# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 1 Introduction
The results of our previous work on multi-constellation GNSS suggested that the positioning performance of a GNSS receiver may vary significantly with the type of operating environment. It has been established that the use of multiple constellations can enhance the availability and reliability of GNSS in constrained environments; however, this cannot guarantee improved positioning accuracy as debated in [25; 26; 27; 28]. The positioning accuracy of satellites is usually determined by the availability, geometry and the quality of the received signals, and these factors are highly influenced by the dynamics and the structure of the urban canyon. At first, the obstructions in urban areas may result in blockages of the line-of-sight (LOS) of satellites, and this eventually distorts their geometric distribution [21]. However, this can be compensated for by using a multi-constellation GNSS configuration. Secondly, the flat surface reflectors give rise to multipath (MP) or non-line-of-sight (NLOS) phenomena, which further degrade the quality of signals by introducing biases in the range measurements. There exists substantial research for improving the GNSSperformance by minimizing the impact of MP and/or NLOS reception. This includes techniques to detect, model and mitigate the NLOS/multipath effects at various aspects, including the antenna, receiver and measurement [21, 29, 30]. Furthermore, there is another class of multipath/NLOS mitigation techniques that works at the discriminator level, such as the maximum likelihood techniques based on the multipath estimating delay locked loop (MEDLL) [31, 32, 33], the coupled amplitude DLL (CADLL) [30] and the multipath insensitive DLL (MIDLL) [34]. However, these mitigation approaches require an increased number of correlators, which results in increased system complexity and computational load. Recently, the mitigation techniques at the receiver and measurement levels (i.e., detecting, de-weighting and rejecting the affected range measurements) has received significant attention because they do not require major hardware modifications [29]. These mitigation models/techniques, however, work effectively only in some contexts and not in all operating environments. Similarly, the correlator-based techniques (i.e., MEDLL or its variants) cannot detect short-delay multipath environments and may introduce bias into the measurements due to their modifications inside the tracking channels of a correlator [30, 35]. Typically, the detection and exclusion of faulty measurements (i.e., signals affected by MP/NLOS) help to improve the positioning accuracy; however, this leads to outages in dense urban environments due to the deficiency of redundant range measurements [25]. Practically, GNSS receivers operate in the wide range of environmental contexts, which significantly influences the signal reception conditions and severity of multipath/NLOS reception. Similarly, most of the multipath mitigation methods are not capable of effectively suppressing the NLOS/multipath effects in all contexts. Hence, in order to operate accurately and effectively in a wide range of environments, a GNSS receiver is required to adopt an optimal mitigation technique/method based on the detected context. Utilizing an inappropriate method may result in less effective reduction of multipath effects while consuming more power with a greater computational load. There exist several methods for environmental context detection and characterization using the GNSS parameters, i.e., satellite availability, DOP, residuals and signal strength or its variants [36, 37, 38, 39, 40, 41, 42],to detect and identify the type of environmental context. Most of the previous work focused on the development of context-detection models using single and dual constellations with little to no consideration to how these models can be utilized at the at receiver level to improve the availability and accuracy of navigation services [36, 37, 38, 39, 40, 41, 42].