A Non-Data-Aided Maximum Likelihood Time Delay Estimator Using Importance Sampling

In this paper, we present a new time delay maximum likelihood estimator based on importance sampling (IS). We show that a grid search and lack of convergence from which most iterative estimators suffer can be avoided. It is assumed that the transmitted data are completely unknown at the receiver. Mo...

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Vydané v:	IEEE transactions on signal processing Ročník 59; číslo 10; s. 4505 - 4515
Hlavní autori:	Masmoudi, A., Bellili, F., Affes, S., Stephenne, A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.10.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Compressed Computer Science Cramér-Rao lower bound (CRLB) Delay effects Detection, estimation, filtering, equalization, prediction Estimators Exact sciences and technology Importance sampling Information, signal and communications theory Intervals Iterative methods Mathematical analysis Mathematical models Maximum likelihood estimation Monte Carlo methods non-data-aided (NDA) estimation optimization methods Random variables Signal and communications theory Signal and Image Processing Signal to noise ratio Signal, noise Studies symbol timing recovery Telecommunications and information theory Time delay Unified modeling language Performance evaluation Parameter estimation AWGN Nuisance parameter Data transmission Iterative method Cramér-Rao lower bound (CRLB) symbol timing recovery Optimization Monte Carlo methods Delay time Random variable Importance function Importance sampling Likelihood function Cramer Rao inequality Monte Carlo method Global optimum optimization methods Algorithm Computational complexity Clock recovery Symbol manipulation Signal processing non-data-aided (NDA) estimation Numerical simulation Maximum likelihood
ISSN:	1053-587X, 1941-0476
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Popis
Shrnutí:	In this paper, we present a new time delay maximum likelihood estimator based on importance sampling (IS). We show that a grid search and lack of convergence from which most iterative estimators suffer can be avoided. It is assumed that the transmitted data are completely unknown at the receiver. Moreover the carrier phase is considered as an unknown nuisance parameter. The time delay remains constant over the observation interval and the received signal is corrupted by additive white Gaussian noise (AWGN). We use importance sampling to find the global maximum of the compressed likelihood function. Based on a global optimization procedure, the main idea of the new estimator is to generate realizations of a random variable using an importance function, which approximates the actual compressed likelihood function. We will see that the algorithm parameters affect the estimation performance and that with an appropriate parameter choice, even over a small observation interval, the time delay can be accurately estimated at far lower computational cost than with classical iterative methods.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2011.2161293