This paper proposes to use a correlator-level global positioning system (GPS) line-of-sight/multipath/non-line-of-sight (LOS/MP/NLOS) signal reception classifier to improve positioning performance in an urban environment. Conventional LOS/MP/NLOS classifiers, referred to as national marine electronics association (NMEA)-level and receiver independent exchange format (RINEX)-level classifiers, are usually performed using attributes extracted from basic observables or measurements such as received signal strength, satellite elevation angle, code pseudorange, etc. The NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. In this paper, LOS/MP/NLOS features were extracted at the baseband signal processing stage. Multicorrelator is implemented in a GPS software-defined receiver (SDR) and exploited to generate features from the autocorrelation function (ACF). A robust LOS/MP/NLOS classifier using a supervised machine learning algorithm, support vector machine (SVM), is then trained. It is also proposed that the Skymask and code pseudorange double difference observable are used to label the real signal type. Raw GPS intermediate frequency data were collected in urban areas in Hong Kong and were postprocessed using a self-developed SDR, which can easily output correlator-level LOS/MP/NLOS features. The SDR measurements were saved in the file with the format of NMEA and RINEX. A fair comparison among NMEA-, RINEX-, and correlator-level classifiers was then carried out on a common ground. Results show that the correlator-level classifier improves the metric of F1 score by about 25% over the conventional NMEA- and RINEX-level classifiers for testing data collected at different places to that of training data. In addition to this finding, correlator-level classifier is found to be more feasible in practical applications due to its less dependency on surrounding scenarios compared with the NMEA/RINEX-level classifiers.

A smartphone with a highly sensitive antenna receiving numerous unhealthy measurements suffers from non-line-of-sight (NLOS) reception and multipath effects. 3D mapping-aided (3DMA) GNSS has been proven to be effective in urban environments. However, the multipath effect remains challenging for urban positioning. In nature, the new GNSS civilian L5-band signal with a shorter chip length shows a much better resistibility to multipath than the conventional L1-band signal. Therefore, this study integrated the multi-constellation L5-band measurements into 3DMA GNSS to improve the positioning performance in urban canyons, namely the L1-L5 3DMA GNSS. Furthermore, this study compares different approaches on the receiver clock biases estimation for 3DMA GNSS. Finally, the integration of different 3DMA GNSSs is presented. The experiments conducted using smartphone data show that the L1-L5 3DMA GNSS is available for a better position solution than the 3DMA GNSS with L1-band only, thereby achieving a positioning accuracy within 10 m on average.

Velocity updates have been proven to be important for constraining motion-sensor-based dead-reckoning (DR) solutions in indoor unmanned aerial vehicle (UAV) applications. The forward velocity from a mass flow sensor and the lateral and vertical non-holonomic constraints (NHC) can be utilized for three-dimensional (3D) velocity updates. However, it is observed that (a) the quadrotor UAV may have a vertical velocity trend when it is controlled to move horizontally; (b) the quadrotor may have a pitch angle when moving horizontally; and (c) the mass flow sensor may suffer from sensor errors, especially the scale factor error. Such phenomenons degrade the performance of velocity updates. Thus, this paper presents a multi-sensor integrated localization system that has more effective sensor interactions. Specifically, (a) the barometer data are utilized to detect height changes and thus determine the weight of vertical velocity update; (b) the pitch angle from the inertial measurement unit (IMU) and magnetometer data fusion is used to set the weight of forward velocity update; and (c) an extra mass flow sensor calibration module is introduced. Indoor flight tests have indicated the effectiveness of the proposed sensor interaction strategies in enhancing indoor quadrotor DR solutions, which can also be used for detecting outliers in external localization technologies such as ultrasonics.

In order to improve the acquisition sensitivity of the weak and dynamic GNSS signal, the Fractional Fourier transform (FRFT) is introduced to deal with the acquisition process. The acceleration can be estimated using the FRFT approach, and the gain of coherent integration as well as the detection probability could be significantly improved. Partially matched filter (PMF) technique is used to be compared with FRFT. The digital computation complexity and the mean acquisition time are provided. Moreover, theoretical analysis for the detection probability has been proposed, and simulations are also conducted to verify the high performance of the technique.

When the fractional Fourier transform (FRFT) is introduced into the weak and high-dynamic global navigation satellite system (GNSS) signal acquisition, the two-dimensional search cell will be transferred to a three-dimensional one with respect to the code chip, the Doppler shift, and the Doppler rate. The proper determinations of the code bin and Doppler shift bin in the acquisition process have already been covered in the previous researches. The aim of this work is to provide an exhaustive analysis of the approach to specify an optimal FRFT order bin, in terms of the Doppler shift rate. The lower and upper bound of FRFT order ranges are determined by the incoming signal dynamics. Then, we propose a precise model to yield an optimal FRFT order bin. Besides, a novel and fast Doppler estimator based on the non-linear least square (NLS) method is presented to improve the performance of the digital FRFT implementation. Finally, an alternate search procedure is proposed to reduce the singular estimations of the NLS method. Simulating examples demonstrate the performance of the proposed algorithms. It has been verified that the computation efficiency and the estimation accuracy have been significantly improved by proposed techniques.

The global navigation satellite system (GNSS) has been applied to many areas, e.g., the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city, and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades. Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmental interference. The vector tracking (VT) technique is promising to enhance the robustness in high dynamics as well as improve the sensitivity against the weak environment of the GNSS receiver. However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loop can decrease the accuracy of the navigation solution. There are few works present dealing with this issue. In this work, the Allan variance is accordingly exploited to specify a model which is expected to account for this type of error based on the 1st-order Gauss-Markov (GM) process. Then, it is used for proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore, the proposed system model makes use of the innovation sequence so that the process covariance matrix can be adaptively adjusted and updated. The field tests demonstrate the performance of the proposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF). When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VT loop, the real-time kinematic (RTK) positioning and code-based differential global positioning system (DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the other hand, the RTK positioning performance has been increased by maximum 21.40% when compared with the results obtained from the commercial low-cost U-Blox receiver.