HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

Abstract

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications.

Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator.

Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.