Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
On target tracking with binary proximity sensors
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Energy-quality tradeoffs for target tracking in wireless sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Detection and Tracking Using Particle-Filter-Based Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks
IEEE Transactions on Mobile Computing
Shaping Throughput Profiles in Multihop Wireless Networks: A Resource-Biasing Approach
IEEE Transactions on Mobile Computing
IEEE Transactions on Signal Processing
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Target Tracking by Particle Filtering in Binary Sensor Networks
IEEE Transactions on Signal Processing
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This paper presents a new detection algorithm and high speed/accuracy tracker for tracking ground targets in acoustic wireless sensor networks (WSNs). Our detection algorithm naturally accounts for the Doppler effect which is an important consideration for tracking higher-speed targets. This algorithm 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 this study. Our approach contrasts with previous approaches that employ more sophisticated tracking algorithms with higher computational complexity and use a power law detection model. The power law detection model is valid only for low speed targets and is susceptible to mismatch with detection by the sensors in the field. Our tracking model also enables us to uniquely study various environmental effects on track accuracy, such as the Doppler effect, signal collision, signal delay, and different sampling time. Our WSN tracking model is shown to be highly accurate for a moving 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.