A persymmetric modified-SMI algorithm

Lujing Cai; Hong Wang

doi:10.1016/0165-1684(91)90141-5

A persymmetric modified-SMI algorithm

Lujing Cai, Hong Wang

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

By exploring the covariance structure information to reduce the uncertainty in adaptive processing, this paper presents a Persymmetric Modified Sample Matrix Inversion algorithm (PMSMI), together with the closed-form expressions of probabilities of detection and false larm. The new algorithm, which requires less computation and has a Constant False Alarm Rate (CFAR) feature, can significantly outperform the corresponding unstructured MSMI, especially in a severely non-stationary/non-homogeneous interference environment.

Original language	English (US)
Pages (from-to)	27-34
Number of pages	8
Journal	Signal Processing
Volume	23
Issue number	1
DOIs	https://doi.org/10.1016/0165-1684(91)90141-5
State	Published - Apr 1991

Keywords

Adaptive detection
doubly symmetric covariance

ASJC Scopus subject areas

Control and Systems Engineering
Software
Signal Processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

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10.1016/0165-1684(91)90141-5

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abstract = "By exploring the covariance structure information to reduce the uncertainty in adaptive processing, this paper presents a Persymmetric Modified Sample Matrix Inversion algorithm (PMSMI), together with the closed-form expressions of probabilities of detection and false larm. The new algorithm, which requires less computation and has a Constant False Alarm Rate (CFAR) feature, can significantly outperform the corresponding unstructured MSMI, especially in a severely non-stationary/non-homogeneous interference environment.",

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AU - Cai, Lujing

AU - Wang, Hong

PY - 1991/4

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N2 - By exploring the covariance structure information to reduce the uncertainty in adaptive processing, this paper presents a Persymmetric Modified Sample Matrix Inversion algorithm (PMSMI), together with the closed-form expressions of probabilities of detection and false larm. The new algorithm, which requires less computation and has a Constant False Alarm Rate (CFAR) feature, can significantly outperform the corresponding unstructured MSMI, especially in a severely non-stationary/non-homogeneous interference environment.

AB - By exploring the covariance structure information to reduce the uncertainty in adaptive processing, this paper presents a Persymmetric Modified Sample Matrix Inversion algorithm (PMSMI), together with the closed-form expressions of probabilities of detection and false larm. The new algorithm, which requires less computation and has a Constant False Alarm Rate (CFAR) feature, can significantly outperform the corresponding unstructured MSMI, especially in a severely non-stationary/non-homogeneous interference environment.

KW - Adaptive detection

KW - doubly symmetric covariance

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A persymmetric modified-SMI algorithm

Abstract

Keywords

ASJC Scopus subject areas

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