This dissertation presents a new detection algorithm and high speed/accuracy tracker for tracking a single and multiple ground vehicles in noisy acoustic wireless sensor networks (WSNs). For tracking ground vehicles, acoustic WSNs have been regarded to be useful and increasingly available due to recent advancement of micro sensors technology with low cost. Requirements for WSNs applications are low computational and communication load to each sensor node as each sensor node runs with a low-power battery.
The new detection algorithm of this dissertation naturally accounts for the Doppler effect which is an important consideration for tracking higher-speed targets. The tracking system used in this study employs Kalman Filtering (KF) with the weighted sensor position centroid being used as the target position measurement. The weighted centroid makes the tracker to be independent of the detection model and changes the tracker to be near optimal, at least within the detection parameters used in the study of single target.
The new approach for a single target tracking contrasts with the previous approaches that employed more sophisticated tracking algorithms with higher computational complexity and used a power law detection model. The power law detection model, neglecting Doppler effect, is valid only for low speed targets and is susceptible to mismatch with detection by the sensors in the field. The tracking model also makes it possible to uniquely study various environmental effects on track accuracy, such as the Doppler effect, signal collision, signal delay, and different sampling time. The tracking model is shown to be highly accurate for a moving single target in both linear and accelerated motions. The computing speed is estimated to be 50∼100 times faster than the previous more sophisticated methods and track accuracy compares very favorably.
As the WSN systems face multiple targets in the real world, the study has been expanded to the multiple targets tracking including the environmental noise mitigation. Localization and tracking multiple targets which undergo merging and split in the noisy acoustic WSNs require a new approach for detection and tracking. Doppler effect is included in the detection model and an efficient noise mitigation algorithm is developed. A new rule-based tracking algorithm is also developed, which guarantees reliable tracking of multiple targets in noisy acoustic WSNs with very low computational complexity and high track accuracy. The tracking system guarantees much lower computational complexity and comparable track accuracy to more sophisticated algorithms of the previous work.
|Commitee:||Pan, David, Park, Moon-Gyu, Vaughn, Gregg L., Wells, Earl, Yoo, Seong-Moo|
|School:||The University of Alabama in Huntsville|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Alabama|
|Source:||DAI-B 74/08(E), Dissertation Abstracts International|
|Subjects:||Computer Engineering, Engineering, Systems science|
|Keywords:||Doppler effect, Environmantal noises, Kalman filter, Target tracking, Target detection, Wireless sensor networks|
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