This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisens...This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.展开更多
For flat fast fading Multiple-Input Multiple-Output(MIMO) channels,this paper presents a sampling based channel estimation and an iterative Particle Filter(PF) signal detection scheme. The channel estimation is compri...For flat fast fading Multiple-Input Multiple-Output(MIMO) channels,this paper presents a sampling based channel estimation and an iterative Particle Filter(PF) signal detection scheme. The channel estimation is comprised of two parts:the adaptive iterative update on the channel distribution mean and a regular update on the "adaptability" via pilot. In the detection procedure,the PF is employed to produce the optimal decision given the known received signal and the sequence of the channel samples,where an asymptotic optimal importance density is constructed,and in terms of the asymptotic update order,the Parallel Importance Update(PIU) and the Serial Importance Update(SIU) scheme are performed respectively. The simulation results show that for the given fading channel,if an appropriate pilot mode is selected,the proposed scheme is more robust than the conventional Kalman filter based superimposed detection scheme.展开更多
基金Supported by the National Natural Science Foundation of China (No.60434020, 60374020)International Cooperation Item of Henan (No.0446650006)Henan Outstanding Youth Science Fund (No.0312001900).
文摘This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.
基金the National Natural Science Foundation of China (No. 60672047)Shanghai Postdoctoral Scientific Program (No. 05R214110).
文摘For flat fast fading Multiple-Input Multiple-Output(MIMO) channels,this paper presents a sampling based channel estimation and an iterative Particle Filter(PF) signal detection scheme. The channel estimation is comprised of two parts:the adaptive iterative update on the channel distribution mean and a regular update on the "adaptability" via pilot. In the detection procedure,the PF is employed to produce the optimal decision given the known received signal and the sequence of the channel samples,where an asymptotic optimal importance density is constructed,and in terms of the asymptotic update order,the Parallel Importance Update(PIU) and the Serial Importance Update(SIU) scheme are performed respectively. The simulation results show that for the given fading channel,if an appropriate pilot mode is selected,the proposed scheme is more robust than the conventional Kalman filter based superimposed detection scheme.
