Paper Info
Title
Uniformly Most Powerful CFAR Test for Pareto-Target Detection in Pareto Distributed Clutter
Abstract
In the Radar context, Pareto distribution has been validated for both sea clutter and aircraft target under specific scenarios. Primarily, after the sea clutter is modeled as Pareto, some heuristic constant-false-alarm-rate (CFAR) processors appeared in the literature with the same form of adaptive thresholding that is derived for conventional exponential vs. exponential hypothesis testing (i.e., for detecting Swerling-I target in exponentially distributed clutter). Statistical procedures obtained under such idealistic assumptions cease to be optimal when applied to newer models esp. heavy tail distributions like Pareto. So, even to accommodate a wide range of application scenarios, in addition to Pareto modeled aircraft detection, we solve for heavy tail in Pareto vs. Pareto distributed lots from the composite hypothesis testing framework. Here, we derive the uniformly most powerful (UMP) test that complies CFAR property with-respect to the tail-index, using the least favorable density (lfd) concept. We further validate this CFAR property from extensive simulation results, and attribute that the geometric mean (GM)-CFAR is the optimal test in UMP sense.
Year
DOI
Venue
2020
10.1109/NCC48643.2020.9056098
2020 National Conference on Communications (NCC)
Keywords
DocType
ISBN
Pareto-target detection,Pareto distributed clutter,sea clutter,aircraft target,constant-false-alarm-rate processors,exponentially distributed clutter,heavy tail distributions,Pareto modeled aircraft detection,composite hypothesis testing,geometric mean-CFAR,least favorable density concept
Conference
978-1-7281-5121-2
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
John Bob Gali100.34
Priyadip Ray2429.26
Goutam Das35023.25