An Information Elasticity Framework for the Adaptive Matched Filter

The adaptive matched filter (AMF) uses a number of training samples observed by the radar to estimate the unknown disturbance covariance matrix of a cell under test. In general, as the number of homogeneous training samples increases, the detection performance of the AMF improves up to a theoretical limit (defined by the performance of a matched filter detector where the disturbance covariance is known). However, radar data are nonhomogeneous in practice. Consequently, a high number of training samples is typically undesirable, since nonhomogeneous training data cause detection performance to suffer. Thus, a decision maker (DM) must consider these tradeoffs when selecting this number of training samples, along with other decision parameters for the AMF. Using the concept of information elasticity, this tradeoff behavior is characterized for decisions that are relevant to a DM. A simple user defined constraint function is proposed, characterizing the relative cost of selecting different decisions. Using a multi-objective optimization (MOO) technique known as compromise programming, information overload is observed, in that increasing the cost of decisions improves performance up to a point, beyond which increasing the cost no longer provides meaningful benefit. Using this framework, a cost-efficient solution is selected.

© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.



Work Title An Information Elasticity Framework for the Adaptive Matched Filter
Open Access
  1. Ram M. Narayanan
  2. Andrew Z. Liu
  3. Muralidhar Rangaswamy
License In Copyright (Rights Reserved)
Work Type Article
  1. IEEE Transactions on Aerospace and Electronic Systems
Publication Date July 15, 2020
Publisher Identifier (DOI)
Deposited November 23, 2021




This resource is currently not in any collection.

Work History

Version 1

  • Created
  • Added An_Information_Elasticity_Framework_for_the_Adaptive_Matched_Filter.pdf
  • Added Creator Ram M. Narayanan
  • Added Creator Andrew Z. Liu
  • Added Creator Muralidhar Rangaswamy
  • Published
  • Updated
  • Updated