A new approach of decentralized detection
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Self-Organizing Sensor Networks for Integrated Target Surveillance
IEEE Transactions on Computers
Approaches to Multisensor Data Fusion in Target Tracking: A Survey
IEEE Transactions on Knowledge and Data Engineering
Fault-tolerant target localization in sensor networks
EURASIP Journal on Wireless Communications and Networking
An agent-oriented information processing architecture for sensor network applications
International Journal of Ad Hoc and Ubiquitous Computing
Multi-modal calibration of surveillance sensor networks
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Distributed network control for mobile multi-modal wireless sensor networks
Journal of Parallel and Distributed Computing
Networked computing in wireless sensor networks for structural health monitoring
IEEE/ACM Transactions on Networking (TON)
Real-time classification via sparse representation in acoustic sensor networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
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Autonomous networks of sensor platforms can be designed to interact in dynamic and noisy environments to determine the occurrence of specified transient events that define the dynamic process of interest. For example, a sensor network may be used for battlefield surveillance with the purpose of detecting, identifying, and tracking enemy activity. When the number of nodes is large, human oversight and control of low-level operations is not feasible. Coordination and self-organization of multiple autonomous nodes is necessary to maintain connectivity and sensor coverage and to combine information for better understanding the dynamics of the environment. Resource conservation requires adaptive clustering in the vicinity of the event. This paper presents methods for dynamic distributed signal processing using an ad hoc mobile network of microsensors to detect, identify, and track targets in noisy environments. They seamlessly integrate data from fixed and mobile platforms and dynamically organize platforms into clusters to process local data along the trajectory of the targets. Local analysis of sensor data is used to determine a set of target attribute values and classify the target. Sensor data from a field test in the Marine base at Twentynine Palms, Calif, was analyzed using the techniques described in this paper. The results were compared to "ground truth" data obtained from GPS receivers on the vehicles.