Adaptive super-exponential methods for blind multichannel equalization

Abstract

An adaptive super-exponential algorithm for multichannel fractionally-spaced blind equalization is proposed. In the noise-free case, oversampling the received signal in space or time leads to a rank-deficient covariance matrix of the corresponding vector process. This seems to be the major obstacle towards a multichannel adaptive super-exponential algorithm. In the absence of additive noise, the optimal multichannel equalizer setting can also be found as the solution of a suitably chosen quadratic cost function. In the presence of a moderate amount of noise, minimization of the same cost function should result in an equalizer setting close to the optimal one. The optimization, however, can now be performed recursively using a stabilized RLS algorithm. Experimental results with underwater acoustic communication data demonstrate the good performance of the suggested method.

Variable observation window length blind equalization detector for underwater acoustic communication

“…For underwater acoustic communication system with QAM modulation, the equalization performance can be improved by using the MMA [25][26][27][28]. In addition, at the cost of increasing complexity, the structure of blind equalization in conjunction with a multichannel combiner has been proven to be effective for improving system performance [29][30][31].…”

Blind deconvolution for robust signal estimation and approximate source localization

“…The virtual receiver algorithm uses a strategically located guide source, to remove many of the distorting effects of unknown multipath propagation (Siderius et al, 1997). Blind deconvolution in multipath sound channels has also been studied under a variety of statistical assumptions including, but not limited to, an adaptive super-exponential algorithm (Weber and Bohme, 2002), a least-squares criterion (Zeng et al, 2009), a hypothesis regarding the form of the prior probability density function (PDF) of the input signal (Roan et al, 2003), time-frequency signal processing (Martins et al, 2002), and multiple convolutions (Smith, 2003). Related, blind sourceseparation techniques also seek to reconstruct source signals (Xinhua et al, 2001).…”

Broadband sparse-array blind deconvolution using frequency-difference beamforming

“…shimah@umich.edu measurements from a known source (Siderius et al, 1997), higher order statistics (Broadhead and Pflug, 2000a,b), information criteria (Xinhua et al, 2001), adaptive algorithms (Weber and Bohme, 2002;Sibul et al, 2002), time frequency analysis (Martins et al, 2002), multiple convolutions (Smith, 2003), an assumption about the probability density function of the signal (Roan et al, 2003), and a least-squares criterion (Zeng et al, 2009). The blind deconvolution technique used in this study, synthetic time reversal or STR (Abadi et al, 2012), develops the requisite additional information from the assumption that sound is conveyed from the unknown source to the receiving array by modes or rays.…”