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

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 reconst...

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Vydané v:IEEE journal of oceanic engineering Ročník 46; číslo 4; s. 1288 - 1301
Hlavní autori: Davari, Narjes, Aguiar, A. Pedro
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Anomaly detection
Artificial neural networks
Autoencoder
autonomous underwater vehicle (AUV)
Autonomous underwater vehicles
Data analysis
Deep learning
Detection
Doppler sonar
Error correction
error-state Kalman filter (ESKF)
Inertial platforms
integrated navigation system
Kalman filters
Learning theory
Navigation
Navigation systems
Neural networks
Neurons
outlier detection (OD)
Outliers (statistics)
Real time
Real-time systems
recurrent neural network (RNN)
Recurrent neural networks
Sensors
Training
Underwater vehicles
Velocity
ISSN:0364-9059, 1558-1691
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Shrnutí: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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ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.2021.3057909