# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 3 Methodology
### Proposed Algorithm: Received Signal Quality Monitoring (RSQM)  
The increased availability of satellites alone cannot provide more precise positioning in highly degraded or obstructed environments due to the serious impact of MP and/or NLOS reception. The possible effects of MP on the actual GNSS signal have been demonstrated in Section 2.5. In a severe multipath environment, significant deviations in characteristics of satellite signals can occur, resulting in a positioning error of tens of meters. However, all of the visible satellites are not equally affected by MP/NLOS; it depends upon the satellite position, elevation angle, and surroundings around the antenna. With increased satellite availability, the probability of MP effects on all satellites is further reduced. The proliferation of GNSS satellites has improved the positional accuracy in various scenarios, but it still does not guarantee the accuracy and precision in severe multipath environments. Section 2.5 details the detrimental effects of MP on GNSS signal characteristics. In severe MP environments, these effects can induce positioning errors exceeding tens of meters. However, the susceptibility of individual satellites to MP/NLOS varies depending on factors like satellite position, elevation angle, and antenna surroundings. Fortunately, the increased availability of satellites through multi-constellation systems reduces the likelihood that all visible satellites will be equally affected by MP. Overall, there's no single solution to address MP, and the best approach will depend on the specific situation and application. Researchers are actively participating and investigating the wide range of techniques to reduce the negative effects of MP and ensure the accuracy and reliability of signals. Within the realm of multipath mitigation techniques, signal quality monitoring approaches at the measurement or position level hold promise due to their minimal, if any, hardware modifications. Previously discussed techniques often necessitate specialized and costly hardware, posing accessibility limitations. Through extensive positioning and accuracy analysis, coupled with field experiments, we propose an adaptive receiver signal quality monitoring (RSQM) algorithm specifically designed for autonomous navigation in urban canyons. In a clear open sky case, having minimal obstructions, there is a good possibility of having a majority of LOS components. However, in the obstructed urban environment, the signal is obscured by complex sur  
Figure 5: Live images of moving candidate sites; (**a**) Complete route of moving experiment, (**b**) clear site, (**c**) sub-urban, (**d**) highly urban environment. rounding structures, leading to limited LOS components, and there are chances for NLOS reception and multipaths. This can impact the range measurements, causing disruptions in the positioning solution. As there is no clear boundary between LOS and NLOS, it is important for the GNSS system to produce/compute the measurement of the signal quality continuously. The quality factor (QF) is introduced to continuously measure the signal quality based on input features. The qualitative relationship between input parameters and QF can be expressed by a fuzzy inference system. Most of the classification models utilized a boolean expression, which means that the fixed classification of sample data either belongs to the particular class or it does not. However, in the context of GNSS, to identify the LOS and NLOS, there is no clear boundary defined that limits the classification. For this purpose, a fuzzy inference system is used in the proposed algorithm that defines the possibility of sample data belonging to a class. The system is based on the fuzzy logic rules and membership functions. The membership functions explain the mapping of input and output variables with the degree of membership to a particular class, while fuzzy logic rules comprise a set of if-then statements that help in making a decision from the inputs in the fuzzy inference system.