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提出基于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。最后,将本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、以及S波段相参与非相参雷达实测数据。实验结果表明,本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 for radar target detection and tracking based on Rao-Blackwellized Monte Carlo data association is proposed. Rao-Blackwellization treats single target tracking and data association separately, and uses sequential Monte Carlo method (particle filter) for data association to achieve multi-target tracking in clutter and false alarm measurement. At the same time, according to the distribution of particles to determine the size of the gate. Under the premise of considering the particle weight, the detection threshold is modified by the relative position of the detection unit and all the particles to improve the detection rate. Finally, this algorithm is combined with the existing clutter suppression algorithm based on spatial characteristics, which are respectively applied to the simulation data and S-band coherent non-coherent radar measured data. The experimental results show that this algorithm can detect and track the weak target in the case of small number of particles.