A dual-space approach to tracking and sensor management in wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Distributed online localization in sensor networks using a moving target
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed state representation for tracking problems in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Power conservation and quality of surveillance in target tracking sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
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
Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Joint multiple target tracking and classification in collaborative sensor networks
IEEE Journal on Selected Areas in Communications
Obstacle discovery in distributed actuator and sensor networks
ACM Transactions on Sensor Networks (TOSN)
On coverage issues in directional sensor networks: A survey
Ad Hoc Networks
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We study the problem of object tracking using highly directional sensors--sensors whose field of vision is a line or a line segment. A network of such sensors monitors a certain region of the plane. Sporadically, objects moving in straight lines and at a constant speed cross the region. A sensor detects an object when it crosses its line of sight, and records the time of the detection. No distance or angle measurements are available. The task of the sensors is to estimate the directions and speeds of the objects, and the sensor lines, which are unknown a priori. This estimation problem involves the minimization of a highly nonconvex cost function. To overcome this difficulty, we introduce an algorithm, which we call "adaptive basis algorithm." This algorithm is divided into three phases: in the first phase, the algorithm is initialized using data from six sensors and four objects; in the second phase, the estimates are updated as data from more sensors and objects are incorporated. The third phase is an optional coordinated transformation. The estimation is done in an "ad-hoc" coordinate system, which we call "adaptive coordinate system." When more information is available, for example, the location of six sensors, the estimates can be transformed to the "real-world" coordinate system. This constitutes the third phase.