NDLI: Using an information-theoretic measure to inform distributed radar placement for automatic target recognition

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Using an information-theoretic measure to inform distributed radar placement for automatic target recognition

Content Provider	IET Digital Library
Author	Wilcher, John S. Lanterman, Aaron D. Melvin, William L.
Abstract	The authors derive a measure suitable for identifying sensor placements that yield high target classification rates in a distributed radar environment. A distance measure is derived as an approximation lower bound of the Kullback–Leibler (KL) divergence between two probable target classes. A relationship between the KL divergence approximation and probability of correct classification (PCC) is demonstrated and used to identify sensor placements likely to yield higher PCC values. Two algorithms are proposed, optimal and approximate, for identifying good placements of a single transmitter and two receivers. A physics-based scattering model is employed to generate reflectivity data suitable for multi-sensor, multi-static classification analysis. KL and PCC data sets are analysed to demonstrate the effectiveness of the proposed distance measure and algorithms.
Starting Page	1098
Ending Page	1106
Page Count	9
ISSN	17518784
Volume Number	10
e-ISSN	17518792
Issue Number	Issue 6, Jul (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/10/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2015.0451
Journal	IET Radar, Sonar & Navigation
Publisher Date	2016-07-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Target Recognition Distance Measure Distributed Radar Placement Information-theoretic Measure KL Divergence Approximation Kullback-Leibler Divergence Multisensor Multistatic Classification Analysis PCC Value Physics-based Scattering Model Probability Probability-of-correct Classification Radar Equipment Radar Target Recognition Reflectivity Data Sensor Placements Signal Classification Signal Processing And Detection Statistics System And Application Target Classification Rate
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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