Efficient maximum-likelihood period estimation from incomplete timing data

The problem of estimation the period of a periodic event from a sequence of noisy, incomplete time-of-arrival (TOA) observations is studied. A novel grid spacing determination strategy is proposed for a class of numerical-search estimators, whose performances is determined by grid spacing selection. A modified algorithm based on the Fogel's Periodogram estimator is proposed by employing that strategy. It is shown that our algorithm is very likely to yield the maximum likelihood estimate (MLE), with a low complexity of O(n2). Simulation results demonstrate the superior performance of our estimator comparing with the other existing estimators.