An Informative Interpretation of Decision Theory: Scalar Performance Measures for Binary Decisions

Polcari, John

doi:10.1109/access.2014.2377593

Cited by 2 publications

(2 citation statements)

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“…The KLD as defined in ( 20) is not a true distance between two distributions because it is asymmetric and does not satisfy the triangle inequality [54]- [56]. The symmetric KLD (SKLD) is defined as…”

Section: Symmetric Kullback-leibler Divergencementioning

confidence: 99%

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Design and Statistical Analysis of Tapered Coprime and Nested Arrays for the Min Processor

Adhikari

Drozdenko

2019

IEEE Access

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Coprime and nested arrays are sparse sensor arrays that provide O(MN ) degrees of freedom using only O(M + N ) sensors. The signals sampled by these sparse arrays contain the aliasing artifact; the min processor is used to disambiguate this aliasing artifact. The min processor beampattern has the same main lobe width and resolution as the beampattern of a full uniform linear array (ULA) with many more sensors. However, the peak side lobe (PSL) height of the min processor beampattern is higher than the PSL height of a full ULA with the same taper and resolution if the sparse arrays are not extended. Extending the sparse arrays, while keeping the intersensor spacing fixed, can reduce the PSL height to the level of the full ULA. Until now, the analytical expressions of the extension factor required to match the PSL height have not been found for the min processor. Also, the analytical expressions of the probability density function (PDF), mean, and variance of the min processor output for non-uniform tapers have not been derived. In this paper, we derive both of these analytical expressions. We consider the uniform, Hamming, Hann, Blackman-Harris, and Dolph-Chebyshev tapers for both coprime and nested arrays. We use the derived PDF in evaluating detection performance metrics such as receiver operation characteristic, Kullback Leibler divergence, and symmetric Kullback Leibler divergence. Our results show that min processing is superior to conventional beamforming and product processing in Gaussian signal detection for a given extended coprime or nested array.INDEX TERMS Blackman-Harris taper, coprime arrays, Dolph-Chebyshev taper, Hamming taper, Hann taper, min processing, nested arrays, sparse arrays, uniform taper.

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Section: Symmetric Kullback-leibler Divergencementioning

confidence: 99%

“…D KL (f z1 ; f z0 ) has been interpreted as the generalized SNR (GSNR) and used as a detection metric [55], [56]. For a CBF processing linear array, the expression for SKLD simplifies to…”

Section: Symmetric Kullback-leibler Divergencementioning

confidence: 99%