The simulation results show that the tracking performance of the proposed algorithm is better than that of the traditional tracking algorithm,and it is suitable for the clutter environment with low SNR.Then,the target tracking algorithm that can deal with radial velocity is researched.Compared with the traditional tracking algorithm which only uses position information,the algorithm researched can improve the target tracking accuracy.For data fusion tracking system for phased array radar seeker primary and passive radar,the tracking precision of multi-radar data fusion tracking and single-radar data tracking is compared in the simulation.And the tracking results before and after the radial velocity introduction are given and analyzed.The introduction of radial velocity in the fusion tracking system can improve the tracking accuracy,and the model probability switching under the interactive multi-model is more accurate.Finally,the motion model of the ballistic target is introduced in detail,and the workflow of the data processing of the large long-range early warning radar is simulated.The adaptive dwell scheduling algorithm is combined with the tracking algorithm that integrates amplitude information or radial velocity information to track the target.The simulation results are given in several scenarios and the performance of knowledge-based tracking and traditional algorithm is compared.