# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 1 Introduction
The constellation landscape has evolved from a single system (US GPS) to encompass quad constellations (Russian GLONASS, European Galileo, Chinese BeiDou), along with regional systems (Japanese QZSS, Indian IRNSS) and Satellite-Based Augmentation Systems (SBAS). This diversification has significantly improved satellite availability. Notably, the transition from a mid-2000s GPS-only system with roughly 24 satellites to a multi-constellation approach \((GPS+GLONASS+Galileo+BeiDou)\) has increased the number of usable satellites to over 100. Furthermore, GNSS modernization has facilitated multi-frequency signal transmission, enhancing capabilities in signal acquisition, atmospheric corrections, multipath mitigation, interference resistance, and ultimately, precise positioning. Despite significant advancements and improvements in satellite availability, the GNSS often fails to maintain consistency in achieving the required performance level in urban environments. In such operating environments, the quality of the GNSS signals deteriorates due to multipath (MP) effects and NLOS reception, resulting in inaccurate positioning and navigation parameter estimation [28]. The standard characteristics of the received signal quality can consequently deviate in such changing environments, and all of these instabilities can be indicated in the signal strength, pseudo-range measurements, and lock-time, which disturb the received signal and increase the uncertainties in range errors. Position error is quantified by satellite availability, geometric distribution of satellites, and quality of the received signal. Signal quality is considered as one of the major constraints in multi-GNSS [29; 30]. There are several methods proposed in the literature for assessing the GNSS signal quality and mitigating the inaccuracies in the position measurements [21; 23; 24; 31; 32; 33; 34]. Those methods and their limitations are summarized in the Table 1.  
This paper proposes a Received Signal Quality Monitoring (RSQM) algorithm. RSQM works in two ways. Initially, it performs a signal quality test based on fuzzy inference system. The input parameters are CNR, NRR and CCD and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements, while considering the satellite geometry constraints (i.e., \(DOP\leq 2\)). The main contributions of the paper are as follows:  
1. To quantify and analyze the possible vulnerabilities caused by MP/NLOS. 2. Based on detailed analysis, a signal quality-based detection RSQM method is proposed to detect the Line-of-sight (LOS) and Non-line-of-sight (NLOS) signals. 3. Once quality is identified, an adaptive mitigation strategy is integrated in RSQM to overcome the MP/NLOS effects that can enhance the navigation performance. The whole paper is organized as follows and as given in Figure 1. It starts with the abstract, and a brief background of the study is given in Section 1. Section 2 discusses the signal model and potential error sources. Section 3 details the methodology and field experimentation. Results are explained in Section 3 and the proposed algorithm RSQM and its implementation in real time are given in Section 4. At the end, the paper is concluded in Section 5. with future recommendations.