Maximum Likelihood (ML)-Based Quantizer Design for Distributed Systems

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Kim, Yoon Hak

  • 투고 : 2015.05.23
  • 심사 : 2015.07.07
  • 발행 : 2015.08.28

초록

We consider the problem of designing independently operating local quantizers at nodes in distributed estimation systems, where many spatially distributed sensor nodes measure a parameter of interest, quantize these measurements, and send the quantized data to a fusion node, which conducts the parameter estimation. Motivated by the discussion that the estimation accuracy can be improved by using the quantized data with a high probability of occurrence, we propose an iterative algorithm with a simple design rule that produces quantizers by searching boundary values with an increased likelihood. We prove that this design rule generates a considerably reduced interval for finding the next boundary values, yielding a low design complexity. We demonstrate through extensive simulations that the proposed algorithm achieves a significant performance gain with respect to traditional quantizer designs. A comparison with the recently published novel algorithms further illustrates the benefit of the proposed technique in terms of performance and design complexity.

키워드

Distributed compression;Generalized Lloyd algorithm;Maximum likelihood;Quantizer design;Sensor networks;Source localization

참고문헌

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피인용 문헌

  1. 1. Probabilistic distance-based quantizer design for distributed estimation vol.2016, pp.1, 2016, doi:10.6109/jicce.2015.13.3.152