智能化开采是煤矿工业发展的必然趋势。对采煤机的精确定位是实现智能化开采的重要环节，基于惯导的采煤机定位技术以定位信息的自主性使其被广泛应用于煤炭领域。然而惯导存在定位误差累积的固有缺陷，同时采煤机截割时易受振动干扰，加大了惯导对位姿检测准确性的影响，导致纯惯性导航的位置漂移较大。本文从降低惯性传感器噪声、初始对准惯导系统、提高采煤机组合导航算法精度等方面开展研究，具体研究内容如下：

（1）针对采煤机工作环境下存在振动和电磁等噪声干扰，导致采煤机惯导定位精度下降的问题，本文采用了一种改进经验模态分解的惯导信号降噪方法。实验结果表明，相较于经验模态分解降噪和小波降噪，改进经验模态分解降噪方法对矿井环境下的振动与电磁噪声的去噪效果更佳，提高了惯导信号的质量，为惯导定位提供良好的数据基础。

（2）针对初始对准误差易导致采煤机惯导在长时间运行中定位结果发散的问题，本文引入了一种基于优化无迹卡尔曼滤波的初始对准方法。实验结果表明，优化无迹卡尔曼滤波算法对初始对准过程中的姿态误差角估计性能更高，将得到的惯导初始对准姿态角误差补偿至惯导数据解算中，降低了初始对准偏差对采煤机惯导定位精度的影响。

（3）针对纯惯导的测量误差随运行时间的增加而累积的问题，本文采用双惯导与里程计融合的定位方式，构建了采煤机的组合定位模型，考虑到模型状态突变对采煤机定位精度的影响，提出了一种基于自适应扩展卡尔曼滤波的双惯导定位方法，对自适应双惯导定位算法的影响参量分别进行了仿真和地面实验。实验结果表明，自适应双惯导定位算法能够有效解决模型状态突变的问题，且在不同工况下自适应双惯导定位算法处理后的定位误差趋于收敛，误差累积现象得到改善。最后针对采煤机运动场景下进行模拟综合定位实验，模拟小车在运行距离约300m时，经综合定位算法处理后的定位相对误差低于1%，具有较高的定位精度。

Intelligent mining is an inevitable trend in the development of the coal mining industry. The precise positioning of the coal shearer is an important link in realizing intelligent mining, and the inertial navigation system based on the positioning technology of the coal shearer has been widely used in the field of coal because of the autonomy of the positioning information. However, the inertial navigation system has the inherent defect of accumulating positioning errors, and at the same time, the shearer is susceptible to vibration interference when cutting, which increases the influence of inertial navigation on the accuracy of position detection, resulting in a larger position drift of pure inertial navigation. In this paper, research is carried out from the aspects of reducing inertial sensor noise, initially aligning the inertial navigation system, and improving the accuracy of the combined navigation algorithm of shearer, and the specific research contents are as follows:

（1）Aiming at the problem that vibration and electromagnetic noise interference exist in the working environment of shearer, which leads to the degradation of inertial navigation positioning accuracy of shearer, this paper adopts an improved empirical modal decomposition noise reduction method for inertial navigation signals. The experimental results show that compared with the empirical modal decomposition noise reduction and wavelet noise reduction, the improved empirical modal decomposition noise reduction method has a better denoising effect on the vibration and electromagnetic interference noise in the mining environment, improves the quality of the inertial signals, and provides a good data basis for inertial navigation system positioning.

（2）Aiming at the problem that the initial alignment error is easy to lead to the dispersion of the positioning results of the inertial navigation system of the shearer in a long time operation, this paper introduces an initial alignment method based on the optimized unscented Kalman filter. The experimental results show that the optimized unscented Kalman filter algorithm has higher performance in estimating the attitude error angle during the initial alignment process, and compensates the obtained attitude angle error of the initial alignment of shearer to the inertial navigation system, which reduces the impact of the initial alignment deviation on the positioning accuracy of shearer inertial navigation system.

（3）Aiming at the problem that the measurement error of pure inertial navigation accumulates with the increase of operation time, this paper adopts the positioning method of fusion of dual inertial navigation and odometer, constructs a combined positioning model of shearer, and proposes a dual inertial navigation localization method based on adaptive extended Kalman filtering considering the influence of the sudden change of the model state on the positioning accuracy of coal mining machine, and carries out the influence parameter of the adaptive dual inertial navigation algorithm respectively. Simulation and ground experiment are carried out respectively on the influential parameters of the adaptive dual inertial guidance localization algorithm. The experimental results show that the adaptive dual inertial navigation system can effectively solve the problem of sudden change of the model state, and the positioning error of the adaptive dual inertial navigation system tends to converge and the error accumulation phenomenon is improved under different working conditions. Finally, the simulated integrated positioning experiment is carried out for the movement scenario of shearer. When the simulated trolley is running at a distance of about 300m, the relative error of positioning processed by the integrated positioning algorithm is less than 1%, which has a high positioning accuracy.

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