| Summary: | 碩士 === 國立中山大學 === 電機工程學系研究所 === 99 === We consider the topic of target tracking in this thesis. Target tracking is one of the applications in wireless sensor networks (WSNs). Clustering approach prolongs sensor’s lifetime and provides better data aggregation for WSNs. Most previous researches assumed that cluster regions are disjointed, while others assigned overlapping cluster regions, and utilized them in some applications, including inter-cluster routing and time synchronization. However, in overlapping clustering, processing of redundant sensing data may impair system performance. We present a regular distributed overlapping WSN in this thesis. The network is based on two kinds of sensors: (1) high-capability sensors, which are assigned as cluster heads (CHs), responsible for data processing and inter-cluster communication, (2) normal sensors, which are in a larger number when comparing with the high-capability sensors, the function of normal sensors are to provide data to the CHs. We define several operating modes of CHs and sensors. WSN works more efficient under the settings. Since a target may be located in the overlapping region, redundant data processing problem exists. To solve the problem, we utilize Cholesky decomposition to decorrelate the measurement noise covariance matrices. The correlation will be eliminated during the process. In addition, we modify extended information filter (EIF) and adapt to the decorrelated data. The CHs track the target, fuse the information from other CHs, and implement distributed positioning. The simulations are based on ultra-wideband (UWB) environment, we have verified that the proposed scheme works more efficient under the setting of different modes. The performance with decorrelated measurement is better than that with correlated ones.
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