面向重大公共卫生事件的位置服务技术: 以COVID-19疫情为例

基于城市三维（three-dimensional, 3D）模型的阴影匹配（shadow matching，SM）方法能有效提高城市峡谷中过街方向的卫星定位精度；但是手机接收的信噪比（signal-to-noise ratio，SNR）波动过大，而且传统方法无法区分位于平行街道的位置，容易引起较大的跨街道误差。提出了一种改进的手机SM定位方法。首先，针对手机采集卫星信号的SNR波动过大的问题，提出采用低通滤波的方法减小SNR波动，从而提高卫星实测信号可见性分类的准确性及稳定性。在此基础上，针对跨街道误差问题，提出了基于SNR滤波的聚类阴影匹配（cluster shadow matching，Cluster-SM）方法，将高分候选点按照位置分组，并根据组内有效点的个数确定点集，从而确定用户的最终位置。实验结果表明，SNR滤波方法将SNR分类的错误率由5%~30%降低至0%~20%；基于SNR滤波的Cluster-SM方法将动态实验中传统卫星定位结果的精度由19.4 m提高至2.1 m，显著地提高了跨街道的手机定位精度，为车辆及行人导航等应用提供了参考。

Privacy Protection for Users of Location-Based Services

  Objectives  Mobile phone positioning is a widely used approach for navigation, which has broad application prospects. The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban canyons because of signal reflections or blockages. Shadow matching (SM) based on the three-dimensional (3D) city model can effectively improve positioning accuracy in cross-street direction. However, variation of signal-to-noise ratio (SNR) is large using smartphone for GNSS signal reception while the conventional method fails to distinguish neighboring streets, hence, greater cross-street errors.  Methods  This paper proposes an improved SM method together with SNR smoothing implemented in smartphones to improve the positioning accuracy in urban canyons. Firstly, a SNR smoothing method based on low-pass filter is proposed to mitigate the variation, and further to improve the correctness and stability of the visibility classification based on observations. On this basis, an improved SM, namely cluster shadow matching (Cluster-SM), is proposed, in which, the effective candidate points are clustered related to their locations.  Results  Experiment results showed that SNR smoothing reduces error rate of the SNR classification from 5% -30% to 0% -20%, while the implementation of optimization Cluster-SM based on SNR filtering significantly improve the GNSS positioning accuracy from 19.4 m to 2.1 m in dynamic test, compared to conventional method.  Conclusions  This shows the effectiveness of the novel approach in increasing positioning accuracy with the ability to distinguish neighboring streets, which provides opportunities to implement the smartphones in location-based services applications, pedestrian positioning or vehicle navigation which requires a higher positioning accuracy.