We consider the Kalman filtering problem in a networked environment where there are partial or entire packet losses described by a two state Markovian process. Based on random packet arrivals of the sensor measurement...We consider the Kalman filtering problem in a networked environment where there are partial or entire packet losses described by a two state Markovian process. Based on random packet arrivals of the sensor measurements and the Kalman filter updates with partial packet, the statistical properties of estimator error covariance matrix iteration and stability of the estimator are studied. It is shown that to guarantee the stability of the Kalman filter, the communication network is required to provide for each of the sensor measurements an associated throughput, which captures all the rates of the successive sensor measurements losses. We first investigate a general discrete-time linear system with the observation partitioned into two parts and give sufficient conditions of the stable estimator. Furthermore, we extend the results to a more general case where the observation is partitioned into n parts. The results are illustrated with some simple numerical examples.展开更多
基金supported by National Natural Science Foundation of China (No. 60504017)Fok Ying Tong Education Foundation(No. 111066)Program for New Century Excellent Talents in University (No. NCET-04-0982)
文摘We consider the Kalman filtering problem in a networked environment where there are partial or entire packet losses described by a two state Markovian process. Based on random packet arrivals of the sensor measurements and the Kalman filter updates with partial packet, the statistical properties of estimator error covariance matrix iteration and stability of the estimator are studied. It is shown that to guarantee the stability of the Kalman filter, the communication network is required to provide for each of the sensor measurements an associated throughput, which captures all the rates of the successive sensor measurements losses. We first investigate a general discrete-time linear system with the observation partitioned into two parts and give sufficient conditions of the stable estimator. Furthermore, we extend the results to a more general case where the observation is partitioned into n parts. The results are illustrated with some simple numerical examples.
