Matrix analysis
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
Energy-efficient broadcasting with cooperative transmissions in wireless sensor networks
IEEE Transactions on Wireless Communications
Management of target-tracking sensor networks
International Journal of Sensor Networks
A survey of communication/networking in Smart Grids
Future Generation Computer Systems
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
On optimizing sensing quality with guaranteed coverage in autonomous mobile sensor networks
Computer Communications
Survey on smart grid modelling
International Journal of Systems, Control and Communications
Information quality-aware tracking in uncertain sensor network
International Journal of Sensor Networks
Navigation algorithm for WSN mobile node on MH particle filtering improvement
International Journal of Sensor Networks
International Journal of Sensor Networks
Adaptive dual cluster heads collaborative target tracking in wireless sensor networks
International Journal of Sensor Networks
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Sensor nodes in Wireless Sensor Network (WSN) need to communicate with other sensor nodes and/or a fusion centre in order to accomplish target tracking task. The limited onboard energy and wireless bandwidth are critical issues in many WSNs. In this paper, we propose a Compressed Kalman Filtering (CKF) algorithm to reduce the totality of transmitted data, hence the total energy consumption as well as the wireless bandwidth. Based on the well-known Kalman Filtering (KF) algorithm, the proposed method introduces an additional operation which replaces the state error covariance matrix in the KF algorithm by a diagonal matrix. This diagonal matrix is chosen to be an upper bound of the original covariance matrix to prevent the divergence of the proposed algorithm. A suboptimal solution is derived for general case, i.e. for a system with arbitrary dimension. The derived suboptimal solution does not require much more computation than the standard KF and can be easily implemented in cheap simple sensor nodes. Better solutions are derived for the cases where target is travelling in either 1D space or 2D space. Simulation results show that our proposed algorithm can improve energy efficiency significantly with comparable tracking performance, as compared to the original KF algorithm.