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Accurate pedestrian positioning based on smartphone’s consumer-grade sensors is a research hotspot for several years. Due to the poor performance of the mass-market MEMS devices, standalone PDR inevitably generates significant accumulated errors over time. Moreover, the GNSS module in smartphone provides absolute position information and complements the relative positioning such as PDR. In this paper, we propose a PDR/GNSS fusion framework based on joint heading and stride length estimation to combine their advantages. Considering that multipath error is one of the major error sources in smartphone’s GNSS measurements, a novel pseudorange multipath error mitigation method based on CN0R spectrum decomposition is also proposed. The experimental results show that the proposed multipath mitigation approach can effectively eliminate the high-frequency multipath errors and reduce the convergence time of positioning accuracy of 2 m from 100 min to 5 min. Meanwhile, the proposed PDR/GNSS fusion algorithm has comparative advantages over the standalone methods in the aspects of heading estimation and noise performance.

Ionospheric delay is an important factor affecting the high precision satellite positioning. Under short baseline conditions, it is possible to directly eliminate most of the ionospheric delay by using double-difference (DD) observation, while under long baseline conditions, it is necessary to use the ionospheric information from the base station network to perform interpolation. A DD ionospheric interpolation method under long baseline conditions based on Gaussian process regression is proposed in this paper. In our theory, we introduced the difference between the geographic longitude of the subsolar point and the ionospheric pierce point, and use it together with geographic coordinates as the input of kernel function. Kernel of the zenith ionospheric delay at the pierce point is reasonable assumed, and kernel of the DD ionosphere delay is derived based on it. Experimental results show that using the kernel proposed in this paper to perform Gaussian process regression interpolation, its effect can be better than the traditional Kriging and linear interpolation algorithms, and the accuracy is generally within 1 cm.

The performance of Pedestrian Dead Reckoning (PDR) based on a smartphone is limited due to the low-cost MEMS IMU. Heading and stride length estimation tend to accumulate errors over time and those errors lead to the failure of PDR. Considering that Global Navigation Satellite System (GNSS) provides absolute location information in outdoor applications, and the characteristics of estimation errors of GNSS are quite different from those of PDR. In this paper, we analyse the characteristics of two different types of errors first, then we propose a fusion positioning framework that can fuse PDR and GNSS information in real time. The joint estimations of heading and stride length are also given. The experimental results show that the fusion algorithm is superior to PDR or GNSS in heading estimation, anti-multipath and noise performance.

Since the signal quality of global navigation satellite system (GNSS) is extremely vulnerable to the surrounding environment, the environment-aware adaptive positioning algorithm has drawn wide attention. In order to select the suitable positioning method in different types of environment, the receiver need to recognize the type of surrounding environment in real-time. Targeting on the vehicle positioning applications in the city, this paper divides the urban environment into six categories: canyon, downtown, suburb, viaduct-up, viaduct-down and boulevard, and proposes a novel environment recognition algorithm based on the navigation signal characteristics. Firstly, a five dimension signal feature vector is proposed to describe the quality of navigation signal. The vector elements are signal power attenuation mean, power attenuation standard deviation, signal blocking coefficient, DOP value expansion ratio and power fluctuation coefficient. Then, taking this vector as environmental attribute, this paper proposes an environment recognition algorithm based on the temporal filtering support vector machine (SVM). In the experiment, the raw navigation signal data are collected for more than 100 thousand epochs in six types of urban environment, with no less than 10 thousand epochs for each type. In order to verify the validity of the proposed recognition algorithm, the five cross validation method is used to train and test all the collecting data. The testing results show that the recognition accuracy of the algorithm are higher than 90% for all types of environment.

In recent years, due to the open access to raw GNSS measurements, it is possible to study other PVT algorithms to improve the positioning accuracy on smartphones. In this paper, we first give the analysis of the characteristics of the smartphone observations and measurement error sources, then focus on the continuous cycle slip of carrier phase with duty cycle mode. Then, we propose an improved Hatch filtering algorithm in order to use the carrier phase observations which have continuous loss of cycles to reduce the measurement noise of pseudoranges. Finally, we give a comparison of several pseudorange positioning algorithms, the model of pseudorange double difference algorithm and the parameter selection principle of Kalman filter. Experiments based on a real smartphone in the open sky environment and the post-processing of the collected data is discussed in detail later. The results show that the proposed carrier smoothing algorithm can effectively reduce the noise of pseudorange observations and the positioning accuracy of the pseudorange double difference algorithm with carrier smoothing technique is less than 5 m, which shows obvious improvement compared with the positioning accuracy of the traditional positioning method on smartphones.

With the rapid development and application of GNSS (Global Navigation Satellite System) high precision positioning technology, multipath signal model, especially in urban canyon environment, has received much attention in academic and industrial circles. In order to get multipath model in the real urban canyon environment, the real GNSS IF signal data is collected in Shanghai typical urban canyon area. Code phase delay, carrier phase changing rate and power attenuation are selected as the multipath characteristic parameters. Then, multipath delay model, multipath power attenuation model, multipath life time model, multipath fading frequency model and multipath occurrence probability model are established by using these parameters. The independent statistic models of GEO and NGEO satellites are analyzed and compared respectively, because GEO satellites which motion is very slow is used in Beidou constellation. In addition, the difference of the statistical characteristics with different satellite elevation angle is analyzed, because multipath characteristics are sensitive to the satellite position. In this paper, statistical models of multipath characteristic parameters in urban canyon environment is studied by analyzing real signal data, which provides important reference for multipath mitigation algorithm and navigation system design.

The geomagnetic field exists everywhere and has been demonstrated to be local disturbed and stable over time. In this paper, we present an approach for indoor positioning which does not rely upon any additional support equipment, using magnetic fingerprint collected by magnetic field sensors in smartphone and walking information of user measured by self-contained inertial sensors. In order to improve the practicability of our positioning method, we choose the Z-axis magnetic of world coordinate system as feature information, which can reach no restrain of phone’s gesture and orientation when pedestrians walk. Moreover, to improve the discernibility of geomagnetic information, we measure the geomagnetic signals as a sequence, and we match the geomagnetic signals with the established fingerprint using dynamic time warping (DTW) algorithm. Meanwhile, a hidden Markov model (HMM) based positioning process is adopted to strengthen the robustness of the algorithm. Extensive field tests have been conducted in an office building to verify the performance of proposed algorithm. Test results show that the proposed algorithm can achieve less than 1.11 ms of mean error and 2.13 ms of 95th percentile error.

Pedestrian dead reckoning (PDR) is a relative positioning technique, which determines the relative location of a pedestrian by using step detection, step length estimation, and heading estimation. Since step can be accurately detected and step length can be well modelled, the heading estimation is the dominant element affecting the positioning accuracy. However, heading estimation using magnetometer is seriously influenced by diversity of indoor electromagnetic environment. In order to calibrate the magnetometer measurement, we introduce an ellipsoid equation to represent the error model of magnetometer measurement, and utilize the Singular Value Decomposition and the Cholesky Decomposition for solving the equation in this paper. To achieve an accurate error model, the state-of-the-art magnetometer calibration methods need to obtain massive samples of magnetic field. To collect the data from magnetometer more efficiently, we propose a simplified method to rotate smartphone two circles around arbitrary two of three axes in a 3D space. This data collection method can provide adequate samples to determine the ellipsoid equation within a short period. The experimental results shows that the variance of data applied with proposed method is reduced by 94.08 % on an average, while data collected by six-direction method is reduced by 91.73 % and ∞ shape method by 49.83 % averagely. The calibration process takes just 2–4 s, while the conventional methods need at least 10 s. Compared to 4.59 m of the 95th percentile error without calibration, the PDR field tests show a 1.62 m error with proposed method.

With the continuous promotion of Beidou satellite navigation applications, improving the navigation terminal performance is a hot issue under city environment because of its wide application and complex signal propagation. Based on received Beidou satellite signal power shadowing under different environments in city application, the paper proposed a statistical Markov model with three states: line-of-sight (LOS), shadow block, and using a combination of mixture Gaussian functions to approximate the distribution of signal variations. Channel parameters are evaluated from the experimental data in shanghai and utilized to verify the proposed simulated model. Signal waveforms and probability density functions show proposed model close agreement with measurements.

Currently, multi-rotor UAV’s navigation mainly depends on satellite navigation systems. In many environments (e.g., indoor, urban, or canyon), lack of satellite signal will lead UAV navigation to failure. In that case, one backup solution is to use the inertial navigation method. Inertial navigation method integrates measurements from gyroscope and accelerometer to obtain the orientation, speed, and position. Due to the instability of sensor bias, a slight orientation error caused by gyroscopic bias change will lead to enormous position error. Noting that there is a strong correlation between the pose and velocity for a flying multi-rotor UAV, we can search for a solution to calculate velocity directly from UAV’s pose. In this work, we propose to use support vector machine (SVM)-based machine learning technique to predict the moving speed of the aircraft. This approach builds a relationship directly between the orientation data and velocity by training. The experiment has two stages. In early stage, we have tested our method in simulation environment; then at later stage, we have tested our method in real-world cases. Experimental results our method can predict the UAV’s velocity within the error of 0.3 m/s and the squared correlation coefficient between the predicted velocity and the ground truth is about 0.8. The method can be used as a complementary navigation source to achieve higher localization accuracy and stability.