TY - JOUR
T1 - The estimated signal parameter detector
T2 - Incorporating signal parameter statistics into the signal processor
AU - Ballard, Jeffrey A.
AU - Culver, Richard Lee
N1 - Funding Information:
Manuscript received December 15, 2007; revised September 23, 2008; accepted December 11, 2008. First published April 10, 2009; current version published May 13, 2009. This work was supported by the Office of Naval Research Undersea Signal Processing Program. Associate Editor: D. A. Abraham. J. A. Ballard is with the Applied Research Laboratories, Austin, TX 78758 USA (e-mail: [email protected]). R. L. Culver is with the Applied Research Laboratory, Pennsylvania State University, State College, PA 16804 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JOE.2009.2014926
PY - 2009
Y1 - 2009
N2 - Acoustic propagation through a time-varying and spatially varying environment can produce variations in the received signal over multiple observations, possibly degrading receiver performance. This paper presents a signal processing structure that utilizes knowledge of received signal statistics to recoup lost performance. Recent research has shown that received signal parameter statistics can be calculated using Monte Carlo simulation and knowledge of ocean environment properties and processes. The processor possesses an estimator-correlator structure, and is referred to in this paper as the estimated signal parameter detector (ESPD). To demonstrate ESPD performance, the derivation is implemented to distinguish between monotone sinusoids with Gaussian-distributed amplitudes with identical means but different variances, embedded in zero-mean white Gaussian noise. In general, the amplitude distributions can possess any form and the noise distribution must belong to a general class of probability density functions (pdfs). The present assumptions allow for analytical results, and performance of the ESPD is seen to depend upon the signal-to-noise ratio (SNR) as well as the difference between the amplitude variances. Larger SNR and greater difference in amplitude variance result in better receiver performance, eventually leading to an asymptotic performance bound prediction.
AB - Acoustic propagation through a time-varying and spatially varying environment can produce variations in the received signal over multiple observations, possibly degrading receiver performance. This paper presents a signal processing structure that utilizes knowledge of received signal statistics to recoup lost performance. Recent research has shown that received signal parameter statistics can be calculated using Monte Carlo simulation and knowledge of ocean environment properties and processes. The processor possesses an estimator-correlator structure, and is referred to in this paper as the estimated signal parameter detector (ESPD). To demonstrate ESPD performance, the derivation is implemented to distinguish between monotone sinusoids with Gaussian-distributed amplitudes with identical means but different variances, embedded in zero-mean white Gaussian noise. In general, the amplitude distributions can possess any form and the noise distribution must belong to a general class of probability density functions (pdfs). The present assumptions allow for analytical results, and performance of the ESPD is seen to depend upon the signal-to-noise ratio (SNR) as well as the difference between the amplitude variances. Larger SNR and greater difference in amplitude variance result in better receiver performance, eventually leading to an asymptotic performance bound prediction.
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U2 - 10.1109/JOE.2009.2014926
DO - 10.1109/JOE.2009.2014926
M3 - Article
AN - SCOPUS:67349143353
SN - 0364-9059
VL - 34
SP - 128
EP - 139
JO - IEEE Journal of Oceanic Engineering
JF - IEEE Journal of Oceanic Engineering
IS - 2
ER -