Constrained Cramer-Rao bounds on source separation

Brian M. Sadler, R. Kozick
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引用次数: 2

Abstract

Constrained Cramer-Rao bounds (CRBs) are developed for multiple-input multiple-output (MIMO) scenarios, including both instantaneous and convolutive mixing. We find the convolutive MIMO Fisher information matrix (FIM) and study its properties. While this FIM is generally rank deficient, we establish equality constraints to achieve regularity. We employ the constrained CRB formulation of Gorman and Hero (1990) and Stoica and Ng (see IEEE SPL, vol.6, no.7, p.177-79, 1998), allowing the incorporation of side information into the bounds. This framework provides bounds on many algorithms proposed in the literature for MIMO channel and source estimation that exploit various kinds of side information, including semi-blind, constant modulus, and others.
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源分离的约束Cramer-Rao界
针对多输入多输出(MIMO)场景,包括瞬时混合和卷积混合,开发了约束Cramer-Rao边界(crb)。我们找到了卷积MIMO Fisher信息矩阵(FIM)并研究了它的性质。由于该FIM通常是秩不足的,我们建立了相等约束来实现正则性。我们采用Gorman和Hero(1990)以及Stoica和Ng(见IEEE SPL, vol.6, no. 6)的约束CRB公式。7, p.177-79, 1998),允许将边信息纳入边界。该框架为文献中提出的用于MIMO信道和源估计的许多算法提供了边界,这些算法利用各种侧面信息,包括半盲,恒模等。
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