Detection, classification, and tracking of targets

TLDR

Wireless sensor networks consist of many small, low‑power nodes that must efficiently exchange data and perform collaborative signal processing to monitor the physical world. The paper proposes CSP algorithms for distributed sensor networks to detect, track, and classify multiple maneuvering targets. The authors develop CSP routines that interface with UW‑API routing, enabling distributed detection, tracking, and classification across the network. An example demonstrates that integrating UW‑API and UW‑CSP yields effective detection and localization of targets.

Abstract

Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. 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 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). 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 becomes a reality.