## 1 Introduction  
Location-based applications are progressively getting more traction in our daily life, including applications such as transportation, communication, precise farming, and life-rescue operations. Positioning accuracy and consistency are the key requirements for the applications such as autonomous land vehicle driving, unmanned aerial vehicle (UAVs) and in life-critical applications. Global Navigation Satellite System (GNSS) is one of the most trustworthy positioning systems. The establishment of new GNSS constellations like the BeiDou and the Galileo has greatly increased the number of visible satellites around the global as well as new frequency signals and satellites are also added in existing GNSS constellations. These developments make it possible to use signals from multiple constellations to enhance the accuracy of user position. However, a key challenge for multiconstellation GNSS positioning is to identify the trustable satellite measurements from all visible satellites for getting true position. The quality of the estimated position depends on the ability of the receiver on getting position in the presence of outlying observations and on the receiver environments. Multipath and NLoS (non-line of sight) signals can cause errors in the satellite observations for the receiver moving in challenging environments such as urban canyons, under the elevated highway or under the dense trees. Hence it is a problematic task to achieve the positioning accuracy and reliability in such environments and it is required to adopt the positioning estimation techniques to prevent the effect of possible wrong or outlier satellite observation on the user position. A number of algorithms and methods have been proposed to improve the GNSS receiver performance in terms of the positioning accuracy. The geometric dilution of precision--(GDOP) based best satellite selection is one of the proposed methods [1; 2; 3; 4; 5]. The fundamental idea is to select a subset of observations having a minimum GDOP factor, though the rejection of a good observation can also lead to the minimized GDOP. Receiver autonomous integrity monitoring (RAIM) is another approach to reject outlier observations using pseudorange measurement redundancy to maintain the positioning accuracy by isolating the contaminated measurements from the good ones [6; 7; 8], and is primarily designed for aviation applications [9; 10]. However, for user position estimation in urban environments, visible and available satellites are rapidly changing and it is difficult to use the RAIM for satellite/pseudorange selections. Besides the satellite/observation selection techniques, several researchers also focused to improve the estimation algorithms to enhance the positioning accuracy. Least-square estimators (LSE) has been used as the fundamental algorithm for GNSS positioning since the start [11]. LSE estimates the position of user by making a relationship between the unknown parameters and satellite to receiver geometry. However, it is assumed during LSE-based estimation, that the variance-covariance matrix of the observations, also called the dispersion matrix, is an identity matrix i.e., all the observations have same accuracy and are uncorrelated. On the other hand, real time GNSS observations have different level of accuracies depending upon the satellite elevation angle, signal strength and other radio interferences. Multiple algorithms and methods have been proposed to improve the GNSS positioning accuracy. WLSE (weighted least-squares estimation) schemes are proposed for calculating and assigning weights to the GNSS observations. In [12], authors proposed a WLSE (weighted least-squares estimator) where signal to noise ratio and observation variance are the weight model parameters. [PERSON] [13] proposed a fuzzy weighted least squares method to enhance the GPS and the GLONASS (RUSSIAN GNSS) integrated navigation solution and 12 rules were defined based on the GDOP and the elevation angles of satellites for assigning weights to the GNSS observations.