This chapter discusses the most serious sources of error affecting global navigation satellite systems (GNSS) signals, classifying these in a new way, according to their nature and/or effects. For instance, errors due to clock bias or drift are grouped together. Errors related to the signal propagation medium, too, are treated in the same way. GNSS errors need to be corrected to achieve accepted positioning and navigational accuracy. We provide a theoretical description for each source, supporting these with diagrams and analytical figures where possible. Some common metrics to measure the magnitude of GNSS errors, including the user equivalent range error (UERE) and the dilution of precision (DOP), are also presented. The chapter concludes with remarks on the significance of the sources of error.

The received global navigation satellite system (GNSS) signal has a very low power due to traveling a very long distance and to the nature of the signal’s propagation medium. Thus, GNSS signals are easily susceptible to signal interference. Signal interference can cause severe degradation or interruption in GNSS position, navigation, and timing (PNT) services which could be very critical, especially in safety-critical applications. The objective of this paper is to evaluate the impact of the presence of jamming signals on a high-end GNSS receiver and investigate the benefits of using a multi-constellation system under such circumstances. Several jamming signals are considered in this research, including narrowband and wideband signals that are located on GPS L1 or GLONASS L1 frequency bands. Quasi-real dynamic trajectories are generated using the Spirent™ GSS6700 GNSS signal simulator combined with an interference signal generator through a Spirent™ GSS8366 unit. The performance evaluation was carried out using several evaluation metrics, including signal power degradation, navigation solution availability, dilution of precision (DOP), and positioning accuracy. The multi-constellation system presented better performance over the global positioning system (GPS)-only constellation in most cases. Moreover, jamming the GPS band caused more critical effects than jamming the GLONASS band.

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.

MEMS sensors have been used in many applications including navigation systems. However, these sensors suffer from highly noisy measurements. If left untreated, these errors will significantly degrade the ultimate navigational solution. Hence, applying a pre-filtering technique becomes a necessity to de-noise these sensor signals to improve the overall system performance. While wavelet denoising is the most common technique for sensor data pre-filtering, it may not be suitable for real-time implementations. This paper explores another method; namely, Savitzky-Golay filters, which can provide competitive denoising performance with a less computationally demanding algorithm. The purpose of the paper is to examine the performance of the new method against wavelet de-noising with respect to both positioning and attitude accuracy and computations time. We applied the filter to denoise MEMS-based inertial sensors data in a tightly coupled integrated INS/GPS system. Our results showed that the new method outperformed the wavelet denoising approach. Moreover, the new method demands much less computations time, which makes it more suitable for embedded systems and real-time applications.

Future autonomous vehicles will rely mainly on global navigation satellite system (GNSS) receivers for positioning services. However, GNSS cannot maintain an accurate, continuous and reliable navigation solution in the presence of jamming. The widespread availability of in-car jammers or personal privacy devices (PPDs) made GNSS receivers an attractive target for signal jamming. Jammers not only jeopardize current positioning and timing services; they can indeed endanger the safety of the evolving intelligent transportation, autonomous road vehicles and critical infrastructure. The disruption of GNSS signal in some applications, especially safety-critical ones can lead to crucial consequences and risks such as loss of time synchronization and potential loss of life, to name a few. Thus, it is essential for future autonomous vehicles and transport service providers to deploy reliable GNSS anti-jamming techniques. This paper introduces a novel anti-jamming technique based on a high-resolution spectral estimation that utilizes fast orthogonal search (FOS) algorithm in which the jamming signal is modeled using a set of candidate functions and then eliminated from the received signal. The performance of the proposed method is assessed using experiments obtained from Spirent _TM GSS6700 simulation system. The jamming signal is either obtained from a real jammer or simulated using interference signal generator that is connected to Spirent _TM system. The results showed that the developed anti-jamming algorithm was able to successfully suppress the continuous wave (CW) interference signal, thus, the performance of the acquisition, tracking and navigation modules within a GPS software receiver were all enhanced.

This chapter describes Global Navigation Satellite Systems (GNSSs) and their signal characteristics, beginning with an overview of Global Positioning System (GPS) architecture and describing its three primary segments: control, space, and user segments. After that, it addresses the GPS modernization program including the new civilian and military signals and their significance. It continues by outlining the GPS signal characteristics and the sources of GPS measurement error. GPS receivers as well are briefly described. Then, it gives an overview of the GLONASS and describes its modernization program. Additionally, it delves into many aspects the GLONASS, including GLONASS signal characteristics, the GLONASS radio frequency (RF) plan, pseudorandom (PR) ranging codes, and the intra-system interference navigation message. Finally, GPS and GLONASS are compared to highlight the advantages of combined GPS and GLONASS measurements over the GPS-only measurements.