多场景下的行人步频自适应检测方法

针对步频检测中容易出现步数过计、错计等问题影响行人航迹推算(PDR)室内定位精度，提出一种自适应步频检测算法. 由于智能手机内置加速度传感器直接采集得到的数据存在大量干扰噪声，提出一种组合滤波去噪方法，即将加速度数据依次通过赫尔指数移动平均法、卡尔曼滤波(KF)和低通滤波的预处理滤波组合去除噪声. 然后在不同场景下，如上下楼、水平地面和不限制步速，经过峰谷值去异、自适应动态阈值、峰谷值成对的检测算法后获取峰谷值个数，实现在多场景和多步态下的准确计步. 实验结果表明:相比峰值检测和动态阈值算法，该方法能有效剔除伪真步数且适应于上下楼场景，综合场景下的实验平均精度达到99.44%. 

Adaptive detection method pedestrian step frequency in multi scenes

Aiming at the problems of over counting and wrong counting in step frequency detection, which affect the indoor positioning accuracy of pedestrian dead reckoning (PDR), an adaptive step frequency detection algorithm is proposed. Because there is a large amount of interference noise in the data directly collected by the built-in acceleration sensor of smart phone, a combined filtering denoising method is proposed.The acceleration data is denoised by preprocessing filter combination of exponential hull moving average, Kalman filter (KF) and low-pass filter. Then, in different scenes, such as upstairs and downstairs, horizontal ground and unlimited walking speed, the number of peak-valley values is obtained after the peak-valley value de differentiation, adaptive dynamic threshold and peak-valley value pairing detection algorithm, so as to achieve accurate step counting in multi scenes and multi gait. The experimental results show that, compared with the peak detection method and dynamic threshold algorithm, the proposed method can effectively eliminate the false steps and adapt to the upstairs and downstairs scenes, and the average accuracy of the experiment in the comprehensive scene reaches 99.44%.