Title

Asymptotically Optimal Detection in Unknown Colored Noise via Autoregressive Modeling

Document Type

Article

Date of Original Version

1-1-1983

Abstract

The problem of detecting a known signal in colored Gaussian noise of unknown covariance is addressed. The noise is modeled as an autoregressive process of known order but unknown coefficients. By employing the theory of generalized likelihood ratio testing, a detector structure is derived and then analyzed for performance. It is proven that for large data records the detection performance is identical to that of an optimal prewhitener and matched filter, and therefore the detector itself is optimal. Simulation results indicate that the data record length necessary for the asymptotic results to apply can be quite small. Thus, the proposed detector is well suited for practical applications. Copyright © 1983 by The Institute of Electrical and Electronics Engineers, Inc.

Publication Title

IEEE Transactions on Acoustics, Speech, and Signal Processing

Volume

31

Issue

4

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