Adaptive detection and estimation in the presence of useful signal and interference mismatches

Antonio De Maio, Silvio De Nicola, Yongwei Huang, Shuzhong Zhang, Alfonso Farina

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

This paper considers adaptive detection and estimation in the presence of useful signal and interference mismatches. We assume a homogeneous environment where the random disturbance components from the primary and secondary data share the same covariance matrix. Moreover, the data under test contains a deterministic interference vector in addition to the possible useful signal. We focus on the situation where an energy fraction of both the useful signal and the deterministic interference may lie outside their nominal subspaces (conical uncertainty model). Under these conditions, we devise a procedure for the computation of the joint maximum likelihood (ML) estimators of the useful signal and interference vectors, resorting to a suitable rank-one decomposition of a semidefinite program (SDP) problem optimal solution. Hence, we use the aforementioned estimators for the synthesis of adaptive receivers based on different generalized likelihood ratio test (GLRT) criteria. At the analysis stage, we assess the performance of the new detectors in comparison with some decision rules, available in open literature.

Original languageEnglish (US)
Pages (from-to)436-450
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume57
Issue number2
DOIs
StatePublished - 2009

Bibliographical note

Funding Information:
Manuscript received January 25, 2008; revised September 09, 2008. First published November 7, 2008; current version published January 30, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yonina C. Eldar. This work was performed while A. De Maio and S. De Nicola were visiting the Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong. This work was supported in part by the Hong Kong RGC Earmarked Grants CUHK418505 and CUHK418406, and by SELEX—Sistemi Integrati.

Keywords

  • Adaptive Detection
  • Non-Convex Quadratic Optimization
  • Radar Signal Processing
  • Semidefinite programming relaxation

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