基于運(yùn)動(dòng)模型的一類傳感器管理方法

利用目標(biāo)檢測(cè)前后信息熵的變化以及卡爾曼濾波方程中協(xié)方差的預(yù)測(cè)與更新陣,可以計(jì)算出傳感器對(duì)目標(biāo)進(jìn)行檢測(cè)或跟蹤所產(chǎn)生的信息增量,并給出了一類基于信息增量最大化傳感器管理方法. 仿真結(jié)果表明:與順序跟蹤方法相比,該方法能夠更合理分配有限的傳感器資源,提高跟蹤系統(tǒng)的整體性能;與隨機(jī)檢測(cè)與跟蹤方法相比,該方法在保證一定跟蹤精度的前提下,能最大限度地發(fā)現(xiàn)并跟蹤新目標(biāo).
關(guān)鍵詞: 　協(xié)方差陣; 傳感器管理; 檢測(cè)與跟蹤
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