Low-cost sensor navigation has been on the rise in the past decade with the onset of many modern applications that demand decimetre-level accuracy using mass market sensors. The key advantage of Precise Pointing Positioning (PPP) technique over Real-Time Kinematic (RTK) is the non-requirement of local infrastructure and still being able to attain decimetre to sub-metre level accuracy while using mass market low-cost sensors. Achieving dm to submetre-level accuracy is a challenge in urban environments. Therefore, adaptive filtering needs to be implemented along with low-cost sensors motion based constraining and atmosphere constraints. The traditional robust adaptive Kalman filter (RAKF) uses empirical limits that are derived by analyzing the GNSS receiver data beforehand to determine when the adaptive factor needs to be applied. In this research, a novel technique is proposed to determine the adaptive factor computation based on the detection of increase in the number of satellite signals after a partial outage, independent of using the traditional empirical values. The proposed method provides 38-55% better accuracy than the traditional RAKF and proves to be a significant improvement for the next generation applications, such as low-autonomous, virtual reality and others.