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
A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with u...A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.展开更多
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.
基金supported by the National Natural Science Foundation of China(No.20155896025)
文摘A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.
