Journal ArticleDOI
The resolution of overlapping echos
TLDR
A new method for estimating the number and arrival times for overlapping signals with a priori known shape from noisy observations received by a sensor is presented, based on a recently developed eigenstructure technique for multitarget direction finding with passive antenna arrays and exploits the structure of the received signal covariance matrix.Abstract:
We present a new method for estimating the number and arrival times for overlapping signals with a priori known shape from noisy observations received by a sensor. The method is based on a recently developed eigenstructure technique for multitarget direction finding with passive antenna arrays and exploits the structure of the received signal covariance matrix. This problem is important in various applications such as radar and sonar data processing, geophysical/seismic exploration, and biomedical engineering. In many of these applications, a known signal is launched into a scattering medium and the returning response-in the form of delayed overlapping echos in noise-has to be processed to yield information on the nature and location of scatterers. The method presented also solves more general problems of signal detection and resolution.read more
Citations
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Journal ArticleDOI
Detection of signals by information theoretic criteria
Mati Wax,Thomas Kailath +1 more
TL;DR: Simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented.
Journal ArticleDOI
MUSIC, maximum likelihood, and Cramer-Rao bound
Petre Stoica,Nehorai Arye +1 more
TL;DR: The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established.
Journal ArticleDOI
Structured Compressed Sensing: From Theory to Applications
Marco F. Duarte,Yonina C. Eldar +1 more
TL;DR: The prime focus is bridging theory and practice, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware in compressive sensing.
Journal ArticleDOI
Direction finding in the presence of mutual coupling
TL;DR: An eigenstructure-based method for direction finding in the presence of sensor mutual coupling, gain, and phase uncertainties is presented and is able to calibrate the array parameters without prior knowledge of the array manifold.
Journal ArticleDOI
Detection of the number of coherent signals by the MDL principle
Mati Wax,I. Ziskind +1 more
TL;DR: An approach is presented to the problem of detecting the number of sources impinging on a passive sensor array that is based on J. Rissanen's (1983) minimum description length (MDL) principle, and two slightly different detection criteria are derived, both requiring the estimation of the locations of the sources.
References
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TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
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Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood
Donald W. Tufts,Ramdas Kumaresan +1 more
TL;DR: In this paper, the frequency estimation performance of the forward-backward linear prediction (FBLP) method was improved for short data records and low signal-to-noise ratio (SNR) by using information about the rank M of the signal correlation matrix.
Statistical Inference in Factor Analysis
T. W. Anderson,Herman Rubin +1 more
TL;DR: This paper discusses some methods of factor analysis and considers some mathematical problems of the model, such as whether certain kinds of observed data determine the model uniquely, and treats the statistical problems of estimation and tests of certain hypotheses.
Journal ArticleDOI
Spatio-temporal spectral analysis by eigenstructure methods
TL;DR: In this paper, the eigenstructure of the covariance and spectral density matrices of the received signals is used for estimating the spatio-temporal spectrum of the signals received by a passive array.