exploiting covariance structure signal de perry (10 résultats)

Paperback or Softback. Etat : New. Exploiting Covariance Structure for Signal Detection in Array Processing 0.37. Book.

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Taschenbuch. Etat : Neu. 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.

Etat : As New. Unread book in perfect condition.

Etat : New.

Paperback. Etat : new. Paperback. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Etat : Neu. Exploiting Covariance Structure for Signal Detection in Array Processing | Perry | Taschenbuch | Englisch | 2024 | tredition | EAN 9783384276643 | Verantwortliche Person für die EU: tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 108 pp. Englisch.