Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests

In this article, we address the problem of distributed detection of a noncooperative (unknown emitted signal) target with a wireless sensor network. When the target is present, sensors observe a (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things, one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The fusion center receives sensor bits via noisy binary symmetric channels and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e., the target position x(T)) observable only under H-1 hypothesis. Davies' framework is exploited herein to design the generalized forms of Rao and locally optimum detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold optimization is also proposed. The simulation results confirm the promising performance of our proposed approaches.

Automated summary

This article addresses the issue of distributed detection of a noncooperative target with a wireless sensor network, using one-bit sensor measurement quantization and investigating two quantization strategies. The proposed approaches, including Rao and locally optimum detection tests, show promising performance in simulation results.