摘要
针对基本粒子滤波算法没有融合当前时刻观测值的缺点,提出了一种卡尔曼粒子滤波算法。该算法针对每一个粒子使用卡尔曼滤波器进行更新,在更新过程中融合最新的观测信息,提高粒子滤波器的估计精度。针对纯方位目标跟踪问题进行实验,与基本粒子滤波算法及卡尔曼滤波进行了对比。实验结果表明,卡尔曼粒子滤波算法的跟踪性能明显优于其他两种算法。
The conventional bootstrap filter suffers a main drawback of not incorporating the latest observations. Therefore, this paper proposed a Kalman Particle Filter （KPF） algorithm, and applied this new algorithm to bearing-only target tracking. An improved scheme was presented to handle this problem and yield a Kalman particle filter. The underlying idea of the new algorithm is that each particle is updated using Kalman filter incorporating the coming observations. A bearing-only tracking model was experimented and compared with bootstrap filter and KPF. The experimental results verify its superiority.
出处
《计算机应用》
CSCD
北大核心
2010年第1期167-170,共4页
journal of Computer Applications
基金
大连东软信息学院青年科研基金资助项目(NEUSOFTIIT20080005)
