摘要
针对恶劣环境下混沌信号的检测与跟踪这一难题,以及基于一阶线性化的扩展卡尔曼滤波(EKF)技术存在的严重退化现象,提出了具有较强稳健性的修正EKF技术,获得了与Un-scented Kalman filter相匹配的性能。针对上述两种滤波方法在低信噪比情况下存在跟踪误差大的问题,为此引入了新颖的粒子滤波技术并且分析了该技术的可行性,最后仿真实验验证了该技术在低信噪比环境下的优越性。
In view of the difficult problem in the chaotic signal detection and track in the abominable environments, and the degenerate phenomenon of the traditional extended Kalman filter （EKF） which is based on the first order linearization, a modified EKF is developed that has much better robustness than the traditional EKF and gets almost the same performance as the Unscented Kalman filter. The two filters mentioned above experience large track error in the low signal noise ratio （SNR）, and a novel particle filter which can be used in the nonlinear and non-Gaussian environ- ments is introduced and also its feasibility is analyzed. The simulation demonstrates the superiorities of particle filtering in the low SNR
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2007年第4期514-517,共4页
Journal of Nanjing University of Science and Technology
基金
南京理工大学博士研究生创新培养基金
关键词
混沌信号
信号检测
扩展卡尔曼滤波
粒子滤波
chaotic signal
signal detection
extended Kalman filter
particle filtering
