We outline a framework for collaborative signal processing in distributed sensor networks. The ideas are presented in the context of tracking multiple moving objects in a sensor field. The key steps involved in the tracking procedure include event detection, target classification, and estimation and prediction of target location. Algorithms for various tasks are discussed with an emphasis on classification. Results based on experiments with real data are reported which provide useful insights into the essential nature of the problems. Issues, challenges and directions for future research are identified.

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.

Abstract. While GPS is a relatively mature technology, its susceptibility to radio frequency interference (RFI) is substantial. Various investigations, including the Volpe Report (Volpe, 2001) which was the result of a US Presidential Decision Directive (PDD-63) assigned to the Department of Transportation (DOT), have recommended that methods should be developed to monitor, report and locate interference sources for applications where loss of GPS is not tolerable. With GPS becoming an integral utility for developed society, the significance of research projects that enhance and expand the capabilities of GPS RFI localisation is highly important. In response to this requirement for GPS interference localisation, a novel technique called “Inverse Diffraction Parabolic Equation Localisation System ” (IDPELS) has

The performance versus complexity tradeoff is particularly acute in sensor networks since collaboration between nodes comes at the cost of exchanging information between them. Consequently, in the present form, all our algorithms are based on processing a single sensing modality, such as seismic or acoustic. Furthermore, current detection and classification algorithms are based on single-node processing, whereas localization and tracking algorithms require collaboration between nodes. Our main emphasis in this article is on target classification, which is arguably the most challenging signal processing task in the context

program is developing software for networks of distributed microsensors. This paper outlines the program goals, technical challenges, and some of the ongoing research. Specific ongoing work in collaborative signal and information processing and fusion is emphasized.