Multiple hypotheses tracking with heavy-tailed noise

The Kalman filter, which is optimal with respect to Gaussian distributed noisy measurements, is commonly used in the Multiple Hypothesis Tracker (MHT) for state update and prediction. It has been shown that when filtering noisy measurements distributed with asymptotic power law tails the Kalman filter underestimates the state error when the tail exponent is less than two and overestimates it when the tail exponent is greater that two. This has severe implications for tracking with the MHT which uses the estimated state error for both gating and probability calculations. This paper investigates the effects of different tail exponent values on the processes of hypothesis creation and deletion in the MHT.