The shearer is one of the core equipment of the fully mechanized coal face. The fast and accurate positioning of the shearer is the prerequisite for its memory cutting, intelligent height adjustment, and intelligent speed adjustment. Inertial navigation technology has many advantages such as strong autonomy, good concealment, and high reliability. The accurate positioning of the shearer based on inertial navigation can not only determine its operating position but also measure the direction of movement. However, when inertial navigation is used to locate the shearer in motion, the cumulative errors will occur, resulting in inaccurate positioning of the shearer. The accuracy of the initial alignment is directly related to the working precision of the inertial navigation system. In order to improve the efficiency and accuracy of initial alignment, an improved initial alignment method is proposed in this paper, which uses a fruit fly-optimized Kalman filter algorithm for initial alignment. In order to improve the filtering performance, the fruit fly-optimized Kalman filter algorithm uses an improved fruit fly algorithm to realize the adaptive optimization of system noise variance. Finally, simulation and experiments verify the effectiveness of the fruit fly-optimized Kalman filter algorithm.

中文翻译：

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基于果蝇优化卡尔曼滤波算法的采煤机惯性导航定位初始对准技术

采煤机是综采工作面的核心设备之一。快速，准确地定位采煤机是其内存切割，智能高度调节和智能速度调节的先决条件。惯性导航技术具有很多优点，例如自治性强，隐蔽性好和可靠性高。基于惯性导航的采煤机的精确定位不仅可以确定其工作位置，还可以测量其运动方向。但是，当使用惯性导航定位运动中的采煤机时，会发生累积误差，从而导致采煤机的定位不准确。初始对准的精度与惯性导航系统的工作精度直接相关。为了提高初始对齐的效率和准确性，提出了一种改进的初始对准方法，该方法采用果蝇优化的卡尔曼滤波算法进行初始对准。为了提高滤波性能，果蝇优化的卡尔曼滤波算法采用改进的果蝇算法来实现系统噪声方差的自适应优化。最后，仿真和实验验证了果蝇优化卡尔曼滤波算法的有效性。