Inertial Sensor Array Processing with Motion Models
Johan Wahlström
- 14:00 26th April 2018 ( Hilary Term 2018 )Conference Room, Level 2, Robert Hooke Building
Abstract
By arranging a large number of inertial sensors in an array and fusing their measurements, it is possible to create inertial sensor assemblies with a high performance-to-price ratio. In comparison with conventional inertial measurement units, inertial arrays offer enhanced estimation accuracy, increased dynamic range, sensor fault detection and isolation, estimation of measurement uncertainties, and direct estimation (i.e., not requiring differentiation) of angular acceleration. Recently, a maximum likelihood estimator for fusing inertial array measurements collected at a given sampling instance was developed. In this talk, the maximum likelihood estimator is extended by introducing a motion model and deriving a maximum a posteriori estimator that jointly estimates the array dynamics at multiple sampling instances. Simulation examples are used to demonstrate that the proposed sensor fusion method have the potential to yield significant improvements in estimation accuracy. Further, by including the motion model, we resolve the sign ambiguity of gyro-free implementations, and thereby open up for implementations based on accelerometer-only arrays.
Bio
Johan Wahlström received his BSc, MSc, and PhD at KTH Royal Institute of Technology, Stockholm, Sweden in 2013, 2014, and 2017, respectively. His main PhD research topic was smartphone-based vehicle telematics. In 2015, he spent one month at University of Porto and six months at Washington University in St. Louis as a visiting PhD student. In 2016, he spent two months at the MIT-startup Cambridge Mobile Telematics. He was accepted into the program of excellence in electrical engineering at KTH in 2014, and in 2015 was the youngest recipient of the Sweden-America foundation's research scholarship.
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