摘要
针对低信噪比条件下弱目标的实时检测与跟踪,提出了一种基于粒子滤波,结合序贯概率比检验和固定样本长度似然比检验的递归检测前跟踪算法。粒子滤波用以解决跟踪中的非线性和非高斯问题;序贯概率比检验通过序贯累积多帧观测数据来提高信噪比,以最小时延检测到目标存在;固定样本长度似然比检验,通过选择合理的样本长度,保证持续检测到目标,且无延迟检测目标消失。仿真试验表明,该算法有着良好的检测与跟踪性能。
A recursive track-before-detect （TBD） algorithm based on particle filter, combined sequential probability ratio test and fixed sample size （SPRT-FSS） likelihood ratio test is presented according to the on-line detection and tracking of weak targets in low SNR environment. The particle filter is used to solve the nonlinear and non-Gaussian problem; SPRT can increase SNR to detect the existence of targets with the shortest delay through sequentially cumulating multi-frame measurements; The FSS likelihood ratio test is used to successively detect targets, and it can detect disappearing of targets in time by setting appropriate sample size. Simulation results show that the TBD algorithm has favorable performance of detecting and tracking.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2007年第12期2143-2148,共6页
Systems Engineering and Electronics
关键词
检测前跟踪
粒子滤波
序贯概率比检验
固定样本长度
track-before-detect
particle filter
sequential probability ratio test
fixed sample size
