Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
Design and Analysis of a Fast Local Clustering Service for Wireless Sensor Networks
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
Passive Localization: Large Size Sensor Network Localization Based on Environmental Events
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Distributed target tracking and boundary estimation in wireless sensor networks
International Journal of Autonomous and Adaptive Communications Systems
Target Counting under Minimal Sensing: Complexity and Approximations
Algorithmic Aspects of Wireless Sensor Networks
Surveillance with wireless sensor networks in obstruction: Breach paths as watershed contours
Computer Networks: The International Journal of Computer and Telecommunications Networking
Tracking people in indoor environments
ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
Management of target-tracking sensor networks
International Journal of Sensor Networks
Reliable and real-time data gathering in multi-hop linear wireless sensor networks
WASA'06 Proceedings of the First international conference on Wireless Algorithms, Systems, and Applications
International Journal of Ad Hoc and Ubiquitous Computing
Performance evaluation of sensor networks by statistical modeling and euclidean model checking
ACM Transactions on Sensor Networks (TOSN)
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We present a novel method for tracking the movement of people or vehicles in open outdoor environments using sensor networks. Unlike other sensor network-based methods, which depend on determining distance to the target or the angle of arrival of the signal, our cooperative tracking approach requires only that a sensor be able to determine if an object is somewhere within the maximum detection range of the sensor. We propose cooperative tracking as a method for tracking moving objects and extrapolating their paths in the short term. By combining data from neighboring sensors, this approach enables tracking with a resolution higher than that of the individual sensors being used. We employ statistical estimation and approximation techniques to further increase the tracking precision, and to enable the system to exploit the tradeoff between accuracy and timeliness of the results. We analyze the behavior of the cooperative tracking algorithm through simulation, focusing on the effects of approximation techniques on the quality of estimates achieved. This work focuses on acoustic tracking, however the presented methodology is applicable to any sensing modality where the sensing range is relatively uniform.