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基于运动模型的一类传感器管理方法
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2009-07-17
王利祥
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利用目标检测前后信息熵的变化以及卡尔曼滤波方程中协方差的预测与更新阵,可以计算出传感器对目标进行检测或跟踪所产生的信息增量,并给出了一类基于信息增量最大化传感器管理方法. 仿真结果表明:与顺序跟踪方法相比,该方法能够更合理分配有限的传感器资源,提高跟踪系统的整体性能;与随机检测与跟踪方法相比,该方法在保证一定跟踪精度的前提下,能最大限度地发现并跟踪新目标.
关键词: 协方差阵; 传感器管理; 检测与跟踪
Abstract : By using evolution of information entropy in target detection and prediction and updating matrices of covariance in Kalman filter equations ,we can solve information gain in a measurement for target detection or tracking ,and present a method of sensor management based on maximization of information gain. Simulation results show that this method can rationally assign the limited sensor resources and improve the whole performance of tracking system compared with sequence tracking method ,and can maximally detect and track new targets under a certain tracking precision compared with random detection and tracking.
Key words : covariance matrix ;sensor management ;detection and tracking
关键词: 协方差阵; 传感器管理; 检测与跟踪
Abstract : By using evolution of information entropy in target detection and prediction and updating matrices of covariance in Kalman filter equations ,we can solve information gain in a measurement for target detection or tracking ,and present a method of sensor management based on maximization of information gain. Simulation results show that this method can rationally assign the limited sensor resources and improve the whole performance of tracking system compared with sequence tracking method ,and can maximally detect and track new targets under a certain tracking precision compared with random detection and tracking.
Key words : covariance matrix ;sensor management ;detection and tracking
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