基于卡尔曼滤波的WiFi-PDR融合室内定位

为降低室内环境复杂性对WiFi指纹定位的影响,提出将支持向量机(SVM)分类与回归分析相结合的WiFi指纹定位算法,以提高定位精度。在基于智能手持设备惯性传感器的行走航位推算(PDR)中,为降低惯性传感器的误差及定位误差的累积,通过状态转换的方法识别行走周期并进行计步,提出对原始加速度数据进行预处理和根据实时加速度数据动态设置状态转换参数的算法。在改进的WiFi定位算法及PDR算法基础上,提出使用联邦卡尔曼滤波融合两种方法,并根据人体运动学确定各级滤波器的状态方程和量测方程。实验证明了该算法的有效性。

WiFi-PDR Fused Indoor Positioning Based on Kalman Filtering

To reduce the negative influence of the complex indoor environment on WiFi fingerprinting, the paper proposes a support vector machines (SVM)-based WiFi fingerprinting algorithm which combines SVM classification and regression for more accurate location estimation. For smartphone based pedestrian dead reckoning (PDR), the paper detects the steps by recognizing the state transitions during human walking using real-time acceleration data. To reduce the measurement noise and the accumulation of positioning errors, the paper proposes a pre-processing algorithm on the original acceleration data and determines the state transition parameters dynamically according to the real time acceleration data. Based on the SVM-based WiFi fingerprinting and the enhanced PDR, the paper uses Kalman filtering to fuse them for more accurate and more stable positioning results. Experiments show that the proposed algorithms are quite effective.