机构地区: 深圳大学信息工程学院ATR国防科技重点实验室
出 处: 《数据采集与处理》 2009年第4期483-486,共4页
摘 要: 提出了一种基于模糊自适应的Kalman滤波机动目标跟踪新算法。算法首先分析了目标航向角和观测残差均能实时反应出目标机动情况,并由此设计了一种有效的模糊逻辑规则,综合利用航向角变化量和残差,通过模糊推理动态估计Kalman滤波器的过程噪声协方差,从而提高对机动目标跟踪的性能。实验结果表明,提出算法对机动目标跟踪的精度较交互式多模型(Interacting multiple model,IMM)算法高,且计算量比IMM算法小。 A new fuzzy adaptive Kalman filter algorithm is proposed for maneuvering target tracking. Firstly, the algorithm analyzes that both the changes of course angles and the measurement residual can reflect the maneuverability of the target. Then, an effective fuzzy logic rule is designed. The fuzzy logic system uses the change of the course angle and the measurement residual as the input variables to compute the magnitude of the process noise covariance. Simulation results show that the proposed method has better performance in tracking accuracy, and lower computational load compared with the interacting multiple model (IMM) algorithm.