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Estimation and tracking of generally non-stationary Markov processes is of paramount importance for applications such as localization and navigation. In this paper, ad hoc Wireless Sensor Networks (WSNs) offer decentralized Kalman Filtering (KF) based algorithms with documented merits over centralized alternatives. Adhering to the limited power and bandwidth resources WSNs must operate with, this paper introduces two novel decentralized KF estimators based on quantized measurement innovations. In the first quantization approach, the region of an observation is partitioned into contiguous, non-overlapping intervals where each partition is binary encoded using a block of bits.
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