摘要
针对观测器探测概率小于1的不完全量测情况下的水下纯方位系统的目标跟踪问题,提出了不完全量测下的基于扩展卡尔曼滤波的目标跟踪算法。首先,建立不完全量测情况下的水下纯方位目标跟踪数学模型;其次,在数据出现不完全量测时,采用前一次的更新值对缺失数据进行弥补并完成滤波;最后,采用最优理论性能下界(CRLB)和均方根误差(RMSE)这两种评价准则对此算法进行评估。仿真实验结果表明:在不完全量测下的水下纯方位系统的目标跟踪问题中,所提出的基于扩展卡尔曼滤波的目标跟踪算法在保证预期跟踪精度的前提下,具有较高的实时性。
Concerning the problem of underwater bearings-only system target tracking with incomplete measurements when the probability of sensor detection is less than 1,an improved extended Kalman filtering algorithm for target state estimation was presented. First, the mathematical model of underwater bearings-only system for target tracking with incomplete measurements was established. Second, based on the sensor＇s incomplete measurement data, the previous update data was used to compensate for the incomplete date and then to perform the filtering. Finally, two evaluation criteria including Cramer-Rao Low Bound（ CRLB） and Root Mean Square Errors（ RMSE） were used to evaluate the proposed algorithm. The simulation results show that the proposed extended Kalman filtering algorithm for target tracking has higher real-time property with desired tracking precision in the problem of underwater bearings-only system target tracking with incomplete measurements.
出处
《计算机应用》
CSCD
北大核心
2015年第4期1106-1109,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61273076)
江苏省自然科学基金资助项目(BK2012801)
关键词
不完全量测
纯方位目标跟踪
扩展卡尔曼滤波
最优理论性能下界
均方根误差
incomplete measurement
Bearings-Only Tracking（BOT）
Extended Kalman Filter（EKF）
Cramer-Rao Low Bound（CRLB）
Root Mean Square Error（RMSE）
