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.