The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attent...The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attention from lots of researchers.The existing achievements for thedelayed fusion all focus on Out-Of-Sequence Measurements(OOSM)problem which has many dis-advantages such as high communication cost,low computational efficiency,huge computational com-plexity and storage requirement,bad real-time performance and so on.In order to overcome theseproblems occurred in the OOSM fusion,the Out-Of-Sequence Estimates(OOSE)are considered tosolve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus thedistributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimallinear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the currentOOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of theOOSE problem also has good fusion accuracy.Performance analysis and computer simulation show thatthe total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.展开更多
A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the in...A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.展开更多
This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local...This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local estimates are not lost too much, the authors propose an optimal distributed fusion algorithm which is equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, based on the above global optimality result and sensor data compression, the authors propose a suboptimal distributed fusion algorithm. Numerical examples show that this suboptimal algorithm still works well and significantly better than the standard distributed Kalman filtering fusion subject to packet loss even if the process noise power is quite large.展开更多
基金the National Natural Science Foundation of China(No.60434020,No.60572051)International Coop-erative Project Foundation(No.0446650006)Ministryof Education Science Foundation of China(No.2050 92).
文摘The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attention from lots of researchers.The existing achievements for thedelayed fusion all focus on Out-Of-Sequence Measurements(OOSM)problem which has many dis-advantages such as high communication cost,low computational efficiency,huge computational com-plexity and storage requirement,bad real-time performance and so on.In order to overcome theseproblems occurred in the OOSM fusion,the Out-Of-Sequence Estimates(OOSE)are considered tosolve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus thedistributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimallinear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the currentOOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of theOOSE problem also has good fusion accuracy.Performance analysis and computer simulation show thatthe total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.
文摘A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.
基金supported by the National Natural Science Foundation of China under Grant Nos.60934009, 60901037 and 61004138
文摘This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local estimates are not lost too much, the authors propose an optimal distributed fusion algorithm which is equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, based on the above global optimality result and sensor data compression, the authors propose a suboptimal distributed fusion algorithm. Numerical examples show that this suboptimal algorithm still works well and significantly better than the standard distributed Kalman filtering fusion subject to packet loss even if the process noise power is quite large.
