粒子流粒子滤波检测前跟踪方法

Particle Flow Particle Filter Track-Before-Detect Method

检测前跟踪通过积累多帧量测以检测和跟踪微弱目标。积累的关键在于对目标后验密度的准确表示。传统粒子滤波器过于依赖建议密度,因而对目标后验密度的表示不够准确。新提出的粒子流滤波器能够准确表示目标后验密度,但无法实现量测的帧间积累。为此,本文提出一种在粒子滤波框架下结合粒子流的检测前跟踪方法:采用粒子滤波框架实现多帧量测积累,并在每一帧内采用Localized Exact Daum-Huang粒子流表示目标后验密度,以提升量测积累效果。我们通过Rayleigh杂波下Swerling1型起伏目标的检测和跟踪实验证明了所提算法的性能。

The track-before-detect strategy detects and tracks weak targets by the integration of measurements in multiple frames.The key step to the integration is the accurate characterization of the target posterior.The traditional particle filter relies too much on the proposal density,and thus the characterization is not exact enough.The newly presented particle flow filter can represent the target posterior exactly.However,it neglects the multi-frame integration.Therefore,in this paper,a novel track-before-detect scheme is proposed,which incorporates a particle flow filter into an encompassing particle filter framework.The particle filter is used for multi-frame measurement integration,and within each frame the Localized Exact Daum-Huang filter is used to represent the target posterior to improve the effect of integration.The performance of the proposed algorithm is evaluated by simulations of a Swerling 1 fluctuating target detecting and tracking in Rayleigh clutter.