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• W1518380561 abstract "The Multiple SIgnal Classification (A.IUSIC) algorithm developed in the late 70's was the first vector subspace approa.ch used to a.ccura.tely determine the arrival angles of signal wavefronts impinging upon a.n a.rray of sensors. As facilitated by the geometry associa,ted with the common uniform linear a.rray of sensors, a root-based formulation was cleveloped to repla.ce the computa.tionally intensive spectral search process and was found to offer a.n enhal~cecl resolution capa.bility in the presence of two closely-spa,cecl signals. Operation i 1-1 heamspa.ce, where sectors of space are individually probed via a pre-processor operat.ing on the sensor data, was found to offer both a performance benefit and a reclucetl compu tationa.1 coinplexi ty resulting from the reduced da t a dimension associa.ted wit11 11ea.mspace processing. Little progress, however, has been nlatle i n the development of ; L cornputationally efficient Root-MUSIC algorithm in a hea,n~spa.ce setting. Two approiaches of efficiently arriving a t a Root-MUSIC for~nulation in bea,nlspace axe developed and analyzed in this Thesis. In the first approach, a structura.1 constraint is placed on. the beamforming vectors tha t can he exploited to yielcl a. redl.lced order polynomial wllose roots provide information on the signal a,nival a.ngles. rl'lie sccoild a,pproa.ch is considerably more general, and hence, a.pplicable to a.llj: ~ec~ ,o r sul,spa.cc a.ngle esti~nation algorithm. In this approa.cl1, cla,ssica.l multirate tligit.a,l signal processing is applied to effectively reduce the dinleilsioll of the vectors t1ia.t span the signal subspace, leading to an efficient beamspa.ce R.oot-MUSIC (or E:SPII.I?') algal-ithm. An auxiliaay, yet importa.nt,, obser~a~t,ion is shown tlo adlo~v a, real-va.lued eigenana,lysis of the beamspa,ce s a i n ~ l e covaria.nce llla,tris to provide a computational savings as well as a performance benefit, particularly i n t,lle case of correlated signal scenes. A rigorous theoretical analysis, based upon derived large-sample statistics of the signal subspace eigenvectors, is included to provide insight into the operation (sf the two algorithmic methodologies employing the real-valued processing enhancement. Numerous simulations are presented to validate the theoretical angle bias and variance expressions as well as to assess the merit of the two beamspace approaches." @default.
• W1518380561 created "2016-06-24" @default.
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• W1518380561 date "1994-01-01" @default.
• W1518380561 modified "2023-09-27" @default.
• W1518380561 title "Efficient beamspace eigen-based direction of arrival estimation schemes" @default.
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• W1518380561 hasPublicationYear "1994" @default.
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