Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a s...Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.展开更多
Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking...Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.展开更多
A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation ...A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.展开更多
基金Projects(90820302,60805027)supported by the National Natural Science Foundation of ChinaProject(200805330005)supported by the Research Fund for the Doctoral Program of Higher Education,ChinaProject(2009FJ4030)supported by Academician Foundation of Hunan Province,China
文摘Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.
文摘Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.
文摘A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.
