GNSS signals are vulnerable to both intentional and unintentional interference due to low signal power at the receiver. Much research has been done to address this vulnerability. Controlled Reception Pattern Antenna (CRPA) allow for the alteration of received gain patterns, introducing the possibility to amplify or nullify signals from specific directions. Space-Time Adaptive Processing (STAP) manipulates the received gain pattern, directing nulls in the direction of jamming signals. Nullifying jamming signals is effective for maintaining access to GNSS signals in a contested environment, but not without cost. GNSS information arriving from an azimuth and elevation similar to the jamming signal will be lost or minimized. In this paper, Minimum Variance Distortionless Response (MVDR) Adaptive Noise Cancellation (ANC) is presented as an alternate anti-jamming (AJ) method to STAP in an effort to mitigate the loss or minimization of GNSS information. The analysis will be performed using simulated GPS L1 C/A signals, generated by the Safran Group’s Skydel software, that have been mixed with Additive White Gaussian Noise (AWGN).
The ongoing Lunar GNSS Receiver Experiment (Lu-GRE) mission is demonstrating that Global Navigation Satellite System (GNSS) can be a major enabler for radionavigation in cislunar space and on the Moon, offering a complementary solution to ground-based tracking infrastructures. However, cislunar Orbit Determination (OD) and timing with GNSS signals remains challenging due to severe pathloss effects, frequent side lobe receptions, and degraded satellite geometry. This study evaluates a single-frequency precise point positioning (SF-PPP) approach for kinematic OD, leveraging the group and phase ionospheric calibration (GRAPHIC) model to process undifferenced code and phase observations. The method incorporates Tikhonov regularization within a batch nonlinear least square (LS) estimator to tackle the ill-conditioning caused by the inherent rank deficiency of the positioning model. The algorithm is assessed through post-processing of raw GNSS observables collected during a hardware-in-the-loop (HIL) test, simulating representative LuGRE payload operations. Results show that the proposed regularized estimator ensures more than 89 % solution availability in most of the scenarios and achieves sub-kilometer positioning accuracy, even in scenarios with insufficient measurement redundancy.
For autonomous robotic systems to receive widespread adoption, their sensor, navigation, and perception systems need to be robust to various operating environments. Thermal cameras have been proposed as a potential supplemental exteroceptive sensor for navigation and perception to address optical cameras’ degradation under low-light and LiDAR’s high cost and sparse data products. However, their adoption into navigation systems, especially in a stereo configuration, has not been sufficiently studied and the amount of datasets featuring thermal sensors in the sensor fusion context for navigation systems is noticeably lacking. To the best of the author’s knowledge, there are only three publicly available datasets with a stereo thermal camera setup for autonomous robot localization context albeit with several shortcomings, e.g., sensor synchronization, operating environments, and lacking LiDAR modalities. To address these shortcomings, the proposed work seeks to acquire a high quality dataset featuring stereo optical, stereo thermal, and micro-electro mechanical system (MEMS) LiDAR alongside two inertial navigation systems (INS) with different grades to enable navigation research. The planned sequences include traversals in an urban environment as well as a wooded environment. Furthermore, each route will be recorded at three different times, i.e., mid-day, twilight, and before sunrise to provide a variety of environmental conditions with varied temperature profiles. This further diversifies the operating environments that could be considered for navigation. The dataset will be publicly available as both raw data and Robot Operating System (ROS) bags to accommodate different preferences by navigation researchers. The resulting dataset aims to provide a means for investigation of the efficacy of thermal cameras in the context of navigation, especially in unstructured environments. The dataset is publicly available at https://sites.google.com/view/lager-nigel
GNSS-denied and long-range unmanned ground vehicle (UGV) operations often occur in environments such as roads, grasslands, and deserts where a lack of visual features in the environment is an issue for visual localization. Common SLAM-based UGV navigation methods in GNSS-denied environments employ LiDAR, radar, or camera odometry, which drift significantly in visually uninformative environments. We present a path-planning approach utilizing a priori satellite imagery to maximize navigation in areas with high visual uniqueness. Offline, iterative template matching is performed on the satellite image to generate a map of visual uniqueness in the environment, utilized as a costmap to the global planner. The uniqueness map was tested on an autonomous UGV in a visually informative environment and a visually uninformative environment based on visual odometry dead-reckoning. Neutral performance was observed in the visually informative environment, while a 55% reduction in visual odometry drift from the ground truth was observed in the visually uninformative environment utilizing this approach.
Global Navigation Satellite System (GNSS) localization in urban environments has long been challenged by multipath effects and interference from non-line-of-sight (NLOS) signals. Many studies introducing additional information (e.g., inertial navigators or light detection and ranging LiDAR) typically rely on a stable and reliable GNSS solution for state initialization or relative positioning. However, these methods require costly and complex system integration. In contrast, integrating map information for matching is a reliable and one of the cheapest solutions to achieve positioning errors of less than ten meters in deep urban environments. However, the accuracy of the initial solution significantly impacts the accuracy of the map-matching algorithm. In addition, vehicles may experience multiple states (e.g., turning or crossing lanes) while driving, leading to map-matching errors. Therefore, fully utilizing the potential of GNSS in urban environments is an urgent problem. This study proposes a method that integrates real-time kinematic (RTK) positioning algorithms and reliable meter-level positioning map-matching results into an advanced factor graph optimization (FGO) framework. Experimental results show that our method improves the average accuracy by 20% compared with the traditional method, demonstrating the significant role of integrated RTK and high-quality a priori solutions for nonlinear system optimization.
