基于Rao-Blackwellized蒙特卡罗数据关联的检测跟踪联合优化

Joint optimization of detection and tracking with Rao-Blackwellized Monte Carlo data association

提出了基于Rao-Blackwellized蒙特卡罗数据关联的雷达目标检测跟踪联合优化算法。Rao-Blackwellization方法将单目标跟踪与数据关联分开处理,将序贯蒙特卡罗方法(粒子滤波)用于数据关联,实现杂波与虚警量测中的多目标跟踪。同时,根据粒子的分布范围确定波门大小。在考虑粒子权重的前提下,利用检测单元与所有粒子的相对位置对检测门限进行修正,提高检测率。将本文算法与已经实现的基于空域特性的杂波抑制算法相结合,分别应用于仿真数据、S波段相参与非相参雷达实测数据。实验结果表明,本文算法能够在粒子数较少的情况下,实现对小弱目标的检测与跟踪。

A joint optimization algorithm was proposed for radar target detection and tracking with RaoBlackwellized Monte Carlo data association. Rao-Blackwellization made the separation of single target tracking and data association,where the data association was solved by the sequential Monte Carlo method（particle filtering）,leading to the multiple target tracking in the environment of clutter and false alarm measurements.Meanwhile,the size of the wave gate depended on the distribution range of particles. Under the consideration of the particle weights,the detection threshold was modified with the relative position of the detection units to all the particles,improving the detection rate. Finally,combined with the algorithm for clutter suppression with spatial features achieved in the previous research,the proposed algorithm was applied to the simulated data as well as the ground-truth data collected by the S-band incoherent and coherent radars. It is demonstrated that the proposed algorithm can realize the detection and tracking of small targets with relatively small number of particles.