Multiple hypotheses tracking with heavy-tailed noise

Scott W. Sims; Jason F. Ralph; Moira I. Smith; Christopher R. Angell; Peter N. Randall

doi:10.1117/12.487007

25 August 2003 Multiple hypotheses tracking with heavy-tailed noise

Scott W. Sims, Jason F. Ralph, Moira I. Smith, Christopher R. Angell, Peter N. Randall

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.487007
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

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 track deletion and creation in the MHT.

Citation Download Citation

Scott W. Sims, Jason F. Ralph, Moira I. Smith, Christopher R. Angell, and Peter N. Randall "Multiple hypotheses tracking with heavy-tailed noise", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.487007

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