Vehicle Localization Using Joint DOA/TOA Estimation Based on TLS-ESPRIT Algorithm

In this paper, a high-resolution vehicle positioning estimation algorithm based on existing Vehicle to Infrastructure (V2I) communications is proposed to achieve joint estimation of vehicle target’s direction of arrival (DOA) and time of arrival (TOA). We adopt the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm based on total least squares (TLS) to estimate the DOA and TOA, and the vehicle location can be obtained from the estimated parameters. The TLS-ESPRIT algorithm not only has a relatively small amount of computation to meet the real-time requirements of vehicle localization, but also has the advantage of strong anti-noise. We also introduce unscented Kalman filter (UKF) to further improve the localization accuracy of the TLS-ESPRIT algorithm and to reduce the influence of noise interference. The simulation results show that compared with the traditional 2D-ESPRIT parameter estimation methods without UKF and the Global Positioning System (GPS), this method has better performance of positioning parameter estimation.

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