Decentralized Estimation Using Learning Vector Quantization

Abstract

Decentralized estimation is an essential problem for a number of data fusion applications. In this paper we propose a variation of the Learning Vector Quantization (LVQ) algorithm, the Distortion Sensitive LVQ (DSLVQ), to be used for quantizer design in decentralized estimation. Experimental results suggest that DSLVQ results in high-quality quantizers and that it allows easy adjustment of the complexity of the resulting quantizers to computational constraints of decentralized sensors. In addition, DSLVQ approach shows significant improvements over the popular LVQ2 algorithm as well as the previously proposed Regression Tree approach for decentralized estimation.