Bridging the gap between sensor noise modeling and sensor characterization. (February 2018)

• Record Type: Journal Article
• Authors: Jerath, Kshitij; Brennan, Sean; Lagoa, Constantino
• Is Part Of: Measurement. Volume 116(2018)
• Page Start: 350
• Page End: 366
• Publication Date: 2018-02
• Subjects: Sensor phenomena and characterization -- Noise -- Noise measurement -- Allan variance; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.09.012
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: Outlines numerical simulation models for commonly found sensor noise types. Links noise model parameters to sensor noise characterization specifications. Provides method to find system performance bounds using simulated sensor data. Abstract: In prior works, the tasks of noise modeling and sensor characterization have typically been studied independently of each other. In spite of extensive research on noise modeling and sensor characterization, there still exists a need to bridge the gap between parameters used in sensor noise models, and quantities used to characterize commercially available sensors. The included work addresses this need by presenting tutorial-style exemplary analyses that relate noise model parameters to sensor characteristics for some common noise types found in sensors. Specifically, this work seeks to demonstrate that sensor noise characterization techniques can be applied to simulated sensor noise data to recover the original noise model parameters. The presented relationships between noise models and sensor characterization tools can help engineers and scientists numerically verify the expected performance bounds of their systems using simulated signals from commercially available sensors. Moreover, these numerical tools can also guide design engineers towards developing or selecting sensors with specifications especially suited to the design constraints of a given application.