远距离干扰环境下目标跟踪的扩展卡尔曼粒子滤波算法  

Extended Kalman Particle Filter Algorithm for Target Tracking in Stand-off Jammer

作　　者：侯静[1] 景占荣[1] 羊彦[1] 

机构地区：[1]西北工业大学电子信息学院,西安710072

出　　处：《电子与信息学报》2013年第7期1587-1592,共6页Journal of Electronics & Information Technology

基　　金：西北工业大学博士论文创新基金(cx201114)资助课题

摘　　要：该文在扩展卡尔曼粒子滤波算法的基础上融合了"负"信息(没有接收到观测值的扫描)来实现远距离干扰环境下的目标跟踪。在整个实现过程中,由传感器模型推导出的高斯和似然函数充分考虑了正负信息,直接用于计算粒子权重更新。并且通过扩展卡尔曼滤波算法产生重要性密度函数,利用当前时刻的量测,使得粒子的分布更接近其后验概率分布,而且使用较少的粒子个数即可达到较好的跟踪效果。仿真证明,扩展卡尔曼粒子滤波算法在航迹连续性和跟踪精度方面明显优于扩展卡尔曼滤波算法,但计算复杂度较高。An Extended Kalman Particle Filter（EKPF） integrated with ＂negative＂ information（scans or dwells with no measurements） is implemented for target tracking in the Stand-Off Jammer（SOJ）.In the EKPF,the Gaussian sum likelihood function which is derived from a sensor model accounting for both the positive information and negative information is directly used in the weight update of the particle filter.And the importance density function is generated by using the Extended Kalman Filter（EKF） to take full account of the current measurement,thus leading to the distribution of the particles approaching the posterior probability density function.Moreover,use of a small number of particles can achieve good tracking accuracy.Simulation results show that EKPF outperforms the EKF implementation in terms of track continuity and track accuracy but at the cost of large computation complexity.

关 键 词：目标跟踪 远距离干扰机 扩展卡尔曼粒子滤波 负信息 

分 类 号：TN972[电子电信—信号与信息处理]