xmlui.dri2xhtml.METS-1.0.item-contributor-funder:
Comunidad de Madrid
Sponsor:
S. Horstmann and P. J. Schreier were supported by the German Research
Foundation (DFG) under grant SCHR 1384/6-1. The work of D. Ramírez
was supported by the Ministerio de Ciencia, Innovación y Universidades
under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia,
Innovación y Universidades, jointly with the European Commission (ERDF),
under grant TEC2017-86921-C2-2-R (CAIMAN), by The Comunidad de
Madrid under grant Y2018/TCS-4705 (PRACTICO-CM), and by the German
Research Foundation (DFG) under grant RA 2662/2-1.
Project:
Gobierno de España. TEC2017-86921-C2-2-R/CAIMAN Gobierno de España. TEC2017-92552-EXP/aMBITION Comunidad de Madrid. Y2018/TCS-4705/PRACTICOCM
Keywords:
Cyclostationarity
,
Generalized likelihood ratio test (GLRT)
,
Locally most powerful invariant test (LMPIT)
,
Multiple-input multiple-output (MIMO) passive detection
This paper considers passive detection of a cyclostationary signal in two multiple-input multiple-output (MIMO) channels. The passive detection system consists of an illuminator of opportunity (IO), a reference array, and a surveillance array, each equipped wiThis paper considers passive detection of a cyclostationary signal in two multiple-input multiple-output (MIMO) channels. The passive detection system consists of an illuminator of opportunity (IO), a reference array, and a surveillance array, each equipped with multiple antennas. As common transmission signals of the IO are cyclostationary, our goal is to detect the presence of cyclostationarity at the surveillance array, given observations from both channels. To this end, we analyze the existence of optimal invariant tests, and we derive an alternative and more insightful expression for a previously proposed generalized likelihood ratio test (GLRT). Since we show that neither the uniformly most powerful invariant test (UMPIT) nor the locally most powerful invariant test (LMPIT) exist, we propose an LMPIT-inspired detector that is given by a function of the cyclic cross-power spectral density. We show that the LMPIT-inspired detector outperforms the GLRT, and both detectors outperform state-of-the-art techniques.[+][-]