基于改进自适应滤波的异质传感器融合方法  

The Fusion Method of Heterogeneous Sensor Based on Improved Self-adaptive Filtering

作　　者：罗志斌[1] 刘先省[1] 

机构地区：[1]河南大学计算机与信息工程学院,开封475001

出　　处：《自动化仪表》2007年第2期1-4,9,共5页Process Automation Instrumentation

基　　金：国家自然科学基金(编号:60272024);河南省高校杰出科研人才创新工程项目(编号:2003KYCX003);河南省高校创新人才培养工程

摘　　要：针对“当前”统计模型中加速度极限值的预先设定问题,提出了一种基于位移预测协方差的改进机动目标跟踪算法。采用扩展卡尔曼滤波实现异质传感器融合。该算法避免了加速度极限值的预先设定对状态估计的不利影响,仿真结果表明,该算法提高了机动目标的跟踪精度。Aiming at the problem of setting in advance for limit of acceleration in ＂Current＂ statistical model, an improved maneuvering target tracking algorithm based on displacement prediction covariance is presented. It utilizes extended Kalman filtering to carry out heterogeneous sen- sor fusion. This algorithm avoids the adverse influence of setting up the limit of acceleration in advance to state estimation, the simulation result shows the accuracy of the maneuvering target tracking is obviously enhanced by this algorithm.

关 键 词：当前统计模型 加速度极限值 位移预测协方差 雷达红外 目标跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]