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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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 59; no. 10; pp. 4505 - 4515
Main Authors: Masmoudi, A., Bellili, F., Affes, S., Stephenne, A.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.10.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2011.2161293