Lin Weiwei, Wang Jiajun, Wang Xiaoling, Zhang Jun, Gao Haojun
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin 300350, China.
ISA Trans. 2025 Jul 9. doi: 10.1016/j.isatra.2025.07.006.
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.
提高滚动机械的定位精度对质量和施工效率至关重要。为了减轻深窄山谷中的随机噪声干扰，提出了一种利用改进的鲁棒卡尔曼滤波器的多源定位信息融合方法。该方法从全球导航卫星系统（GNSS）、机器人全站仪（RTS）和超宽带（UWB）数据中自适应选择最优观测值，补偿噪声干扰导致的位置偏差和数据丢失，从而提高数据的鲁棒性。通过引入厚尾拉普拉斯分布来动态调整噪声协方差，对卡尔曼滤波器进行了改进，克服了数据融合中存在的大随机误差问题，提高了鲁棒性。工程测试表明，该方法能够适应深窄河谷的复杂恶劣环境，数据偏移和丢失问题的补偿率超过97.33%，与单一方法相比，定位偏移率降低了7.72%，丢失率降低了1.64%，有效提高了实时监测结果的鲁棒性、准确性和完整性。
