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

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Narjes Davari (Author) Other; António Pedro Aguiar (Author) FEUP

Journal

Title: IEEE Journal of Oceanic Engineering

Vol. 46

Pages: 1288-1301

ISSN: 0364-9059

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00T-QWZ

DOI: 10.1109/joe.2021.3057909

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14