# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 4 Context-Aware Navigation (CAN)  
In positioning and navigation, context is the environment that a navigation system is operating in, and this can result in significant effects on the satellite availability and quality of the received signals. Recently, context-aware navigation has received significant attention because it does not require any major hardware modifications and can be easily incorporated into a receiver. As demonstrated in the performance evaluation results, the fundamental quality indicators show a great deal of variations when moving across different environments in urban and suburban areas. Based on these results, a set of temporal features can be extracted from the GNSS measurements and can be used to distinguish low-to-highly degraded multipath environments. Previously, several algorithms have been proposed for environment detection and characterization using the satellite availability, DOP, residuals and signal strength or its variants [36; 37; 38; 39; 40; 41; 42]. In [36], [PERSON] laid the foundation of environment context detection and characterization based on GNSS signal characteristics and measurements. The preliminary context detection experiments in [36] showed that the signal strength can significantly vary in different environments and that this can be used to distinguish indoor from outdoor environments. Then, in [37], a method was proposed that initially classifies the environment into indoor and outdoor categories and then classifies the outdoor category into open sky and urban environments based on two features extracted from the GNSS measurements, i.e., the satellite availability and signal strength. Similarly, the satellite availability, height and signal strength were also investigated as context detection features in [38; 39]. In [40], the operating environment was categorized as indoor, intermediate and outdoor using the number of detected satellites and variations in signal strength as characterization parameters. In [41], a five-dimensional signal feature vector (i.e., the mean and standard deviation of the signal strength attenuation, blockage coefficient, geometric dilution of precision (GDOP) and strength fluctuations) was proposed to characterize the operating environment into six categories, such as urban canyon, semi-urban, suburban, viaduct-up, viaduct-down and boulevard. Similarly, ref. [42] proposed a model based on behavior association for further improving the environment detection. Most of the previous work focused on the development of context-detection models using single and dual constellations with little-to-no consideration regarding how these models can be utilized at the at receiver level to improve the availability and accuracy of navigation services. Furthermore, several researchers have used signal characteristics (i.e., the number of visible satellites, DOP and signal strength/CNR) to propose algorithms for environment detection using machine-learning techniques, fuzzy-inference systems, stochastic modeling and the Hidden Markov model [36; 37; 38; 39; 40; 41; 42]. However, the completion of emerging navigation systems (i.e., BeiDou and Galileo) along with the modernization of existing constellations (i.e., GPS and GLONASS) promises significant improvements in satellite visibility, geometry, the quality of navigation signals and, thus, the overall localization accuracy. Furthermore, remarkable advancements in receiver technology are paving the way for using multi-constellation systems.