An inverse QRD-RLS algorithm for linearly constrained minimum variance adaptive filtering
نویسندگان
چکیده
منابع مشابه
An inverse QRD-RLS algorithm for linearly constrained minimum variance adaptive filtering
In this paper an inverse QR decomposition based recursive least-squares algorithm for linearly constrained minimum variance filtering is proposed. The proposed algorithm is numerically stable in finite precision environments and is suitable for implementation in systolic arrays or DSP vector architectures. Its performance is illustrated by simulations of a blind receiver for a multicarrier CDMA...
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The main limitation of FQRD-RLS algorithms is that they lack an explicit weight vector term. Furthermore, they do not directly provide the variables allowing for a straightforward computation of the weight vector as is the case with the conventional QRD-RLS algorithm, where a back-substitution procedure can be used to compute the coefficients. Therefore, the applications of the FQRD-RLS algorit...
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Shepherd and McWhirter proposed a QRD-RLS algorithm for adaptive filtering with linear constraints. In this paper, the numerical properties of this algorithm are considered. In particular, it is shown that the computed weight vector satisfies a set of constraints which are perturbed from the original ones, the amount of perturbation being dependent on the wordlength. The linearly constrained FL...
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ژورنال
عنوان ژورنال: Signal Processing
سال: 2013
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2012.11.002