An Efficient Sensor Scheduling for Target Tracking

In this article, an efficient sensor scheduling is proposed which is an interesting issue by applying wireless sensor network (WSN) for addressing to track a mobile target. The proposed efficient and simple algorithm is mainly for assisting mobile sensors management that is deployed in tracking a considerable moving target. The presented novel method leads to minimize comparison number of sensor sequences and to reduce executive time for unconstrained optimization. Moreover, the simulation results show that it is in reduction of memory utilization, energy consumption and computational cost as well. The famous Kalman Filter technique was applied to estimate posteriori state of the target in order to minimize mean square error (MSE) value in prediction step. In addition, the propagation channel is assumed as additive white Gaussian noise (AWGN) model for significant the contribution. Numerical example reveals that the developed algorithm can obtain an optimal scheduling outcome by an easy implementation.

關鍵字

mobile target tracking ； MSE ； WSN ； Kalman Filter ； AWGN

並列摘要

於本論文中，經由利用無線感測網路（wireless sensor network, WSN）之有趣的議題，提出一有效之感測器排程，藉以針對移動式目標物加以追蹤。此一有效而簡易的演算法，主要著重於協助移動式感測器的管理，其佈署以追蹤顯著的移動性目標，比較於許多其他之感測器序向研究，本新穎之演算法可以導致最小的追蹤序列，而且可以降低無以限制的最佳化之演算時間，尤有甚者，模擬結果顯示其可以減少記憶體之使用，能量耗損與計算成本的費用。其中著名的卡門濾波（Kalman filter）技術也應用於對物體之事後狀態的估測，其使預期步驟之中可以將均方誤差（mean square error, MSE）最小化，此外，假設信號之行進的通道係以加法性白色高斯雜訊（AWGN）為模型，藉以突顯本文之貢獻程度。數值之演算倒顯示出本文所研發之演算法，能夠藉由簡易的實驗獲得最佳之排程結果。

並列關鍵字

移動目標追蹤；均方誤差；無線感測網路；卡門濾波；加法性白色高斯雜訊

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