Over the past years, many techniques have been developed to address this contamination problem. In the antenna-design technique, based on polarization discrimination, a polarization-sensitive antenna can reduce the magnitude of signal tracking errors caused by the multipath effect. The usage of carrier-power-to-noise-density ratio measurements based on this antenna could enhance the performance of NLOS detection, except for doubly reflected signals between the transmitter and receiver [4]. However, due to the cost and size limitations, this technique is less effective in the patch-antenna product for land-vehicle applications [4,9,10]. Other designs, such as using choke-ring antennas and multiple antennas, are expensive approaches as well [4,9]. In the post-receiver technique, consistency checking is the conventional approach based on the fact that a multipath/NLOS-contaminated measurement is inconsistent with normal measurements [4,11,12,13]. This method can improve the GNSS performance under open environments with the emergence of multi-constellation GNSS, in which the number of visible satellites can be between 15 and 30, especially in the Asia Pacific region where is easy to receive GPS, GLONASS, Beidou, Galileo and QZSS signals at the same time [13,14]. However, in the meantime, less satellites would be received due to signal blockage in urban areas, while more reflected signals can be received in such environments [15]. Similarto the concept of receiver autonomous integrity monitoring (RAIM), the consistency checking fails in GNSS-challenging environments where a large proportion of the signals are NLOS-contaminated [13]. Although, using the random sample consensus (RANSAC) method can improve its performance in case of multiple outliers [16], it is a time-intensive process as it involves an iterative selection of the observation subset. Compared with the previous fault detection and exclusion (FDE) based methods, robust regression as a quality assessment method adaptively adjusts the weightings of the measurements [17; 18]. This method is based on the minimization of cost function depending on the least-squares residuals, which represent an approximation of the measurement errors [19]. The magnitude of the residuals can be used to verify the consistency between the measurements and reweight each measurement accordingly. On the basis of this concept, robust regression has the capability of more efficient computation when multiple faulty measurements emerge, compared with RAIM using the RANSAC. Reference [19] further extends this approach and implements in extended Kalman filter (EKF) to enhance the performance when outliers exist. Nonetheless, the performance of reweighting method could be unsatisfied when abnormal measurements are in majority, because the resulting residuals are significantly influenced by contaminated signals. In the multi-sensor aiding technique, some of the methods utilize additional information from other sensors, such as LiDAR, camera, or 3D-building models to detect the NLOS signals [20; 21; 22; 23; 24; 25; 26]. However, it cannot discriminate multipath-contaminated signals from the received group, and suffers from cost and computational challenges, thereby, limiting their application in current consumer products. One of the most popular methods in this technique is integrating with aiding sensors to improve the natural GNSS limitations (i.e., satellite availability and spatial geometrical strength), thereby, enhancing the accuracy and continuity for standalone GNSS. Among these sensors, the integration of inertial navigation system (INS) and GNSS is the most common scheme applied in navigation applications due to its complementarity [27; 28; 29; 30; 31]. Nonetheless, it is well-known that the error of low-cost INS grows rapidly, due to large uncertainties in the sensor output, including having high drift rates, being vulnerable to non-linear error behavior, and sensitivity to environmental effects, such as temperature variations and shocks [31; 32; 33].