摘要
探讨了TMA(目标运动分析)中基本的非线性估计问题,介绍了基于无味变换(Unscented Transform ation-UT)的无味卡尔曼滤波(Unscented Kalm an F iltering-UKF)算法的设计思想与具体实现,特别针对空对海单站只测方位与到达时间TMA(BTO-TMA)问题应用UKF和EKF(扩展卡尔曼滤波)进行了对照研究,建立了问题的离散非线性滤波估计模型,设计了典型的应用场景,给出了初值有偏和无偏两种情形下的Monte Carlo仿真运行结果;表明UKF在该应用背景下是切实可行的,具有更高的估计精度和更强的收敛特性。
The problem oi nonlinear filtering is discussed firstly which is the groundwork embedded in the application of target motion analysis （TMA）. The unscented transformation （UT） and unscented Kalman filtering （UKF） algorithms are then introduced, including their design consideration and specific algorithm. Particular attention is paid to the problem of single observer passive air - to - sea BTO - TMA （TMA based on bearing and time-of-arrival measurements only）. The discrete-time models are formulated pertinent to the nonlinear filtering problem and a typical scenario is depicted. Both biased and unbiased cases are investigated. The contrast results of Monte Carlo simulations between UKF and EKF have demonstrated that UKF is more feasible to the air - to - sea BTO - TMA by virtue of its favorable behaviors with higher accuracy and stronger convergence.
出处
《探测与控制学报》
CSCD
北大核心
2006年第5期56-61,共6页
Journal of Detection & Control
关键词
递推非线性滤波
扩展卡尔曼滤波
无味变换
无味卡尔曼滤波
方位-到达时间目标运动分析
recursive nonlinear filtering
extended kalman filtering
unscented transformation
unscent-ed kalman filtering
bearing and time-of-arrival only target motion analysis
