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Distributed Iteratively Quantized Kalman Filtering for Wireless Sensor Networks
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Abstract — Estimation and tracking of generally nonstationary Markov processes is of paramount importance for applications such as localization and navigation. In this context, ad hoc wireless sensor networks (WSNs) offer distributed Kalman ¿ltering (KF) based algorithms with documented merits over centralized alternatives. Adhering to the limited power and bandwidth resources WSNs must operate with, this paper introduces a novel distributed KF estimator based on quantized measurement innovations. The quantized obser-vations and the distributed nature of the iteratively quantized KF algorithm are amenable to the resource constraints of the ad hoc WSNs. Analysis and simulations show that KF-like tracking based on m bits of iteratively quantized innovations communicated among sensors exhibits MSE performance identical to a KF based on analog-amplitude observations applied to an observation model with noise variance increased by a factor of [1 − (1 − 2/π)m]−1. With minimal communication overhead, the mean-square error (MSE) of the distributed KF-like tracker based on 2-3 bits is almost indistinguishable from that of the clairvoyant KF. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Centralized Alternative Paramount Importance Abstract Estimation Quantized Obser-vations Ad Hoc Wsns Noise Variance Nonstationary Markov Quantized Measurement Innovation Distributed Nature Bandwidth Resource Wsns Documented Merit Observation Model Mean-square Error Clairvoyant Kf Kf Estimator Resource Constraint Ad Hoc Wireless Sensor Network Quantized Kf Algorithm Sensor Exhibit Mse Performance Identical Minimal Communication Overhead Wireless Sensor Network Analog-amplitude Observation Quantized Innovation 2-3 Bit Distributed Kf-like Tracker Limited Power |
| Content Type | Text |
| Resource Type | Article |
