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A method of phase comparison time delay estimation using large time-bandwidth product signals is presented. This method compares the phases of the matched filters for each channel, and it is shown that for signals with symmetric power spectra, a meaningful estimate of the time delay can be expected from this phase information and knowledge of the carrier frequency of the signal. The estimator is evaluated while operating in white Gaussian noise which is in general correlated between channels, and curves are given for the density function, mean, and variance of the estimator for various noise assumptions. The estimator is shown to take advantage of the processing gain of large time-bandwidth product signals to reduce the variance of the time delay estimate. Keywords include: Phase comparison time dealy estimator; Matched filters; Large time-bandwidth; Symmetric power spectra; and White gaussian noise.
An overview on the challenging new topic of phase-aware signal processing Speech communication technology is a key factor in human-machine interaction, digital hearing aids, mobile telephony, and automatic speech/speaker recognition. With the proliferation of these applications, there is a growing requirement for advanced methodologies that can push the limits of the conventional solutions relying on processing the signal magnitude spectrum. Single-Channel Phase-Aware Signal Processing in Speech Communication provides a comprehensive guide to phase signal processing and reviews the history of phase importance in the literature, basic problems in phase processing, fundamentals of phase estimation together with several applications to demonstrate the usefulness of phase processing. Key features: Analysis of recent advances demonstrating the positive impact of phase-based processing in pushing the limits of conventional methods. Offers unique coverage of the historical context, fundamentals of phase processing and provides several examples in speech communication. Provides a detailed review of many references and discusses the existing signal processing techniques required to deal with phase information in different applications involved with speech. The book supplies various examples and MATLAB® implementations delivered within the PhaseLab toolbox. Single-Channel Phase-Aware Signal Processing in Speech Communication is a valuable single-source for students, non-expert DSP engineers, academics and graduate students.
This work presents model-based algorithmic approaches for interference-invariant time delay estimation, which are specifically suited for the estimation of small time delay differences with a necessary resolution well below the sampling time. Therefore, the methods can be applied particularly well for transit-time ultrasonic flow measurements, since the problem of interfering signals is especially prominent in this application.
The maximum likelihood (ML) estimator for time-delay in a multisensor array in the presence of small phase incoherences is derived. The structure obtained is the canonical ML estimator with a correction term to compensate for the phase. The Cramer-Rao matrix bound is developed and used to evaluate the standard deviation of the time-delay estimate (TDE) as a function of system parameters such as number of sensors, phase variance, bandwidth and center frequency of the source. The curves indicate a sensitivity, at low input SNR, of the TDE variance to phase standard deviation and signal center frequency. (Author).
Time-delay estimation is important in a wide range of applications in oceanic engineering. In this paper, we present a novel time-delay estimation algorithm based on maximum likelihood theory for the case that the measurements are corrupted by colored or nonuniform zero-mean Gaussian noise. It turns out that the likelihood function associated with the problem is highly oscillatory, and we propose a computationally efficient technique to maximize this function. Our algorithm first obtains an initial estimate based on a smooth approximate cost function, and then refines this estimate based on the true cost function. Simulation results show that our estimator outperforms a traditional phaseshift based estimator, and that the estimation error approaches the Cramer-Rao bound (CRB) when the signal-to-noise ratio (SNR) increases without bound.
The multiple signal demixing and parameter estimation problems that result from the impacts of background noise and interference are issues that are frequently encountered in the fields of radar, sonar, communications, and navigation. Research in the signal processing and control fields has always focused on improving the estimation performance of parameter estimation methods at low SNR and maintaining the robustness of estimations in the presence of model errors. This book presents a universal and robust relaxation estimation method (RELAX), and introduces its basic principles and applications in the fields of classical line spectrum estimation, time of delay estimation, DOA estimation, and radar target imaging. This information is explained comprehensively and in great detail, and uses metaphors pertaining to romantic relationships to visualize the basic problems of parameter estimation, the basic principles of the five types of classical parameter estimation methods, and the relationships between these principles. The book serves as a reference for scientists and technologists in the fields of signal processing and control, while also providing relevant information for graduate students in the related fields.