In this paper, an improved variational adaptive Kalman filter for cooperative localization with inaccurate prior information is proposed, in which the scale matrix of one-step prediction error covariance adaptively estimated by using expectation-maximization algorithm. A novel alternate iteration strategy proposed to reduce computational complexity method. Convergence analysis and theoretical c...

Respiratory motion correction degrades quantitatively and qualitatively Nuclear Medicine images. We propose that adaptive approaches are required to correct for the irregular breathing patterns often encountered in the clinical setting, which can be addressed within a Bayesian tracking formulation. This allows inference of the hidden organ configurations using only knowledge of an external obse...

If a sensor requires finite processing and communication time, then the current sensor reading actually corresponds to the states of a vehicle at some point in the past. At the sensor fusion algorithm, it is expected that large errors may accumulate over time if we simply ignore the fact that the sensor measurement is lagged. Over several decades, the Kalman filter based compensation techniques...

Absrrucr-This paper addresses the problem of adaptive channel tracking and equalization for multi-input multi-output (MIMO) time-variant frequency-selective channels. A finite-length minimum-mean-squarederror decision-feedback equalizer (MMSE-DFE) performs the equalization task, while a Kalman filter tracks the MIMO channel, which models the corrupting effects of inter-symbol interference @SI),...

In this work, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters commun...

In this chapter, we consider the estimation of a dynamical system over a wireless fading channel using a Kalman filter. We develop a framework for understanding the impact of stochastic communication noise, packet drop and the knowledge available on the link qualities on Kalman filtering over fading channels. We consider three cases of “full knowledge”, “no knowledge” and “partial knowledge”, b...

By monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong trac...