High-precision positioning from Global Navigation Satellite Systems (GNSS) has garnered increased interest due to growing demand in various applications, like autonomous car navigation and precision agriculture. Precise Point Positioning (PPP) offers a distinct advantage over differential techniques by enabling precise position determination of a GNSS rover receiver through the use of external corrections sourced from either the Internet or dedicated correction satellites. However, PPP’s implementation has been challenging due to the need to mitigate numerous GNSS error sources, many of which are eliminated in differential techniques such as Real-Time Kinematics (RTK) or overlooked in Standard Point Positioning (SPP). This paper extensively reviews PPP’s error sources, such as ionospheric delays, tropospheric delays, satellite orbit and clock errors, phase and code biases, and site displacement effects. Additionally, this article examines various PPP models and correction sources that can be employed to address these errors. A detailed discussion is provided on implementing the standard dual-frequency (DF)-PPP to achieve centimeter- or millimeter-level positioning accuracy. This paper includes experimental examples of PPP implementation results using static data from the International GNSS Service (IGS) station network and a kinematic road test based on the actual trajectory to showcase DF-PPP development for practical applications. By providing a fusion of theoretical insights with practical demonstrations, this comprehensive review offers readers a pragmatic perspective on the evolving field of Precise Point Positioning.

Global Navigation Satellite Systems (GNSS) is utilized to provide route guidance information to land and autonomous vehicles. The GNSS-based positioning and navigation (POS/NAV) usually suffer from satellite signal blockage, interference, and multipath in urban areas. Autonomous land vehicles are equipped with cameras, radars, and laser ranging devices. The availability of these systems provides an attractive opportunity to increase the POS/NAV system accuracy. This research focuses on the development of an integrated multi-sensor POS/NAV system capable of offering seamless positioning for autonomous land vehicles. A new multi-sensor POS/NAV module integrating both adaptive cruise control frequency modulated continuous wave (ACC-FMCW) radar (RAD), and magnetometer measurements with the reduced inertial sensor system (RISS) was designed to update the navigation system during GNSS outages. Augmenting RAD/RISS system with the magnetometer measurements produces a robust solution. The designed system is further improved by utilizing fast orthogonal search (FOS) to provide nonlinear error modeling of the residual errors associated with the RAD/RISS positioning solution in order to reduce the error growth overextended and frequent GNSS outages.The proposed systems were evaluated on several real road test trajectories involving different types of land vehicles experiencing different motion dynamics. GNSS outages of up to 10 minutes were intentionally introduced to examine the performance. The results show that the proposed methods have resulted in a significant performance improvement in the positioning accuracy that can reach more than 80% if compared to the present techniques that rely only on integrating the inertial sensor technology with GNSS.

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

The last decade has witnessed a growing demand for precise positioning in many applications, including autonomous car navigation. The safety features in autonomous driving and Advanced Driver Assistance Systems (ADAS) require lane-level positioning accuracy. Such accuracy can be obtained from the Global Navigation Satellite Systems (GNSS) through either differential techniques or Precise Point Positioning (PPP). PPP is currently favored over differential GNSS because it provides a global solution without the need for local reference stations. Nevertheless, employing PPP for land vehicles would be challenging due to frequent signal degradation and blockage. Integrating PPP with an Inertial Navigation System (INS) can solve the solution continuity problem; however, the INS solution drifts over time, resulting in losing the desired accuracy. Implementing a reliable PPP/INS system that can preserve the required accuracy is not trivial, especially with financial and computational cost constraints. This article proposes the integration of PPP with the Reduced Inertial Sensor System (RISS) for lane-level car navigation. The high-precision needed in lane-level positioning can be achieved by integrating PPP with high-end INS. Since high-end INS are expensive, this work proposes the use of RISS instead of the traditional INS. RISS uses only one gyroscope and two accelerometers, which can save more than half the high-end INS cost. The proposed PPP/RISS system was tested through three road tests that included highway driving under several overpasses. The system was able to maintain horizontal position errors of less than 50 cm.

This research introduces a new twodimensional direction of arrival (DOA) elevation and azimuth angles estimation technique for global positioning system (GPS) jamming signals in challenging environments based on the fast orthogonal search (FOS) method. FOS-DOA estimation is accommodated to process array structure optimized for interference rejection. Performance of FOS-DOA is compared to the predominant multiple signal classification (MUSIC) DOA estimation method. Results showed significant improvement in jamming detection of the multiple sources of interference with slight variations in their amplitude at jamming to signal ratio (JSR) 15dB and 45dB. The improvement introduced by the proposed DOA estimation technique is mainly in the accuracy of detecting the number of jammers and their direction of arrival.

The safety and reliability of various navigation applications are critically dependent on the integrity of sensor measurements. The inertial measurement unit (IMU) is a primary sensor in many navigation systems, yet is susceptible to diverse errors and faults, particularly with micro-electro-mechanical systems (MEMS). To address these challenges, we propose a Kalman filter-based framework incorporating multiple redundant IMUs offering robust fault detection and isolation (FDI) capabilities in the context of inertial navigation. The primary contributions of this work include effective multi-IMU calibration and integration, a comprehensive FDI enabled by a bank of auxiliary Kalman filters, and an optimal dual-objective function combined with the differential evolution (DE) algorithm for fault detection parameters optimization. The effectiveness of the introduced method was validated using real-world data from homogeneous MEMS IMUs during urban road tests. Through data augmentation, fault simulation, and parameter optimization experiments, an exceptional fault detection performance was demonstrated, with an F1 score of 99.9%. Furthermore, our approach significantly enhanced inertial navigation accuracy, with position improvements of up to 78.4% in fault-free conditions compared to a single IMU and 64.5% in fault conditions compared to the standalone IMU fusion. These results confirm that the system can maintain an accurate and reliable navigation solution even in the presence of IMU sensor faults.