Infrared/laser multi-sensor fusion and tracking based on the multi-scale model

The state estimation problem of targets detected by infrared/laser composite detection system with different sampling rates was studied in this paper. An effective state estimation algorithm based on data fusion is presented. Because sampling rate of infrared detection system is much higher than that of the laser detection system, the theory of multi-scale analysis is used to establish multi-scale model in this algorithm. At the fine scale, angle information provided by infrared detection system is used to estimate the target state through the unscented Kalman filter. It makes full use of the high frequency characteristic of infrared detection system to improve target state estimation accuracy. At the coarse scale, due to the sampling ratio of infrared and laser detection systems is an integer multiple, the angle information can be fused directly with the distance information of laser detection system to determine the target location. The fused information is served as observation, while the converted measurement Kalman filter (CMKF) is used to estimate the target state, which greatly reduces the complexity of filtering process and gets the optimal fusion estimation. The simulation results of tracking a target in 3-D space by infrared and laser detection systems demonstrate that the proposed algorithm in this paper is efficient and can obtain better performance than traditional algorithm.     

解耦的转换测量Kalman滤波算法

对于极坐标观测下的目标跟踪问题,基于转换测量Kalman滤波器(CMKF),应用正规变换,得到了解耦的CMKF算法,同时研究了CMKF和解耦CMKF算法的初值估计问题.仿真结果表明了解耦CMKF算法及其初值估计的有效性.     

多站无源定位系统中的机动目标跟踪算法

针对多站测向无源定位系统提出了一种杂波环境下机动目标的被动跟踪算法———ＣＭＩＭＭＰＤＦ 算法。该算法首先用转换测量的卡尔曼滤波（ＣＭＫＦ）替代了传统的扩展卡尔曼滤波,克服 了后者精度不高易发散的缺点,并将其结合交互多模型（ＩＭＭ）算法及概率数据关联（ＰＤＦ）算法,有效 地完成了多站无源定位系统对杂波环境下机动目标的跟踪。仿真结果证明了该算法的有效性。     

三维解耦转换测量Kalman滤波算法

基于去偏转换测量Kalman滤波（CMKF）,利用对角化和单位化方法求解正交变换,设计了实用的三维空间解耦降维算法。算法复杂度分析和仿真试验结果表明了算法的有效性。     

基于极坐标的CMKF定位算法研究

针对经典的推广卡尔曼滤波算法受初值和测量噪声影响大,算法不稳定等缺点,提出了一种新的基于极坐标的转换测量卡尔曼滤波定位算法,计算机仿真结果验证了这种算法具有较好的稳定性和实用性.