Adaptive Data Fusion in Inertial Sensors and Data Quality Analysis of Sensor Networks

This paper investigates microelectromechanical systems (MEMS) technology sensors. The device- and system-level analyses are performed. An investigation is focused on multi-degree-of-freedom (MDOF) microsystem-technology inertial sensors which are widely used in mobile devices, surveillance and reconnaissance platforms, wearable electronics, guidance and navigation systems, etc. Open problems of data quality (DQ) in sensor networks, evaluation of sensor data, data fusion, DQ improvement, and data homogeneity in MDOF sensors are addressed. The DQ metrics design and their use are reported. Our research and algorithmic developments are focused on data assertion, adaptive signal processing, asynchronous data fusion and data management from heterogeneous multiple sensors and sensor networks. Using a serial protocol I
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C interface, considered MDOF sensors can fuse the directly measured data, as well as output the post-processed data. The integrated sensors (triaxial accelerometers, gyroscopes and magnetometers) ensure physics-consistent data-intensive processing, advance modalities, interoperability, integration, compliance and redundancy. Alternative solutions in adaptive filtering and near-real-time reconfigurable signal processing are considered to enable dynamic range, improve precision and accuracy, ensure robustness, minimize uncertainties, etc. The experimental results are reported.