Detection, classification, and tracking of targets - IEEE Signal Processing Magazine

Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and manipulation of the physical world in a tetherless fashion [1], [16], [17], [22], [23]. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks becomes a reality. In particular, there are two critical problems underlying successful operation of sensor networks: 1) efficient methods for exchanging information between the nodes and 2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API) [2]. We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques. Tracking multiple targets via a wireless sensor network is a very challenging, multifaceted problem and several research groups have tackled various aspects of it [3]-[8], [12], [13], [15], [18], [19], [21], [23], [25]. We consider the signal processing aspects of this problem under the constraints imposed by limited capabilities of the nodes as well as those associated with networking and routing.