Narrowband Tracking Using a Markov Random Field Algorithm
Paul M. Baggenstoss · 2006
We present an algorithm for formulating the narrowband (or target) tracking problem as a Markov Random Field (MRF) with discrete and continous-valued hidden state variables. We then derive a simplified algorithm to estimate the model state variables. An MRF exists whenever there is a collection of sites that statistically interact with their neighbors. In the narrowband tracking problem, we assume we have detected a number of "interesting sites" in a spectrogram. These "interesting sites" are regions where there appears to be straight-line motion of a narrowband signal, perhaps detected by the application of a radon transform. We apply a linear dynamical model, to explain the behaviour of the target. This is identical in formulation to a Kalman filter, with the exception that the state transition matrix is many-to-one (many neighbor sites to a single site). The method is generalizable to any tracking problem