Navigation-Based learning for survey trajectory classification in autonomous underwater vehicles
| Autor: | Helen Hastie, David M. Lane, M. De Lucas Alvarez |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Data processing
Artificial neural network business.industry Computer science Context (language use) 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre System monitoring 01 natural sciences 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Computer vision Artificial intelligence Time series business Hidden Markov model Baseline (configuration management) computer 0105 earth and related environmental sciences |
| Zdroj: | MLSP |
| DOI: | 10.1109/mlsp.2017.8168137 |
| Popis: | Timeseries sensor data processing is indispensable for system monitoring. Working with autonomous vehicles requires mechanisms that provide insightful information about the status of a mission. In a setting where time and resources are limited, trajectory classification plays a vital role in mission monitoring and failure detection. In this context, we use navigational data to interpret trajectory patterns and classify them. We implement Long Short-Term Memory (LSTM) based Recursive Neural Networks (RNN) that learn the most commonly used survey trajectory patterns from surveys executed by two types of Autonomous Underwater Vehicles (AUV). We compare the performance of our network against baseline machine learning methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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