A mean level adaptive detector using nonconcurrent data

Author

Gerlach, Karl

Author_Institution

Naval Res. Lab., Washington, DC, USA

Volume

30

Issue

1

fYear

1994

fDate

1/1/1994 12:00:00 AM

Firstpage

258

Lastpage

265

Abstract

Convergence results for a mean level adaptive detector (MLAD) are presented. The MLAD consists of an adaptive matched filter (for spatially correlated inputs) followed by a mean level detector (MLD). The optimal weights of the adaptive matched filter are estimated from one batch of data and applied to a statistically independent batch of nonconcurrent data. The threshold of the MLD is determined from the resultant data. Thereafter a candidate cell is compared against this threshold. Probabilities of false alarm and detection are derived as a function of the threshold factor, the order of the matched filter, the number of independent samples per channel used to calculate the adaptive matched filter weights, the number of samples used to set the MLD threshold, and the output signal-to-noise power ratio of the optimal matched filter. A number of performance curves are shown and discussed

Keywords

adaptive filters; filtering and prediction theory; matched filters; probability; signal detection; adaptive matched filter weights; candidate cell; mean level adaptive detector; mean level detector; nonconcurrent data; optimal matched filter; optimal weights; performance curves; probabilities of false alarm; signal-to-noise power ratio; spatially correlated inputs; statistically independent nonconcurrent data; threshold; Adaptive filters; Adaptive signal detection; Convergence; Covariance matrix; Detectors; Laboratories; Matched filters; Maximum likelihood detection; Maximum likelihood estimation; Probability; Signal to noise ratio; Testing;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.250426

Filename

250426