By Topic

Performance Limit for Distributed Estimation Systems With Identical One-Bit Quantizers

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hao Chen ; Dept. of EECS, Syracuse Univ., Syracuse, NY, USA ; Varshney, P.K.

Full precision Crame??r-Rao lower bound (CRLB) where no quantization is assumed is often employed to evaluate and compare distributed estimation performance even though the sensor observations are quantized before any further processing. However, as it completely disregards quantization and often does not exist when the sensor observation noise is bounded, full precision CRLB is often too optimistic or not applicable. In this work, we determine the performance limit of a distributed estimation system with identical one-bit quantizers in terms of the metric minimax CRLB. The performance limit that a distributed estimation scheme with identical quantizers can achieve is found as well as the set of optimal noise distribution functions and quantizers. Compared to the full precision CRLB, the performance limit is shown to be a much tighter bound when the parameter range is relatively large and reveals the important role of the quantization system.

Published in:

Signal Processing, IEEE Transactions on  (Volume:58 ,  Issue: 1 )