Accurate navigation is essential for autonomous robots and vehicles. In recent years, the integration of the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and camera has garnered considerable attention due to its robustness and high accuracy in diverse environments. However, leveraging the full capacity of GNSS is cumbersome because of the diverse choices of formulations, error models, satellite constellations, signal frequencies, and service types, which lead to different precision, robustness, and usage dependencies. To clarify the capacity of GNSS algorithms and accelerate the development efficiency of employing GNSS in multi-sensor fusion algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB), together with detailed documentation and a comprehensive land vehicle dataset. A factor graph optimization-based multi-sensor fusion framework is established, which combines almost all GNSS measurement error sources by fully considering temporal and spatial correlations between measurements. The graph structure is designed for flexibility, making it easy to form any kind of integration algorithm. For illustration, RTK, PPP, and four Real-Time Kinematic (RTK)-based algorithms from GICI-LIB are evaluated using our dataset and public datasets. Results confirm the potential of the GICI system to provide continuous precise navigation solutions in a wide spectrum of urban environments.

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

Single‐antenna global navigation satellite system (GNSS) spoofing, where all spoofing signals are broadcast by the same antenna, is a great threat to mass market location‐based services. This study proposes an anti‐spoofing technique based on Doppler frequency difference (DFD) monitoring against single‐antenna spoofing, which requires only a standalone receiver with a single antenna. When the receiver moves randomly, the DFD between two authentic satellite signals is non‐linear in time domain, whereas between two single‐antenna spoofing signals is linear. This study deduces the DFD model and converts the problem of spoofing detection into a sequence linearity detection problem. The hypothesis based on DFD is given, and the statistic is constructed by the method of sequence block processing. This technique has low complexity because it only requires the knowledge of the Doppler frequency without additional information. The detection performance is verified by simulations and real data experiments. The simulation results show that the detection probability can exceed 99.99% easily at 0.001% false alarm probability when the receiver's motion state changes sufficiently. The real data experiments show that the proposed technique can effectively detect GNSS spoofing and distinguish spoofing signals from authentic signals.

Multiple unmanned aerial vehicle (UAV) systems have attracted extensive research interest for their potential benefits of scalability and flexibility. To keep all the UAVs in the desired formation, the navigation system must continuously provide highly accurate and robust relative positioning results. In open‐sky areas, this mission can be achieved using Global Satellite Navigation System (GNSS). However, the performance will be significantly degraded in urban canyons, due to signal blockage or high multipath. In response, we propose a cooperative relative navigation method that exploits peer‐to‐peer (P2P) ranging measurements to assist GNSS, and introduce cooperative technology to relative navigation. The principle of this approach is integrating multiple observations from multiple sensors and vehicles in the formation, thereby (a) improves navigation accuracy and (b) enhances the robustness against operational scenario changes. Simulations and flight experiments are carried out to validate the proposed algorithm in urban environments, and the results reveal significant accuracy improvement comparing to GNSS only. In addition, multiple sets of sensitivity analyses are performed to address the impact of UAV numbers, formation geometry and accuracy of P2P ranging on navigation performance.

Characterization of global transcriptomes using conventional short-read sequencing is challenging because of the insensitivity of these platforms to transcripts isoforms, multigenic RNA molecules, and transcriptional overlaps, etc. Long-read sequencing (LRS) can overcome these limitations by reading full-length transcripts. Employment of these technologies has led to the redefinition of transcriptional complexities in reported organisms. In this study, we applied LRS platforms from Pacific Biosciences and Oxford Nanopore Technologies to profile the dynamic vaccinia virus (VACV) transcriptome and assess the effect of viral infection on host gene expression. We performed cDNA and direct RNA sequencing analyses and revealed an extremely complex transcriptional landscape of this virus. In particular, VACV genes produce large numbers of transcript isoforms that vary in their start and termination sites. A significant fraction of VACV transcripts start or end within coding regions of neighboring genes. We distinguished five classes of host genes according to their temporal responses to viral infection. This study provides novel insights into the transcriptomic profile of a viral pathogen and the effect of the virus on host gene expression. Author Summary Viral transcriptomes that are determined using conventional (first- and second-generation) sequencing techniques are incomplete because these platforms are inefficient or fail to distinguish between types of transcripts and transcript isoforms. In particular, conventional sequencing techniques fail to distinguish between parallel overlapping transcripts, including alternative polycistronic transcripts, transcriptional start site (TSS) and transcriptional end site (TES) isoforms, and splice variants and RNA molecules that are produced by transcriptional read-throughs. Long-read sequencing (LRS) can provide complete sets of RNA molecules, and can therefore be used to assemble complete transcriptome atlases of organisms. Although vaccinia virus (VACV) does not produce spliced RNAs, its transcriptome contains large numbers of TSSs and TESs for individual viral genes and has a high degree of polycistronism, together leading to enormous complexity. In this study, we applied single molecule real-time and nanopore-based cDNA and direct-RNA sequencing methods to investigate transcripts of VACV and the host organism.