Accurate, ubiquitous and reliable navigation can make transportation systems (road, rail, air and marine) more efficient, safer and more sustainable by enabling path planning, route optimization and fuel economy optimization. However, accurate navigation in urban contexts has always been a challenging task due to significant chances of signal blockage and multipath and non-line-of-sight (NLOS) signal reception. This paper presents a detailed study on environmental context detection using GNSS signals and its utilization in mitigating multipath effects by devising a context-aware navigation (CAN) algorithm that detects and characterizes the working environment of a GNSS receiver and applies the desired mitigation strategy accordingly. The CAN algorithm utilizes GNSS measurement variables to categorize the environment into standard, degraded and highly degraded classes and then updates the receiver’s tracking-loop parameters based on the inferred environment. This allows the receiver to adaptively mitigate the effects of multipath/NLOS, which inherently depend upon the type of environment. To validate the functionality and potential of the proposed CAN algorithm, a detailed study on the performance of a multi-GNSS receiver in the quad-constellation mode, i.e., GPS, BeiDou, Galileo and GLONASS, is conducted in this research by traversing an instrumented vehicle around an urban city and acquiring respective GNSS signals in different environments. The performance of a CAN-enabled GNSS receiver is compared with a standard receiver using fundamental quality indicators of GNSS. The experimental results show that the proposed CAN algorithm is a good contributor for improving GNSS performance by anticipating the potential degradation and initiating an adaptive mitigation strategy. The CAN-enabled GNSS receiver achieved a lane-level accuracy of less than 2 m for 53% of the total experimental time-slot in a highly degraded environment, which was previously only 32% when not using the proposed CAN.

Satellite-based navigation is an essential part of all the technology-dependent applications, such as road transport, cell phones, the medical field, aviation or the shipping industry, etc. The performance of the navigation systems depends upon how quickly they can acquire and process the received signals for positioning solutions. However, in dense urban or indoor environments, signal acquisition can be a challenging task due to fading as a result of multipath and/or interference. This paper presents post-processing acquisition results on Global Positioning System (GPS) signals to study the relationship between data lengths used for signal acquisition and the achieved signal power using a Fast Fourier Transform (FFT)-based circular correlation method. Based on this study, the detection performance of the FFT-based method has also been analyzed by intentionally degrading the signal power levels. A new Adaptive Data length (ADL) method for acquisition has been proposed in this paper, which can be used for speeding up the acquisition process and uses adaptive data lengths rather than fixed data lengths. The ADL method works by estimating the threshold level based on the noise present in the signal and then comparing it with the signal power levels. Less difference between the threshold level and signal power level means less data length will be used while more difference means that more data length will be used for acquisition. The proposed algorithm can be used in commercially available receivers for adopting to an adaptive acquisition process for increased efficiency.