In face of the high complexity of modern systems and the increasing reliance on technology, the strategies and methods to evaluate the integrity of systems and the information they provide are of great importance. The use of sensors to provide important information in various applications has become ubiquitous, which puts more pressure on the accuracy of sensor measurements. In the navigation field, in particular, the integrity of positioning information provided by sensors can be critical, as failures can lead to fatalities and catastrophic damages. Extensive research has been done into methods to assess and improve the integrity of positioning information provided by GNSS receivers, led by the high safety requirements in the aviation field. More recently, a shift into the research of integrity solutions suitable for the challenging positioning requirements of autonomous vehicles in urban environment has been a trend. There have been extensive advancements in developing integrity monitoring algorithms for GNSS and navigation in general. However, these methods have not been sufficiently extended into other types of sensors, and the development of integrity methods outside the navigation field have been scarce and scattered. This work aims to provide an overview of recent advances in the integrity monitoring field, including research outside the navigation domain. The goal is to give an introduction to the integrity monitoring concept to a broader audience, as these techniques have been highly specialized by GNSS experts and navigation related research, fostering multi-disciplinary approaches and creative use of existing methods in different areas and applications.
To improve the resilience and ensure the dependability of a critical system, the measurements and the derived intelligence provided by the sensors monitoring the system need to be reliable. This is increasingly challenging. As the computer vision methods evolve, the usage of cameras as a part of monitoring solutions has increased, and, consequently, the need for reliable diagnosis strategies for those image-based sensors. This work investigates the suitability of various single-value image metrics, derived from first and second-order statistics, for detecting partial camera obstruction. The presented methodology includes using data augmentation techniques to expand a small dataset of labeled images, and a score-based selection of the best metrics for the target application. The results show that even simple first-order statistics, such as the image histogram skewness, can provide good detection results. The strategy presented could be extended and adapted for the detection of other types of physical anomalies, being particularly useful for integrity assessment in applications with limited computational resources.
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