Real-Time Outlier Detection Applied to a Doppler Velocity Log Sensor Based on Hybrid Autoencoder and Recurrent Neural Network

异常检测 自编码 离群值 计算机科学 人工智能 循环神经网络 人工神经网络 卡尔曼滤波器 深度学习 模式识别(心理学) 计算机视觉
作者
Narjes Davari,A. Pedro Aguiar
出处
期刊:IEEE Journal of Oceanic Engineering [Institute of Electrical and Electronics Engineers]
卷期号:46 (4): 1288-1301 被引量:10
标识
DOI:10.1109/joe.2021.3057909
摘要

This article presents a real-time outlier detection deep-learning (OD-DL)-based method using a hybridized artificial neural network (ANN) approach. We propose an unsupervised ANN scheme that runs in parallel, a denoising autoencoder (DAE) and a recurrent neural network (RNN). The DAE aims to reconstruct relevant/normal input data, whereas it seeks to ignore outliers; the RNN, with a recursive structure, is used to predict time-series data. As measurements arrive, two tasks are performed: 1) the outlier decision, which is based on a reconstruction error and an energy score criteria from the output difference between the DAE and the RNN; and 2) the training procedure for both DAE and RNN. The proposed OD-DL scheme is specifically targeted to address the outlier problem of the data generated by a Doppler velocity log (DVL) sensor installed on board of an autonomous underwater vehicle (AUV) to enhance the AUV navigation system performance. In particular, the DVL data enter into the OD-DL scheme whose output is fed into an AUV navigation system that runs an error-state Kalman filter that integrates the corrected DVL data with the measurements of an inertial measurement unit and a depth meter. The experimental results show that the AUV navigation system with the OD-DL method outperforms in terms of a more accurate estimated position when compared with the case that there is no outlier detection and with the case of a navigation system using a conventional outlier detection method, or other simpler deep-learning methods.
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