杂波背景下基于概率假设密度的辅助粒子滤波检测前跟踪改进算法

Track-Before-Detect Algorithm Based on Improved Auxiliary Particle PHD Filter under Clutter Background

在杂波背景条件下,现有的基于概率假设密度(PHD)滤波的粒子滤波检测前跟踪(TBD)算法,存在对密集多目标数目估计不准,使用粒子数目较多会造成维数灾难的问题。因此,该文引入两层粒子的概念,将基于平行分割(PP)理论的辅助粒子滤波(APF)应用于基于概率假设密度的检测前跟踪(PHD-TBD)算法中,提出基于概率假设密度滤波的平行分割辅助粒子滤波检测前跟踪(APP-PF-PHD-TBD)算法以提高目标数目及状态估计精度。仿真实验证明,相对于现有基于PHD的粒子滤波检测前跟踪算法,该算法在目标数目和状态估计精度上具有显著的性能优势,在密集目标场景下,优势尤为突出。最后,利用导航雷达实测所得海杂波背景数据证明,该算法在应用中性能更加优异。

Under the clutter background condition,the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density(PHD)filtering is not accurate enough to estimate the number of targets in dense multi-objectives.In this study,the concept of two-layer particle is introduced.The Auxiliary Particle Filter(APF)based on Parallel Partition(PP)theory is applied to PHD-TBD.The Auxiliary Parallel Partition Particle Filter(which is based on APF and PP)Track-Before-Detect based on the Probability Hypothesis Density filter(APP-PF-PHD-TBD)algorithm is proposed to improve the target number and state estimation accuracy.The simulation results show that,compared with the existing PHD-filtering-based particle filter track-before-detect algorithm,the proposed algorithm has significant performance advantages in target number and state estimation accuracy.These advantages are particularly obvious in dense target scenarios.Finally,the sea clutter background data obtained using the navigation radar prove that the proposed algorithm outperforms the existing PHD-filtering-based particle filter track-before-detect algorithm in application.