Distributed Bounded-Error Parameter and State Estimation in Networks of Sensors
Numerical Validation in Current Hardware Architectures
Decentralized Communication-Aware Motion Planning in Mobile Networks: An Information-Gain Approach
Journal of Intelligent and Robotic Systems
Power-efficient dimensionality reduction for distributed channel-aware kalman tracking using WSNs
IEEE Transactions on Signal Processing
Transmission rate allocation in multisensor target tracking over a shared network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Target tracking in wireless sensor networks using compressed Kalman filter
International Journal of Sensor Networks
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Cooperative multi-robot localization under communication constraints
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Weighted average approach to quantized measurement fusion in wireless sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Low-power distributed Kalman filter for wireless sensor networks
EURASIP Journal on Embedded Systems
Adaptive quantized target tracking in wireless sensor networks
Wireless Networks
Networked strong tracking filters with noise correlations and bits quantization
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Interacting multiple sensor filter
Signal Processing
Quantized steady-state kalman filter in a wireless sensor network
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Doppler effect on target tracking in wireless sensor networks
Computer Communications
Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks
Journal of Signal Processing Systems
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When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations-a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic processes, whereby the low communication cost is effected by requiring the transmission of a single bit per observation. Following a Kalman filtering (KF) approach, we develop recursive algorithms for distributed state estimation based on the sign of innovations (SOI). Even though SOI-KF can afford minimal communication overhead, we prove that in terms of performance and complexity it comes very close to the clairvoyant KF which is based on the analog-amplitude observations. Reinforcing our conclusions, we show that the SOI-KF applied to distributed target tracking based on distance-only observations yields accurate estimates at low communication cost