Multi-source positioning information fusion method based on improved robust Kalman filter.

Abstract

Enhancing positioning accuracy in rolling machinery is vital for quality and construction efficiency. To mitigate random noise interference in deep and narrow valleys, a multi-source positioning information fusion method utilizing an improved robust Kalman filter is proposed. This method adaptively selects optimal observations from GNSS, Robotic Total Station (RTS) and Ultra Wide Band (UWB) data, compensates for location deviation and data loss from noise interference, thus improving data robustness. The Kalman filter is improved by incorporating a thick tail Laplace distribution to dynamically adjust noise covariance, overcoming challenges with large random errors in data fusion and improving the robustness. Engineering tests show this method can adapt to complex and harsh environments in deep and narrow river valleys, with a compensation rate of over 97.33 % for data offset and loss issues, reducing localization offset rates by 7.72 % and loss rates by 1.64 % compared to single-method approaches, effectively improving the robustness, accuracy, and completeness of real-time monitoring results.