Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach
- URL: http://arxiv.org/abs/2207.06903v1
- Date: Wed, 22 Jun 2022 08:15:11 GMT
- Title: Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach
- Authors: Eran Vertzberger and Itzik Klein
- Abstract summary: Attitude determination using the smartphone's inertial sensors poses a major challenge.
Data-driven techniques are employed to address that challenge.
A hybrid deep learning and model based solution for attitude estimation is proposed.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Attitude determination using the smartphone's inertial sensors poses a major
challenge due to the sensor low-performance grade and variate nature of the
walking pedestrian. In this paper, data-driven techniques are employed to
address that challenge. To that end, a hybrid deep learning and model based
solution for attitude estimation is proposed. Here, classical model based
equations are applied to form an adaptive complementary filter structure.
Instead of using constant or model based adaptive weights, the accelerometer
weights in each axis are determined by a unique neural network. The performance
of the proposed hybrid approach is evaluated relative to popular model based
approaches using experimental data.
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