WiFi室内定位技术在健身场所的应用研究

随着WiFi技术的发展，当前国内健身房管理方式逐渐从传统射频卡识别转化成WiFi室内定位识别，改进后有效地节约了健身成本。目前WiFi室内定位技术如三边定位、贝叶斯定位算法等已均被提出，但仍存在定位误差大、计算复杂度高等弊端。因此，提出基于滤波的三边定位及模式聚类两种定位算法。基于滤波的三边定位通过增加卡尔曼滤波，降低噪声数据的影响，从而减小定位误差。模式聚类定位转变常用的贝叶斯概率聚类方式，采用健身者运动模式进行个性化聚类，有效地提高定位识别率和准确率。实验结果表明，相比基线方法，基于滤波的三边定位和模式聚类定位算法应用于健身场所更加精确化、人性化。

A Research on the Application of WiFi Indoor Positioning Technology in Fitness Places

With the development of the WiFi technology,the current domestic gym management mode has gradually transformed from the traditional radio frequency card recognition into WiFi indoor positioning,which effectively saves the fitness cost.The WiFi indoor positioning technologies such as trilateral orientation and Bayesian algorithm have been put forward,but there are still large positioning error and high computational complexity.Therefore,the trilateral location and pattern clustering localization algorithm based on the filtering are presented.The trilateral one by increasing the Kalman filter decreased the influence of the noise data,and reduced the positioning error.Pattern clustering changed from the mode of the Bayesian probability of commonly used clustering into personalized clustering with fitness person movement patterns,which effectively improved the recognition rate and accuracy of localization.The experimental results show that,compared with the baseline method,the trilateral orientation and pattern clustering algorithm based on filtering is more accurate and user-friendly in the fitness field.