一种基于粒子滤波的联合跟踪与分类算法

A joint tracking and classification algorithm based on particle filtering

针对纯运动学信息联合跟踪与分类问题,提出了一种基于混合无味粒子滤波的联合跟踪与分类算法。在传统粒子滤波联合跟踪与分类算法的基础上,通过采用无味变换,利用多个无味卡尔曼滤波器给出更高质量的粒子建议分布,提高整个算法的性能。理论分析和仿真结果都表明,与传统粒子滤波联合跟踪与分类算法相比,该算法无论在跟踪精度还是在分类正确率上都有明显的提高。

In order to cope with the joint tracking and classification （JTC） problem, a new mixture unscented particle joint tracking and classification algorithm （MUPF-JTC） was proposed. Based on traditional mixture unscented particle joint tracking and classification algorithm （MPF-JTC） , by adopting the methods of unscented transform（UT） , several unscented Kalman filters（UKF） were designed in order to get higher quality particle distributions. Mathematical analysis and simulation results confirm that the MUPF-JTC algorithm can achieve better es- timation than common MPF-JTC algorithm.