Deng Zhi-An, Wang Guofeng, Qin Danyang, Na Zhenyu, Cui Yang, Chen Juan
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
School of Electronics Engineering, Heilongjiang University, Harbin 150080, China.
Sensors (Basel). 2016 Sep 5;16(9):1427. doi: 10.3390/s16091427.
To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning (PDR), and landmarks, we propose a novel fusion approach based on an extended Kalman filter (EKF). For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning.
为了利用WiFi定位、行人航位推算（PDR）和地标各自的优势，我们提出了一种基于扩展卡尔曼滤波器（EKF）的新型融合方法。对于WiFi定位，与以往凭经验设置测量噪声参数的融合方法不同，我们采用基于核密度估计的模型来自适应地测量相关测量噪声统计量。此外，利用上一步融合结果和WiFi信号异常检测定义的WiFi定位可信区域，以降低计算成本并提高WiFi定位精度。对于PDR，我们集成了陀螺仪、加速度计和磁力计，基于另一个EKF模型来确定用户的行进方向。为了减少PDR的累积误差并实现连续的室内定位，不仅定位结果，而且行进方向估计都通过室内地标进行重新校准。在实际室内环境中的实验结果表明，所提出的融合方法比包括PDR和WiFi定位在内的单独定位方法在定位精度上有显著提高。
