融合改进小波去噪与T-Taylor的井下定位算法

A downhole localization algorithms incorporating improved wavelet denoising and T-Taylor

针对超宽带井下定位方法中,信号易受到非视距(NLOS)误差的影响,导致定位算法的精确度与环境适应性较差的问题,本文提出了一种融合改进小波去噪和T-Taylor算法的井下定位算法。对原始测距值进行降噪处理,抑制NLOS误差对定位的影响;同时引入三球交会算法,将其作为Taylor算法的初始算法,确保Taylor算法收敛的同时,增强定位算法在井下复杂环境的定位精度与环境适应性;通过蒙特卡罗仿真试验进行验证。结果表明,融合算法能够在一定程度上降低测距误差,增强算法对于复杂环境的适应性,提高定位精度,在NLOS环境下较C-Taylor算法有更高的定位精度与抗噪声性能。经实地定位试验验证,融合算法平均定位精度相比于C-Taylor算法有一定程度的提升,提高了37.3%。

A downhole localisation algorithm that incorporates improved wavelet denoising and the T-Taylor algorithm is proposed to address the problem that the signal is susceptible to non line of sight(NLOS) errors in the ultra wide band downhole localisation method, resulting in poor accuracy and environmental adaptability of the localisation algorithm. Noise reduction is applied to the original range values to suppress the influence of NLOS errors on localisation, while the three-ball rendezvous algorithm is introduced as the initial algorithm of the Taylor algorithm to ensure the convergence of the Taylor algorithm while enhancing the localisation accuracy and environmental adaptability of the localisation algorithm in complex downhole environments. The results show that the fusion algorithm can reduce the ranging error to a certain extent, enhance the adaptability of the algorithm to the complex environment, improve the positioning accuracy, and have higher positioning accuracy and noise immunity than the C-Taylor algorithm in the NLOS environment. The average positioning accuracy of the fusion algorithm is improved by 37.3% compared to the C-Taylor algorithm, as verified by field positioning experiments.