Innovation-Based Fault Detection and Exclusion Applied to Ultra-WideBand Augmented Urban GNSS Navigation

Abstract

Due to their ability to provide a worldwide absolute outdoor positioning, Global Navigation Satellite Systems (GNSS) have become a reference technology in terms of navigation technologies. Transportation-related sectors make use of this technology in order to obtain a position, velocity, and time solution for different outdoor tasks and applications. However, the performance of GNSS-based navigation is degraded when employed in urban areas in which satellite visibility is not good enough or nonexistent, as the ranging signals become obstructed or reflected by any of the numerous surrounding objects. For these situations, Ultra-Wideband (UWB) technology is a perfect candidate to complement GNSS as a navigation solution, as its anchor trilateration-based radiofrequency positioning resembles GNSS’s principle. Nevertheless, this fusion is vulnerable to interferences affecting both systems, since multiple signal-degrading error sources can be found in urban environments. Moreover, an inadequate location of the augmenting UWB transmitters can introduce additional errors to the system due to its vulnerability to the multipath effect. Therefore, the misbehavior of an augmentation system could lead to unexpected and critical faults instead of improving the performance of the standalone GNSS. Accordingly, this research work presents the performance improvement caused by the application of Fault Detection and Exclusion methods when applied to a UWB-augmented low-cost GNSS system in urban environments.

Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning

“…A false alarm poses a problem for the continuity risk, which is the probability that the system will be interrupted despite there being no issues present [36,37]. Particularly, for typical safety-of-life applications, an allowable maximum false alarm rate is allocated, as unscheduled system interruptions can lead to fatal accidents [38,39]. Therefore, it is critical to determine the proper detection threshold so that the probability of a false alarm does not exceed the desired requirements.…”

The Development of a Set of Novel Low Cost and Data Processing-Free Measuring Instruments for Tree Diameter at Breast Height and Tree Position

“…The ultra-wideband (UWB) technology is based on sending and receiving carrier-less radio impulses using extremely accurate timing, and it is used in indoor localization of moving objects in complicated, light to medium sheltered, and space-constrained environments [48][49][50]. Compared with other wireless technologies such as Wi-Fi, Bluetooth, and ZigBee, it has higher precision, better antiinterference ability, stronger penetration ability, and wider communication range [51][52][53]. Therefore, this technology is particularly suitable for measuring distance and positions in forests.…”

“…Although the penetration ability of UWB wireless signals is very strong, in an actual forest survey, the random ranging error may be caused by a thick tree stem, dense bushes, or a human body [7,[51][52][53][54], resulting in Dis A , Dis B , and Dis C ( dis A , dis B , and dis C ) that may be slightly longer than the actual distances, and consequently, the three circles for positioning the tag may not intersect at one common point (Figure 8). The received signal strength indication (RSSI) theory [7,[52][53][54] suggests ways to mitigate the effects of signal attenuation on ranging, to improve accuracy and to increase positioning precision [7,[53][54][55]. According to the trilateration localization algorithm [53][54][55][56][57][58], the following equations can be developed:…”

“…The received signal strength indication (RSSI) theory [7,[52][53][54] suggests ways to mitigate the effects of signal attenuation on ranging, to improve accuracy and to increase positioning precision [7,[53][54][55]. According to the trilateration localization algorithm [53][54][55][56][57][58], the following equations can be developed:…”

“…The tree DBH reference value was obtained by measuring the tree DBH using a DBH tape in the 6 plots, and the average was used as the reference tree DBH value for comparison. The accuracy of using the handheld device to measure tree DBH was evaluated by comparing with the reference tree DBH value and by calculating the bias, relative bias, root mean square error (RMSE), relative RMSE, and mean absolute percent error (MAPE), as defined in the following equations: According to the trilateration localization algorithm [53][54][55][56][57][58], the following equations can be developed:…”