An image-based filter for discrete-time Markovian jump linear systems
Automatica (Journal of IFAC)
Filters for estimating Markov modulated Poisson processes and image-based tracking
Automatica (Journal of IFAC)
Finite-dimensional filters for passive tracking of Markov jump linear systems
Automatica (Journal of IFAC)
Recursive estimation from discrete-time point processes
IEEE Transactions on Information Theory
Computer Vision and Image Understanding
An efficient hybrid estimator for nonlinear channels in state space
Signal Processing
Intent inference via syntactic tracking
Digital Signal Processing
Brief Hybrid state estimation: a target tracking application
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
We consider tracking algorithms for manoeuvring targets when the observations include extra information on the current operating mode of the target obtained from an image sensor. The target is modelled as a Markov jump linear system and the image-based observations form a discrete-time point process. We derive the optimal (minimum mean square error) filtered estimate which intrinsically fuses the image-based and primary observations. This optimal filter is computationally prohibitive but provides the basis for a clear understanding of various suboptimal approaches. We propose the image-enhanced IMM filter as a practical alternative which retains many desirable properties of the optimal filter and outperforms existing image-enhanced tracking algorithms over a broad range of operating scenarios.