Timestamp-Free Clock Parameters Tracking Using Extended Kalman Filtering in Wireless Sensor Networks
Abstract : Clock synchronization is crucial for applications in wireless sensor networks, such as event scheduling and data fusion. In practical wireless networks, environmental changes cause the oscillator to be imperfect. Thus, it is necessary to track the nonlinear varying clock dynamically. In this paper, we propose a timestamp-free clock skew (frequency difference) and offset (time difference) joint tracking algorithm based on extended Kalman filter (EKF), which can be embedded into the general network data flow to achieve long-term synchronization without additional communication overhead. To further improve energy efficiency of clock synchronization, this paper develops an EKF clock skew tracking algorithm for silent nodes in receiver-only synchronization. For the situation that silent node overhears synchronization information from multiple active nodes, we present multi-information timestamp-free synchronization and multi-observation clock skew fusion scheme for silent nodes. Simulation results show the effectiveness of the proposed tracking algorithms.
? The significance, applications and existing research works of Kalman filtering over wireless sensor networks (WSNs).
? Existing indoor location systems integrated with WSNs can be grouped into two classes based on their architectures.
? Another channel model extensively used in existing literatures is the Gilbert-Elliott model, which was first introduced into the context of Kalman filtering with intermittent observations in.
? We claim that the stationary Kalman filter is consistent with the existing results on Kalman filtering with intermittent observations.
? The implementation of nonlinear filters such as EKF and UKF in smart grids is also widely studied in existing literatures.
? The challenge in this estimation problem is that the quantization operator is discontinuous.
? Applications of quantized Kalman filters using 1 bit and 3 bits per observation are presented for a simulated target-tracking problem and an experimental multiple robot localization problem.
? In state-estimation problems, the innovations sequence is defined as the difference between the current observation and its prediction based on past observations.
? Target tracking based on distance-only measurements is a typical problem in bandwidth-constrained distributed estimation using WSNs for which an extended SOI-KF to nonlinear models appears to be attractive.
• The DKF algorithm proposed in is based on the standard Kalman filtering with one extended step where sensor nodes merge their estimates by a weighted average approach.
• However the measurement model is a nonlinear function of the state vector, which implies that standard Kalman filtering cannot be applied in this case since it is specifically proposed for linear systems.
• In, a class of non-degenerate systems is proposed, and the corresponding stability results are established.
• An optimal KCF is derived by minimizing the mean square estimation error at each sensor node, and a suboptimal filter is proposed for scalability considerations.
? The effect of packet losses in the MSE performance of the SOI-KF is illustrated for the extended version presented for target tracking.
? Dimensionality reduction for WSNs with a fusion center offers better MSE performance because all available data at each time instant are gathered and processed at the fusion center.
? Tracking performance is almost identical for both the reduced and standard extended Kalman filters.
? The MSE performances of the SOI-KF and the Kalman filter can be compared by looking at the MSE reductions at each iteration.
? In the ad hoc setup, only one sensor transmits per time slot, which further improves power efficiency.
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