机动目标跟踪下雷达资源自适应管理研究

发布时间：2018-06-15 14:34

本文选题：相控阵雷达 + 资源管理　；参考：《杭州电子科技大学》2017年硕士论文

【摘要】：随着现代战场环境的日益复杂,相控阵雷达作为现代战争的重要组成部分,需要承担更多的任务,因此对雷达自适应资源管理研究就很有意义。其目的就是通过控制雷达参数,在满足跟踪精度的情况下,合理地分配雷达资源,提高雷达使用效率,使其尽可能执行更多的任务,实现多种战术功能。本文针对时间资源自适应管理问题深入研究,旨在找出一种优化的采样策略合理地分配雷达资源,提高雷达的使用效率。主要的研究工作概括如下:首先概述了本文的研究背景、意义及相关研究现状,并简述了相关基础理论知识。其次,分析了几种常用的采样周期自适应策略,针对传统预测协方差门限法运算量大、算法效率低的问题,提出了一种改进的预测协方差门限法。该算法改进传统采样周期的全遍历寻优策略,并将提出的改进算法与其他算法进行仿真比较。仿真结果表明,改进算法在目标跟踪过程中能满足跟踪需求,具有较少的计算量,较高的运行效率。再次,从基于Riccati方程的相控阵雷达稳态资源管理的角度出发,提出了一种基于离线Riccati方程的相控阵雷达采样周期自适应算法。该算法通过Riccati方程离线计算出各模型的最优采样周期,然后利用交互式多模型更新概率对各模型最优采样周期进行混合估计,得到系统的自适应采样周期。算法优势体现在将一定的计算工作离线处理,只在线混合估计,所以算法的效率比较高,能节省计算机运算资源。然后将提出算法与其他自适应采样周期算法进行仿真比较,仿真结果表明,所提算法能达到预期效果。最后,针对实际目标跟踪中目标的机动性较复杂的问题,在上述研究的基础上,将基于“当前”统计模型的混合网格多模型算法与改进的预测协方差门限法相结合,提出了一种混合网格多模型的采样周期自适应算法。该算法不仅能够对目标进行更精确的跟踪,而且还能自适应调整采样周期,节省雷达资源。最后通过仿真比较,证明了提出算法的有效性。
[Abstract]:With the increasing complexity of modern battlefield environment, phased array radar, as an important part of modern warfare, needs to undertake more tasks, so the research on radar adaptive resource management is of great significance. The aim is to reasonably allocate radar resources, improve the efficiency of radar use, perform as many tasks as possible and realize various tactical functions by controlling radar parameters and satisfying tracking accuracy. In this paper, the adaptive management of time resources is studied in order to find out an optimal sampling strategy to allocate radar resources reasonably and improve the efficiency of radar. The main research work is summarized as follows: firstly, the research background, significance and related research status are summarized, and the basic theoretical knowledge is briefly described. Secondly, several commonly used sampling period adaptive strategies are analyzed, and an improved predictive covariance threshold method is proposed to solve the problems of large computation and low efficiency of the traditional predictive covariance threshold method. This algorithm improves the whole ergodic optimization strategy of the traditional sampling period, and compares the proposed algorithm with other algorithms. The simulation results show that the improved algorithm can meet the requirements of target tracking with less computation and higher running efficiency. Thirdly, from the point of view of steady state resource management of phased array radar based on Riccati equation, an adaptive algorithm for sampling period of phased array radar based on off-line Riccati equation is proposed. The optimal sampling period of each model is calculated off-line by Riccati equation, then the optimal sampling period of each model is estimated by using interactive multi-model update probability, and the adaptive sampling period of the system is obtained. The advantage of the algorithm lies in the off-line processing of a certain amount of computing work, only on-line mixed estimation, so the efficiency of the algorithm is relatively high, and the computer computing resources can be saved. Then the proposed algorithm is compared with other adaptive sampling cycle algorithms. The simulation results show that the proposed algorithm can achieve the desired results. Finally, aiming at the complex problem of target mobility in actual target tracking, the hybrid grid multi-model algorithm based on the "current" statistical model is combined with the improved predictive covariance threshold method on the basis of the above research. In this paper, a sampling period adaptive algorithm for hybrid mesh multimodel is proposed. The algorithm not only can track the target more accurately, but also adaptively adjusts the sampling period and saves radar resources. Finally, the effectiveness of the proposed algorithm is proved by simulation and comparison.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN958.92

【参考文献】