Smartphone-based pedestrian localization is still a challenge in deep urban canyons, where GNSS signals suffer from the degrading of signal transmission, multipath effects, and NLOS reception. This paper presents a comprehensive pedestrian localization scheme based on PDR and GNSS observations at different times, using the internal sensors equipped in the smartphone, including GNSS raw measurements (pseudo-range, carrier phase), internal MEMS sensor (including the gyroscope, accelerometer, magnetometer, and barometer). The core algorithm utilizes historical effective satellite observation and PDR to solve pedestrian position, exploiting both PDR and GNSS’s complementary properties. The proposed approach can improve accuracy and continuity and solve the problem of missing data, such as without satellite coverage. Besides, we design a Kalman filter model to reduce systematic errors and correct PDR in real-time to decrease the cumulative error of PDR. To evaluate the proposed pedestrian localization scheme’s performance, we perform experiments in a typical urban canyon with dense foliage and tall buildings and compare it with the different state-of-the-art approaches. The comparison and analysis of the overall positioning performance show that the method proposed in this paper can provide a better localization scheme, and the RMS value of positioning error is improved from 51.7m (GNSS only) to 9.6m.

The scenario is very important to smartphone-based pedestrian positioning services. The smartphone is equipped with MEMS(Micro Electro Mechanical System) sensors, which have low accuracy. Now, the methods for scenario recognition are mainly machine-learning methods. The recognition rate of a single method is not high. Multi-model fusion can improve recognition accuracy, but it needs to collect many samples, the computational cost is high, and it is heavily dependent on feature selection. Therefore, we designed the DT-BP(decision tree-Bayesian probability) scenario recognition algorithm by introducing the Bayesian state transition model based on experience design in the decision tree. The decision-tree rules and state transition probability assignment methods were respectively designed for smartphone mode and motion mode. We carried out experiments for each scenario and compared them with the methods in the references. The results showed that the method proposed in this paper has a high recognition accuracy, which is equivalent to the accuracy of multi-model machine learning, but it is simpler, easier to implement, requires less computation, and requires fewer samples.