【作者】 万莉； 皮亦鸣；

【Author】 WAN Li, PI Yi-ming

【机构】 电子科技大学；

【摘要】 雷达系统下的非线性目标跟踪已被人们广泛重视。扩展卡尔曼滤波器是将卡尔曼滤波器局部线性化,其算法简单、计算量小,适用于弱非线性、高斯环境下。不敏卡尔曼滤波器足用一系列确定样本来逼近状态的后验概率密度,在高斯环境中,对任何非线性系统有较好的跟踪性能。粒子滤波器是用随机样本来近似状态后验概率密度函数,适用于任何非线性非高斯系统。在仿真实验中,对三者的性能分别从三个角度进行仿真比较,结果证明了在复杂的非高斯非线性环境中,粒子滤波器的性能明显优于另外两种滤波器,但计算复杂,耗时长。更多还原

【Abstract】 Nonlinear target-tracking methods have been widely researched in Radar system. Extended Kalman filter, based on local linearization of KF, is easy to realize and has good performance in Gaussian and mild nonlinear environment. Unscented KF utilizes a set of definite samplings to approximate posterior probability density function, while particle filter uses random particles. Hence, UKF is suitable for any nonlinear but Gaussian environment, but PF plays good act in any nonlinear and non-Gaussian environment. In simulation experiments, their performances are compared in three aspects. The results prove the tracking performance of PF is much better than EKF and UKF in complex environment, but computation of PF is much larger than EKF and UKF.更多还原