Neuware -Array processing involves utilizing multiple sensors (e.g., antennas) to collect data from a spatial environment. The primary objective is to extract the desired signal from a mixture of noise and interference. Several techniques exist for signal detection, including beamforming, matched filtering, and likelihood ratio tests. These methods typically rely on assumptions about the signal and noise characteristics.However, real-world environments often violate these assumptions. Noise may not be purely white (uncorrelated) and can exhibit spatial coherence. Additionally, interference might be structured and non-stationary. Here's where exploiting the covariance structure of the received data becomes advantageous.tredition GmbH, Heinz-Beusen-Stieg 5, 22926 Ahrensburg 108 pp. Englisch.

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T�tulo: Exploiting Covariance Structure for Signal Detection in Array Processing
Editor: Tredition Jul 2024
Fecha de publicaci�n: 2024
Idioma: Ingl�s
ISBN 10: 3384276647
ISBN 13: 9783384276643
Encuadernaci�n: Taschenbuch
Estado: Neu
Dimensiones: 234�mil�metros de ancho por 155 de alto por 8 de largo
Peso: 197 gramos

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Array processing involves utilizing multiple sensors (e.g., antennas) to collect data from a spatial environment. The primary objective is to extract the desired signal from a mixture of noise and interference. Several techniques exist for signal detection, including beamforming, matched filtering, and likelihood ratio tests. These methods typically rely on assumptions about the signal and noise characteristics. However, real-world environments often violate these assumptions. Noise may not be purely white (uncorrelated) and can exhibit spatial coherence. Additionally, interference might be structured and non-stationary. Here's where exploiting the covariance structure of the received data becomes advantageous.

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