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Heterogeneous Multi-Task Learning for Multiple Pseudo-Measurement Estimation to Bridge GPS Outages

Shuangqiu Lu,Yilin Gong,Haiyong Luo,Fang Zhao,Zhao-hui Li,Jinguang Jiang

Published 2021 in IEEE Transactions on Instrumentation and Measurement

ABSTRACT

To enhance the performance of the inertial navigation system (INS)/global position system (GPS) integrated navigation system for the land vehicle during GPS outages is an extremely challenging task. Though existing researches have made reasonable progress in positioning accuracy, they largely ignore sophisticated vehicle stopping events, and the further improvement of positioning performance is urgently needed in complex urban environments. In this article, we propose a heterogeneous multi-task learning (MTL) structure with a shared de-noising process to conduct pseudo-GPS position prediction and zero-velocity detection. The raised model builds upon three vital parts: 1) a shared de-noising convolutional autoencoder (CAE), which can effectively filter the measurement noises in the original inputs and provide more clean data for subsequent calculations without the ground-truth sensor data; 2) a predictor that uses a deep temporal convolutional network (TCN) to predict pseudo-GPS position to bridge GPS gaps; and 3) a robust zero-velocity detector that utilizes a 1-D deep convolutional neural network to accurately detect the vehicle stationary pattern, allowing for timely correcting the velocity and heading. Our proposed MTL model is evaluated on extensive practical road data and achieves a root mean square error of 3.794 m for 120-s GPS outages under long-term vehicle stopping scenarios, which obviously outperforms the stand-alone long short-term memory, TCN, and TCN + CAE. Experimental results also demonstrate that our proposed MTL method yields a remarkable accuracy of over 99.0% for vehicle stationary detection.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    IEEE Transactions on Instrumentation and Measurement

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TIM.2020.3028438
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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