## 1 Introduction  
Nowadays, positioning services can greatly improve life quality by covering a wide range of applications such as automatic driving, intelligent transportation, agriculture and so on. In this context, the Global Navigation Satellite System (GNSS) is an optimal infrastructure to perform accurate positioning. The GNSS-based absolute positioning technologies can be called single point positioning (SPP) technologies and offer multifold advantages, such as the absence of restriction by the inter-station distance, low cost and simple data processing. GNSS brings more signals to enhance the accuracy of user position. However, a main challenge for multi-constellation GNSS positioning is to determine the reliable satellite measurements from all visible satellites for inferring true positions. The estimation precision depends on the ability of the receiver to get the position in the presence of outlying observations that can affect the data. Multipath and NLoS (Non-Line Of Sight) signals can cause errors in the satellite observations for the receiver moving in harsh environments such as urban canyons, under crowded trees, or inside tunnels. It is thus challenging to achieve the positioning accuracy and reliability in such environments and it is required to adopt robust positioning estimation techniques to prevent the effects of possible wrong or outlier satellite observations on the user position. For this purpose, several algorithms and methods have been proposed to improve the GNSS receiver performance in terms of positioning accuracy. Receiver autonomous integrity monitoring (RAIM) is a commonly used approach to reject outlier observations using pseudo-range measurement redundancy to maintain the positioning accuracy by isolating the contaminated measurements from the good ones [1; 2; 3]. 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 selection. Besides the satellite selection, Weighted Least-Squares Estimation (WLSE) is often used for calculating and assigning weights to the GNSS observations [4; 5; 6; 7; 8; 9]. The weight model is defined based on the elevation angle and the signal to noise ratio. However, it suffers from the same shortcomings as RAIM since the satellite elevation angle and the signal to noise ratio can be impacted by the multipath and radio interferences, especially in urban canyons. This has been highlighted in previous works [8; 9] where we used the WLSE based on the elevation angle and signal to noise ratio. The positioning accuracy can improve, but the estimations remain unstable. In consequence, the robust-Extended Kalman Filter (robust-EKF) [10; 11; 12; 13; 14; 15; 16; 17; 18] is proposed in this paper to address this issue. Robust-EKF is the combination of the Extended Kalman Filter with Iterative Reweighted Least Squares algorithm with a weight matrix defined by MM-Estimation, and using the RAIM algorithm to check the protection zone of user. The robust-EKF is based on robust statistical estimation which is employed to reduce the negative impact of the inaccurate pseudo-range measurements. In the statistical framework, robust estimation algorithms are assessed based on three factors: efficiency, stability and breakdown point. Efficiency is a measure of the performance of an estimator; stability is a measure of how small deviations from the model assumptions affect the performance; whereas the breakdown point is defined as the measure of the maximal fraction of outlier data that the estimator can handle without resulting in an incorrect final estimate. The role of a robust algorithm is to harmonize these three factors. Multiple robust statistical estimation techniques have been discussed including M-Estimation, S-Estimation, MM-Estimation etc. [19]. In this paper, the MM-Estimation proposed by [PERSON] [20] is used to define the weight matrix for its efficient statistical data analysis with a high breakdown point.