Random walk/Markov Chain model for sensor positional uncertainty with application to UXO discrimination

We considered the problem of sensor signal processing in the presence of positional uncertainty with application to classification of unexploded ordnance from observations of electromagnetic induction data. Our approach is based on the synthesis of random walk and Markov chain models for describing the correlated positional perturbations. Using this model, we develop an algorithm for the estimation of target parameters in which we minimize the maximum data misfit where the maximization is taken over the range of positional uncertainties supported by our model. The specific Markov nature of this model leads naturally to a low complexity estimation scheme based on the Viterbi algorithm. The results of using our Min- Max approach show significant improvement in final classification relative to the case where positional uncertainty is ignored.