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

Area of Science:

Robotics and Control Systems
Signal Processing
Geospatial Analysis

Background:

Maneuvering target tracking using GPS is vital for autonomous systems but challenged by complex motion and unknown sensor/noise characteristics.
Classical Kalman filter methods struggle with parameter uncertainty and unknown color noise in GPS data, degrading performance.
Accurate state estimation for maneuvering targets is critical for reliable navigation and autonomous vehicle operation.

Purpose of the Study:

To develop a robust GPS-based maneuvering target localization and tracking method that overcomes limitations of classical approaches.
To introduce a novel state estimation technique utilizing attention learning and online parameter estimation.
To improve the accuracy and reliability of state estimation in the presence of complex dynamics and unknown GPS data characteristics.

Main Methods:

A Kalman filter-based state estimation method incorporating attention learning via a transformer encoder and LSTM network.
Online estimation of system model parameters using the expectation maximization (EM) algorithm, driven by attention learning outputs.
Integration of learned system, dynamics, and measurement characteristics into the Kalman filter for state estimation.

Main Results:

The proposed attention learning-based method demonstrated superior estimation accuracy compared to classical and pure model-free network approaches.
Experimental validation using GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset confirmed the method's effectiveness.
The approach successfully addressed challenges posed by complex maneuvering target motion and unknown GPS data properties.

Conclusions:

The developed attention learning-based Kalman filter offers an effective solution for practical maneuvering target tracking applications.
This method provides accurate state estimation without reliance on predefined system parameters, enhancing robustness.
The findings contribute to advancing autonomous driving and navigation systems through improved GPS tracking capabilities.