ハイブリッドモデル学習による適応的姿勢推定
Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach
スマートフォンの慣性センサ向けに、モデルベースの相補フィルタとニューラルネットを組み合わせ、加速度計の重みを学習で適応的に決める姿勢推定手法を提案した論文。
著者: Eran Vertzberger, Itzik Klein
分類: eess.SP, cs.LG, cs.RO
原文アブストラクト
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.
