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• W2513604350 abstract "We consider an iterative design of independently operating local quantizers at nodes that should cooperate without interaction to achieve application objectives for distributed estimation systems. We suggest as a new cost function a probabilistic distance between the posterior distribution and its quantized one expressed as the Kullback Leibler (KL) divergence. We first present the analysis that minimizing the KL divergence in the cyclic generalized Lloyd design framework is equivalent to maximizing the logarithmic quantized posterior distribution on the average which can be further computationally reduced in our iterative design. We propose an iterative design algorithm that seeks to maximize the simplified version of the posterior quantized distribution and discuss that our algorithm converges to a global optimum due to the convexity of the cost function and generates the most informative quantized measurements. We also provide an independent encoding technique that enables minimization of the cost function and can be efficiently simplified for a practical use of power-constrained nodes. We finally demonstrate through extensive experiments an obvious advantage of improved estimation performance as compared with the typical designs and the novel design techniques previously published." @default.
• W2513604350 created "2016-09-16" @default.
• W2513604350 creator A5031626637 @default.
• W2513604350 date "2016-09-02" @default.
• W2513604350 modified "2023-09-26" @default.
• W2513604350 title "Probabilistic distance-based quantizer design for distributed estimation" @default.
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• W2513604350 doi "https://doi.org/10.1186/s13634-016-0389-0" @default.
• W2513604350 hasPublicationYear "2016" @default.
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