Hardware support for research of the sensor fusion of inertial sensors

Andel, Jan; Šimák, Vojtech

doi:10.1109/ae54730.2022.9920062

Cited by 1 publication

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“…extracting information from sensors, they still lack the exploration of the optimal number of sensors required to attain specific accuracy levels, as well as the impact of noise on measurement performance. Although improving measurement accuracy under independent noise distribution with multiple identical sensors has been suggested in [16] and [17], they are limited to homogeneous sensors, and have not considered noise covariance of sensors.…”

Section: Introductionmentioning

confidence: 99%

Improving Measurement Accuracy With a Neuro-Inspired Multi-Sensor Approach

Wang

2024

IEEE Access

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In this paper, we present a novel approach that draws inspiration from the way the brain processes sensory information, using multiple sensors to provide redundant and complementary information that can be combined with machine learning techniques to improve accuracy and reduce noise. In particular, we train a machine learning model to estimate ground truth signals using response data obtained from multiple sensors exhibiting heterogeneity. After only one stage of training, our method can be applied under various conditions. We present simulation results demonstrating the effectiveness of our approach in reducing noise and improving accuracy in a variety of measurement scenarios. Our method achieves competitive outcomes in comparison to the Kalman filter without relying on historical data. The theoretical efficacy of our method is elucidated by establishing a connection with parallel Gaussian channels from information theory. Moreover, we provide estimation to the extent of performance improvement in relation to the increasing count of sensors. Our approach has the potential to be applied to a wide range of industries and fields.INDEX TERMS Machine learning, multi-sensor approach, neuro-inspired, parallel Gaussian channels.YUN WANG received the B.S. and M.

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Section: Introductionmentioning

confidence: 99%

Improving Measurement Accuracy With a Neuro-Inspired Multi-Sensor Approach

Wang

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Hardware support for research of the sensor fusion of inertial sensors

Cited by 1 publication

References 0 publications

Improving Measurement Accuracy With a Neuro-Inspired Multi-Sensor Approach

Improving Measurement Accuracy With a Neuro-Inspired Multi-Sensor Approach

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