Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. Howev...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 20; p. 8650
Main Authors: Jin, Xue-Bo, Chen, Wei, Ma, Hui-Jun, Kong, Jian-Lei, Su, Ting-Li, Bai, Yu-Ting
Format: Journal Article
Language:English
Published: Basel MDPI AG 23.10.2023
MDPI
Subjects:
Accuracy
Algorithms
Analysis
Big Data
Computational linguistics
Datasets
Deep learning
Driverless cars
Efficiency
Evaluation
Kalman filter
Language processing
long- and short-term memory network
Machine learning
Natural language interfaces
Natural language processing
Neural networks
Parameter estimation
Sensors
Signal processing
state estimation
trajectory tracking
Transformer
Wavelet transforms
China
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23208650