摘要
传感器采样周期是影响目标跟踪的一个重要参数。现有自适应采样周期策略中,一些算法运算量比较大,计算效率低,不具有一般性。为此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。最后与几种自适应采样周期算法与固定采样周期算法通过交互式多模型(IMM)滤波算法进行仿真比较。仿真结果表明该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,比固定采样周期算法更能节约资源。
Sensor sampling period is an important parameter in affecting target tracking. Some sampling period selection algorithms have a large amount of computation time and are lack of efficiency and universality. An improved prediction covariance threshold method is proposed in this paper. Instead of using full enumeration optimization, the algorithm gives an improved optimization strategy. Finally it is compared with several adaptive sampling period algorithms and the fixed sampling period algorithm with interactive multiple model (IMM) filter algorithm. Comparison results show that the improved algorithm can satisfy the tracking requirement in the target tracking process with less computational time and higher computational efficiency than that of the adaptive sampling period algorithms. It also can conserve more sensor resources than that of the fixed sampling period algorithm.
出处
《火力与指挥控制》
CSCD
北大核心
2017年第5期19-22,共4页
Fire Control & Command Control
基金
国家自然科学基金(61004119
61174024
61375011)
浙江省自然科技基金资助项目(LY16F030009)
关键词
目标跟踪
自适应采样周期
固定采样周期
交互式多模型
target tracking,adaptive sampling period,fixed sampling period,interactive multiple model (IMM)