Journal ArticleDOI
Asymptotically optimal detection in unknown colored noise via autoregressive modeling
TLDR
It is proven that for large data records the detection performance is identical to that of an optimal prewhitener and matched filter, and therefore the detector itself is optimal.Abstract:
The problem of detecting a known signal in colored Gaussian noise of unknown covariance is addressed. The noise is modeled as an autoregressive process of known order but unknown coefficients. By employing the theory of generalized likelihood ratio testing, a detector structure is derived and then analyzed for performance. It is proven that for large data records the detection performance is identical to that of an optimal prewhitener and matched filter, and therefore the detector itself is optimal. Simulation results indicate that the data record length necessary for the asymptotic results to apply can be quite small. Thus, the proposed detector is well suited for practical applications.read more
Citations
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Journal ArticleDOI
Advanced Radar Detection Schemes Under Mismatched Signal Models
TL;DR: It turns out that such solutions guarantee a wide operational range in terms of tunability while retaining, at the same time, an overall performance in presence of matched signals commensurate with Kelly's detector.
Journal ArticleDOI
Performance analysis of LMS adaptive prediction filters
TL;DR: It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights.
Journal ArticleDOI
Radar signal processing
TL;DR: In this paper, a review of recent developments in radar signal processing in the presence of clutter is presented, with particular reference to an air-traffic environment, and two different signal processing issues are considered.
Journal ArticleDOI
Highlights of statistical signal and array processing
Alfred O. Hero,Hagit Messer,J. Goldberg,David J. Thomson,M.G. Amin,Georgios B. Giannakis,Ananthram Swami,Jitendra K. Tugnait,Arye Nehorai,A.L. Swindlehurst,Jean-François Cardoso,Lang Tong,Jeffrey L. Krolik +12 more
TL;DR: This article represents an endeavor by the members of the SSAT-TC to review all the significant developments in the field of SSAP and introduces the recent reorganization of three technical committees of the Signal Processing Society.
Journal ArticleDOI
Model-based adaptive detection of range-spread targets
TL;DR: In this paper, the problem of detecting range-distributed targets in the presence of structured disturbance modelled as an autoregressive Gaussian process with unknown parameters is considered, and four detectors exploiting the asymptotic generalised likelihood ratio criterion are devised and assessed.
References
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Journal ArticleDOI
Spectrum analysis—A modern perspective
Steven Kay,S.L. Marple +1 more
TL;DR: In this paper, a summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented, including classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods.
Journal ArticleDOI
Digital signal processing for sonar
TL;DR: This paper is a tutorial which describes "main stream" sonar digital signal processing functions along with the associated implementation considerations to promote further cross-fertilization of ideas amongdigital signal processing applications in sonar, radar, speech, communications, seismology, and other related fields.
Journal ArticleDOI
Signal detection in Gaussian noise of unknown level: An invariance application
Louis L. Scharf,D. Lytle +1 more
TL;DR: It is shown that there exists a test of H_o versus H_1 that is UMP-invariant for a very natural group of transformations on the space of observations that permits choice of operating receiver thresholds and evaluation of performance characteristics.
Journal ArticleDOI
On the statistics of the estimated reflection coefficients of an autoregressive process
Steven Kay,J. Makhoul +1 more
TL;DR: In this paper, the exact statistics of the estimated reflection coefficients for an autoregressive process are difficult to determine, since almost all the common methods for estimating the reflection coefficients are maximum likelihood estimates for large data records, the asymptotic distribution of the estimates is multivariate Gaussian with a covariance matrix given by the Cramer-Rao bound.
Journal ArticleDOI
II. Sonar System Technology
TL;DR: In this article, the authors present the fundamentals of sonar system technology and present a system model for active and passive sonar operatmion, the signal waveforms, transmission and reception modes commonly used in echo ranging, the propagation effects and reverberation mechanisms of the acoustic channel, the multidimensional aspects of conventional beamforming and the feasibility of adaptive control in an operational environment, the effectiveness of gain control receivers and hard clipping for dynamic range compression and normalization of acoustic data, the techniques presently employed for signal detect,ion and parameter estimation-for video and aural presentation