Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algori...Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.展开更多
This paper deals with distributed state estimation problem for discrete time-varying systems over binary sensor networks,where every binary sensor is equipped with an energy harvester.The input of every binary sensor ...This paper deals with distributed state estimation problem for discrete time-varying systems over binary sensor networks,where every binary sensor is equipped with an energy harvester.The input of every binary sensor considers the randomly occurring missing measurements.The differences between the real and estimated inputs of binary sensor are employed to derive useful information in order to address the insufficient information for estimation purpose.The information from neighboring nodes is transmitted only if its energy level is positive,where a random variable is introduced to formulate the energy level.By means of the available information,distributed estimator is constructed for each binary sensor and the desirable performance constraints is given for the dynamic characteristics of estimation errors within anite time horizon.Sucient conditions are established for the existence of desired distribution estimation quantities through local performance analysis methods.Also,the desired distributed estimator gains are calculated recursively,which means the desirable scalability.Ultimately,the viability and efficiency of the distributed scheme are exhibited through a practical illustration.展开更多
基金This work is supported by The National Science Fund for Distinguished Young Scholars （60725105） National Basic Research Program of China （973 Program） （2009CB320404）+5 种基金 Program for Changjiang Scholars and Innovative Research Team in University （IRT0852） The National Natural Science Foundation of China （60972048, 61072068） The Special Fund of State Key Laboratory （ISN01080301） The Major program of National Science and Technology （2009ZX03007- 004） Supported by the 111 Project （B08038） The Key Project of Chinese Ministry of Education （107103）.
文摘Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.
基金supported in part by the National Natural Science Foundation of China under Grants 62073070 and U21A2019,and in part by the Alexander von Humboldt Foundation of Germany.
文摘This paper deals with distributed state estimation problem for discrete time-varying systems over binary sensor networks,where every binary sensor is equipped with an energy harvester.The input of every binary sensor considers the randomly occurring missing measurements.The differences between the real and estimated inputs of binary sensor are employed to derive useful information in order to address the insufficient information for estimation purpose.The information from neighboring nodes is transmitted only if its energy level is positive,where a random variable is introduced to formulate the energy level.By means of the available information,distributed estimator is constructed for each binary sensor and the desirable performance constraints is given for the dynamic characteristics of estimation errors within anite time horizon.Sucient conditions are established for the existence of desired distribution estimation quantities through local performance analysis methods.Also,the desired distributed estimator gains are calculated recursively,which means the desirable scalability.Ultimately,the viability and efficiency of the distributed scheme are exhibited through a practical illustration.
