一种面向弹道再入目标跟踪的HPD-SRCQSPF算法

针对弹道再入目标轨迹跟踪问题，提出基于混合建议分布的平方根容积求积采样粒子滤波（HPD-SRCQSPF）算法，该算法以混合建议分布为框架，由两个基本建议分布组成。其中一个基本建议分布为先验分布，另一个基本建议分布为平方根容积求积卡尔曼滤波估计后的值。该混合建议分布与真实的后验分布很接近，因此有着高效性、高精度等特点。仿真结果表明，对于弹道再入目标轨迹跟踪模型，相比于标准粒子滤波（SPF）算法和平方根容积求积粒子滤波（SRCQPF）算法，HPD-SRCQSPF算法可以在较低运算负载的情况下获得更好的跟踪性能。特别是在弹道目标变轨机动的情况时，所提出算法的性能增益更为显著。

The HPD SRCQSPF Algorithm for Ballistic Reentry Target Tracking

For the problem of ballistic reentry target trajectory tracking, the square-root cubature quadrature sampling particle filter algorithm based on hybrid proposal distribution (HPD-SRCQSPF) is proposed. The proposed algorithm consists of two basic proposal distributions. The priori distribution is used as a basic proposal distribution, and the square root cubature quadrature Kalman filter is used as the other basic proposal distribution. The hybrid proposal distribution is close to the posterior distribution, so it has the characteristics of high efficiency and high precision. Simulation results show that for the ballistic reentry target trajectory tracking model, compared with the standard particle filter (SPF) algorithm and the square-root cubature quadrature particle filter (SRCQPF) algorithm, the HPD-SRCQSPF algorithm not only has a low computational complexity, but also has very good tracking accuracy. Especially in the case of a ballistic target maneuver, the proposed algorithm can obtain higher estimation accuracy at lower computational cost.