Research On Adaptive Management Of Radar Resource For Maneuvering Target Tracking

With the modern battlefield environment becoming more and more complex,phased array radar need to operate more tasks as it becomes an important part of the modern war.It is meaningful to study the research on adaptive resource management to control the radar parameters,allocate radar resources to meet the tracking accuracy,improve the efficiency of radar usage,perform more tasks as much as possible and achieve a variety of tactical functions.The time resource adaptive management problem is investigated to find an optimal sampling strategy to allocate the radar resources reasonably and efficiently.The main research works are summarized as follows:Firstly,the background and related research status of this research are summarized,and the related basic theory knowledge is described briefly.Secondly,several typical adaptive sampling period strategies are analyzed,and an improved predictive covariance threshold method is proposed to deal with the problem of large computation complexity and low efficiency.The algorithm improves the traditional sampling period of the whole traversal optimization strategy,and the proposed improved algorithm and other algorithms for simulation.The simulation results show that the improved algorithm can meet the tracking requirements in the target tracking process,with less computation,higher operating efficiency.Thirdly,a phased array radar sampling period adaptive algorithm based on off-line Riccati equation is proposed,which is based on the Riccati equation for the steady state resource management of the phased array radar.The algorithm through the Riccati equation to calculate the optimal model of the off-line sampling period,and then use the interactive multiple model updating probability of each model optimal sampling period mixed estimation,adaptive sampling period system.Algorithm advantage is reflected in the calculation of a certain work offline processing,online mixed estimation,so the efficiency of the algorithm is relatively high,can save computing resources.Then the proposed algorithm is compared with other adaptive sampling period algorithm.The simulation results show that the proposed algorithm can achieve the desired results.Finally,the paper combines the mixed grid multi-model algorithm based on the "current" statistical model with the improved predictive covariance threshold methodto deal with the problem that the target's maneuverability is more complex in the actual target tracking.A sampling period adaptive algorithm based on hybrid grid multiple model is proposed.This algorithm not only can track the target more accurately,but also can adjust the sampling period adaptively and save the radar resource.The effectiveness of the proposed algorithm is proved by simulation comparison.