Sponsor:
This work was supported in part by the German Research Foundation (DFG) under
Contract SCHR 1384/3-2, and in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grant
CAIMAN TEC2017-86921-C2-2-R
Project:
Gobierno de España. TEC2017-86921-C2-2-R/CAIMAN
Keywords:
Almost cyclostationarity
,
Cycle period estimation
,
Detection
,
Multiple hypothesis test
,
Sample rate conversion
In array signal processing, the detection of the number of sources is an important step. Most approaches assume the signals to be embedded in white noise. However, this assumption is unrealistic in many scenarios. In this letter, we propose a strategy that canIn array signal processing, the detection of the number of sources is an important step. Most approaches assume the signals to be embedded in white noise. However, this assumption is unrealistic in many scenarios. In this letter, we propose a strategy that can handle colored noise. We model the source detection as a regression problem and apply information-theoretic criteria to determine the model order of the regression. We show simulations of different scenarios, where our approach outperforms traditional techniques.[+][-]