Aviation is one of the earliest application of the Global Navigation Satellite System (GNSS). Since the early days of the Global Positioning System (GPS), satellite navigation has been an essential part of the aviation industry. Being a particular mean of transport, which usually involves a large number of human lives, civil aviation always requires a high level of reliability from the navigation system. Such requirement brings about the concept of integrity, which concerns about the consistency and reliability of a navigation system, is defined as the capability of the system to provide timely warning when it should not be used for navigation. The concept of integrity allows the standardization of guidance systems' performance, with the utmost purpose of keeping safety for every flight. The concept of integrity has gained interests in other GNSS applications as well, especially in those that also require high reliability from the navigation solution, such as Intelligent Transport System (ITS), railways. This leads to the necessity to adapt the integrity monitoring techniques, in particular the Receiver Autonomous Integrity Monitoring (RAIM) algorithms, to use in working conditions other than the typical airport areas, such as urban environment. As a matter of fact, adaptation of RAIM algorithms to urban environment requires a throughout analysis of the environmental difference of the working condition as well as the requirement of the intended applications. This thesis focuses on developing a Kalman filter-based Advanced RAIM (ARAIM) algorithm for urban environment, which is an adaptation of the conventional ARAIM algorithm for civil aviation. ARAIM algorithm is considered the next generation of RAIM, aiming at providing higher integrity performance for more stringent phase of flight. The first step is to survey the necessary changes to adapt ARAIM algorithm to urban scenario. Experimental study highlights the prerequisite of finding a noise model to represents the signal noise level in urban area. After a suitable noise model was found after a comparative study, the KF-based ARAIM algorithm was developed. This method evaluates the separation of state correction using different subsets of measurement to detect abnormalities as well as potential faulty satellites for exclusion. The proposed method was also validated using simulation and real data. Performance analysis results show that the proposed algorithm can effectively follows the changes of signal quality which is expected to occur frequently when moving in urban environment, confirming its suitability for integrity monitoring in urban environment.