Application and Modeling of a Magnetic WSN for Target Localization

This research presents the development and experimental validation of a magnetic Wireless Sensor Network (WSN) designed for the detection, identification, and localization of ferromagnetic targets in indoor environments. The system employs MICAz motes integrated with Honeywell HMC1002 magnetometers, organized in a sparse, tree-based topology. Target detection is achieved by measuring disturbances in the Earth’s magnetic field caused by the presence of ferromagnetic objects. These variations are processed using an Orthogonal Matching Pursuit (OMP) algorithm, which enables accurate spatial localization and classification based on magnetic field signatures.

The study introduces the Detection, Identification, and Sequential Localization (DISL) framework, which supports both single and multi-target tracking. A key innovation is the use of a sparse signal processing approach to create parametric dictionaries that map sensor readings to target locations and profiles. This allows not only proximity detection but also fine-grained spatial resolution, achieving up to 72% localization accuracy at 10×10 cm granularity and 98% accuracy at quadrant-level resolution. Target identification accuracy reaches 100% when the entry point is known, and 48% in the absence of such prior information.

In addition to the algorithmic contributions, the study provides a comprehensive evaluation of real-world implementation challenges. Environmental factors such as temperature variations, power supply instability, and ambient magnetic noise are shown to significantly affect sensor measurements and system reliability. Mitigation strategies, including power management and sensor calibration, are discussed to enhance network longevity and performance.

Overall, this work establishes a scalable and energy-efficient methodology for magnetic target localization in WSNs and provides practical guidelines for deploying such systems in resource-constrained environments. The insights gained lay the foundation for future enhancements including Kalman Filter-based tracking, multi-target discrimination, and larger-scale deployments for smart surveillance and monitoring applications.

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