Introduction to signal processing
Introduction to signal processing
Tracking a moving object with a binary sensor network
Proceedings of the 1st international conference on Embedded networked sensor systems
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
The capacity of wireless networks
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
Average consensus based scalable robust filtering for sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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The quantized measurement fusion problem for target tracking in a sensor network is investigated using a weighted average approach. The measurement in each local sensor is quantized by uniform quantization and then transmitted to a fusion center (FC). To estimate the state of the target in the FC, the quantized messages are first to be combined in a weighted average way instead of merging all the quantized messages to a vector. Then extended Kalman filtering (EKF) is employed to estimate the target state. Focuses are on tradeoff between bandwidth of each sensor and the global tracking accuracy. The closed-form solution to the optimization problem for bandwidth scheduling is given, where the mean square error (MSE) incurred by weighted average fusion is minimized subject to a constraint on the total energy consumption. Nonlinear Gaussian discrete-time system model following the EKF principle is employed. Simulation example is given to illustrate the proposed scheme can obtain average percentage of energy saving up to 37.2% with computational burden reduction 32%.