Noise covariance modeling in array processing

B. Friedlander; A. J. Weiss

doi:10.1109/SSAP.1994.572467

Noise covariance modeling in array processing

B. Friedlander, A. J. Weiss

School of Electrical Engineering

University of California at Davis

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

We consider the problem of direction finding in the presence of colored noise whose covariance matrix is unknown. The ambient noise covariance matrix can be modeled by a sum of Hermitian matrices known up to a multiplicative scalar. Using this model, we estimate jointly the directions of arrival of the signals and the noise model parameters. Under certain conditions, it is possible to obtain unbiased and efficient estimates of the signal directions.

Original language	English
Pages	153-156
Number of pages	4
DOIs	https://doi.org/10.1109/SSAP.1994.572467
State	Published - 1994
Event	7th IEEE SP Workshop on Statistical Signal and Array Processing, SSAP 1994 - Quebec, Canada Duration: 26 Jun 1994 → 29 Jun 1994

Conference

Conference	7th IEEE SP Workshop on Statistical Signal and Array Processing, SSAP 1994
Country/Territory	Canada
City	Quebec
Period	26/06/94 → 29/06/94

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10.1109/SSAP.1994.572467

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abstract = "We consider the problem of direction finding in the presence of colored noise whose covariance matrix is unknown. The ambient noise covariance matrix can be modeled by a sum of Hermitian matrices known up to a multiplicative scalar. Using this model, we estimate jointly the directions of arrival of the signals and the noise model parameters. Under certain conditions, it is possible to obtain unbiased and efficient estimates of the signal directions.",

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AB - We consider the problem of direction finding in the presence of colored noise whose covariance matrix is unknown. The ambient noise covariance matrix can be modeled by a sum of Hermitian matrices known up to a multiplicative scalar. Using this model, we estimate jointly the directions of arrival of the signals and the noise model parameters. Under certain conditions, it is possible to obtain unbiased and efficient estimates of the signal directions.

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Noise covariance modeling in array processing

Abstract

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