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Performance Study on Detection of the Number of Signals in the Presence of Spatial Colored Noise

Qunfei Zhang

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Abstract

In array signal processing,signal number detection is a necessary step for high-resolution directions-of-arrival(DOA) estimation.With the assumption of white noise and spatially colored noise,many methods have been proposed for detecting the number of signals.This paper focuses on several methods for detection of the number of signal sources,especially signals in presence of spatially correlated noise.Each method employs two well-separated arrays placed in a colored noise field to receive distinct signals.The noise models for the two arrays are chosen as spatial complex ARMA model.

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What this paper is about

In array signal processing,signal number detection is a necessary step for high-resolution directions-of-arrival(DOA) estimation.With the assumption of white noise and spatially colored noise,many methods have been proposed for detecting the number of signals.This paper focuses on several methods for detection of the number of signal sources,especially signals in presence of spatially correlated noise.Each method employs two well-separated arrays placed in a colored noise field to receive distinct signals.The noise models for the two arrays are chosen as spatial complex ARMA model.

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Available abstract

In array signal processing,signal number detection is a necessary step for high-resolution directions-of-arrival(DOA) estimation.With the assumption of white noise and spatially colored noise,many methods have been proposed for detecting the number of signals.This paper focuses on several methods for detection of the number of signal sources,especially signals in presence of spatially correlated noise.Each method employs two well-separated arrays placed in a colored noise field to receive distinct signals.The noise models for the two arrays are chosen as spatial complex ARMA model.

Key concepts: Colors of noise, Computer science, Colored, Noise (video), SIGNAL (programming language), White noise, Detection theory, Speech recognition

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