On periodic pulse interval analysis with outliers and missing observations
- 1 November 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 46 (11) , 2990-3002
- https://doi.org/10.1109/78.726812
Abstract
Analysis of periodic pulse trains based on time of arrival is considered, with perhaps very many missing observations and contaminated data. A period estimator is developed based on a modified Euclidean algorithm. This algorithm is a computationally simple, robust method for estimating the greatest common divisor of a noisy contaminated data set. The resulting estimate, although it is not maximum likelihood, is used as initialization in a three-step algorithm that achieves the Cramer-Rao bound (CRB) for moderate noise levels, as shown by comparing Monte Carlo results with the CRBs. This approach solves linear regression problems with missing observations and outliers. Comparisons with a periodogram approach based on a point process model are shown. An extension using multiple independent data records is also developed that overcomes high levels of contamination.Keywords
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