This paper studies the nonstationary filtering problem of Markov jump system under <span style="white-space:nowrap;"><i>l</i><sub>2</sub> - <i>l</i><sub>...This paper studies the nonstationary filtering problem of Markov jump system under <span style="white-space:nowrap;"><i>l</i><sub>2</sub> - <i>l</i><sub>∞</sub> </span>performance. Due to the difference in propagation channels, signal strength and phase will inevitably change randomly and cause the waste of signals resources. In response to this problem, a channel fading model with multiplicative noise is introduced. And then a nonstationary filter, which receives signals more efficiently is designed. Meanwhile Lyapunov function is constructed for error analysis. Finally, the gain matrix for filtering is obtained by solving the matrix inequality, and the results showed that the nonstationary filter converges to the stable point more quickly than the traditional asynchronous filter, the stability of the designed filter is verified.展开更多
A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akai...A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.展开更多
Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conv...Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter,which does not model the Poisson noise properly.This study proposes a nonstationary filter for Poisson noise control.The filter is implemented in the spatial domain in a form similar to convolution.展开更多
A linear denoising filter is usually of lowpass type,and the main parameter in a lowpass filter is the cutoff frequency.The lowpass filters are normally shift invariant and can be implemented as convolution in the spa...A linear denoising filter is usually of lowpass type,and the main parameter in a lowpass filter is the cutoff frequency.The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain.This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model.An example of such a linear filter is presented for lowdose X-ray computed tomography(CT).展开更多
文摘This paper studies the nonstationary filtering problem of Markov jump system under <span style="white-space:nowrap;"><i>l</i><sub>2</sub> - <i>l</i><sub>∞</sub> </span>performance. Due to the difference in propagation channels, signal strength and phase will inevitably change randomly and cause the waste of signals resources. In response to this problem, a channel fading model with multiplicative noise is introduced. And then a nonstationary filter, which receives signals more efficiently is designed. Meanwhile Lyapunov function is constructed for error analysis. Finally, the gain matrix for filtering is obtained by solving the matrix inequality, and the results showed that the nonstationary filter converges to the stable point more quickly than the traditional asynchronous filter, the stability of the designed filter is verified.
文摘A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.
文摘Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter,which does not model the Poisson noise properly.This study proposes a nonstationary filter for Poisson noise control.The filter is implemented in the spatial domain in a form similar to convolution.
文摘A linear denoising filter is usually of lowpass type,and the main parameter in a lowpass filter is the cutoff frequency.The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain.This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model.An example of such a linear filter is presented for lowdose X-ray computed tomography(CT).
