随着导航定位技术的快速发展及应用,复杂室内环境下的定位与应用已成为当今热点.以普通智能手机内三轴加速度信号为研究对象,将加速度数据利用低通滤波、卡尔曼滤波去除噪声,再利用巴特沃斯滤波方法进行细化去噪;分析处理后的数据,利用峰值检测法得到行人行进时每一步的开始和结束点,通过准确的步态点计算行进的实时步频,并依据实时步频建立步长估计模型,将实时步频利用阈值分割法处理,将行人静止、慢走、正常走、快走这四个基本状态进行区分.由实验数据得出,步数检测以及实时步长的估算精度都可以满足普通室内定位的需要,并且可以有效区分行人的基本运动状态.%With the rapid development and application of navigation and positioning technology, the positioning and application under complex indoor environment become a hot spot. Taking three-axis acceleration signals of an ordinary smartphone as the research object, this paper aims to remove the noise of acceleration data by using low-pass filter and the Kalman filter, and then refines denoising by using the Butterworth filter. After that, the processed data are analyzed, gaining the start and end points of each step during pedestrian walking by using peak detection method, and then the real-time step frequency when walking according to these accurate points is calculated. The step-length estimation model is established according to the real-time step frequency, and the real-time step frequency is processed by using threshold segmentation method. The pedestrians' four basic states (static, slow walking, normal walking and brisk walking) are distinguished. The experiment results show that the step number detection and the estimated accuracy of the real-time step length can meet the needs of ordinary indoor positioning, and can effectively distinguish the pedestrians' basic motion states.
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