# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 1 Introduction  
The era of the industrial revolution is transforming remarkably from traditional machine to synergy between humans and advanced technology. The advent of Industry 5.0 integrates human intervention with advanced artificial intelligence-based technology to produce more resilient and sustainable systems. The GNSS is the bedrock and the most promising technology for various modern industries [1; 2; 3; 4; 5; 6]. With the advent of Industry5.0, GNSS systems are regarded as swarm intelligence, where at least four satellites from a single constellation or seven satellites from multiple constellations collaborate with each other to deliver position and navigation services at all times, in all weather conditions, at full coverage worldwide. It is significantly used in several applications ranging from safety-critical applications and autonomous vehicle systems to portable commercial devices and energy systems [7; 8; 9; 10; 11]. A variety of scientists are focusing their efforts on discovering techniques that decrease the negative impact of multipath (MP) effects on signals. MP is the scenario in which a signal hits any object before it arrives at the destination, which can make the signal weak and could lead to problems, for instance, with the positioning system. The techniques can also be applied during the signal processing stage to improve the accuracy of the signal, especially in situations where the signal or environment is constantly changing (dynamic applications). One approach involves using weighting models, which take into account factors like the signal-to-noise ratio (C/N0) or the elevation of the satellite to improve positioning accuracy. These weighting models are relatively easy to implement. Other signal processing techniques focus on the actual signal itself [12]. Doppler and pseudorange estimators, feed-forward estimators, and multi-frequency C/N0 techniques can be particularly useful in these situations. Researchers are also exploring other approaches to address MP. These techniques can involve consistency checks, where the system looks for inconsistencies in the received signal to identify potential MP issues. Statistical approaches, like Kalman filters, maximum-likelihood estimation, and particle filtering, can also be used to analyze the signal and reduce the impact of MP [13; 14; 15; 16; 17]. Additionally, some techniques focus on mitigating MP in the carrier signal itself (carrier smoothing) or in the Doppler domain (Doppler-domain MP mitigation). Machine learning-based methods, such as neural networks (NNs), support vector machines (SVMs), and pattern recognition, have also been explored for MP mitigation. These methods can learn to identify and classify multipath scenarios, enabling more effective mitigation strategies. The effective mitigation of the MP and NLOS errors in GNSS systems requires a combination of receiver configuration, signal processing techniques, and adaptive strategies [18; 19; 20; 21; 22; 23; 24]. By carefully considering these factors and leveraging advanced techniques, it is possible to enhance the positioning and navigation performance of GNSS positioning in challenging environments. It is obvious that the GNSS provides seamless and resilient positioning information in clear, open sky regions. Navigation performance is infrequently getting worse due to the disruptions caused by the MP/NLOS in urban environments. GNSS signals are impaired by large-sized buildings, closed-loop bridges, and trees, which leads to increased positional uncertainties. In simple terminology, the more severe the urban area is, the more challenges there are on positioning. Hence, precise navigation is the most severe challenge to the GNSS in terms of autonomy and requires highly accurate and precise positioning and navigation solutions [25; 26; 27]. The GNSS has undergone rapid growth in recent years.