Robust Target Localization From Binary Decisions in Wireless Sensor Networks

Wireless sensor networks (WSNs) are becoming important tools in various tasks, including monitoring and tracking of spatially occurring phenomena. These networks offer the capability of densely covering a large area, but at the same time are constrained by the limiting sensing, processing and power capabilities of their sensors. To complete the task at hand, the information collected by the sensor nodes needs to be appropriately fused. In this article we study the problems of estimating the location of a target and estimating its signal intensity. The proposed algorithms are based on the local vote decision fusion (LVDF) mechanism, where sensors first correct their original decisions using decisions of neighboring sensors. These corrected decisions are more accurate and robust and improve detection: however, they are correlated, which makes maximum likelihood estimation intractable. We adopt a pseudolikelihood formulation and examine several variants of localization and signal estimation algorithms based on original and corrected decisions using direct optimization methods. as well as an EM approach. Uncertainty assessments about the parameters of interest are provided using a parametric bootstrap technique. An extensive simulation study of the developed algorithms, along with several benchmarks, establishes the overall superior performance of the LVDF-based algorithms, especially in low signal-to-noise ratio environments. Extensions to tracking moving targets and localizing multiple targets also are considered.