## 1 Introduction  
As is well known, urban environments are critical locations for Global Navigation Satellite Systems (GNSS). In such environments, buildings block many of the signals, thus reducing satellite availability and weakening observation geometry, with the extreme case being solution unavailability. Buildings can also reflect signals, causing multipath phenomena which introduces the greatest measurement errors in these areas. Past research on this problem can be broadly classified as focusing on: (a) increasing the number of satellites, usually by including additional GNSS to an existing system, or (b) integrating GNSS with external sensors, most commonly an inertial navigation system (INS). With a few exceptions, the U.S. Global Positioning System (GPS) has been the primary GNSS since its inception many years ago. The Russian GLONASS system saw some use in the mid to late 1990s before suffering setbacks that plagued the system until the last few years. Nevertheless, the benefits of integrating GLONASS with GPS have been fairly well documented and improvements in measurement and solution availability, accuracy and reliability of positioning, and ambiguity resolution have been shown [1; 2; 3]. With the recent re-emergence of the GLONASS system, it is once again being considered for used in many systems (ibid.). Integrating GNSS with INS has been very actively researched for many years due to the complementary nature of the two systems. In challenging GNSS environments like urban canyons and under foliage, the goal of the INS is to provide a navigation solution during GPS outages. Moreover the integration of GNSS with an inertial navigation system can deliver more robust and reliable systems than either of the individual systems alone [4]. The performance of the INS, however, is largely dependent on the quality (and thus cost) of the inertial sensors used, with higher quality sensors producing the best results. To this end, use of high-end INS is generally limited to high accuracy applications only, owing to their price and size [5; 6]. That said, recent advances in Micro Electro-Mechanical Systems (MEMS) technology have made possible the development of a new generation of low cost inertial sensors characterized by small size and light weight, which represent an attractive option for commercial applications such as pedestrian and vehicular navigation. MEMS-based INS are characterized by low performance too, especially in the absence of GNSS data, so their use as part of an integrated navigation system is currently under investigation. In the last few years several researchers have investigated the integration of GNSS systems with MEMS-based INS [5; 6; 7; 8; 9; 10; 11; 12]. It is worth noting however, that all of these studies have focused only on GPS for updating the INS. The integration of combined GPS/GLONASS with INS was already tested several years ago (e.g., by [PERSON] _et al_. [13]) or more recently, for instance by [PERSON] _et al_. [14], but with high-end IMU devices), it follows the idea to test in this work the GPS/GLONASS performance with low-cost inertial sensors. For vehicular navigation in particular, constraints on the vehicle's velocity--known as non-holonomic constraints--can be applied to further improve INS performance [6,15-19]. In addition, GNSS-derived yaw (azimuth) can also be used to improve performance [11]. Finally, by assuming the vehicle is running on an approximately level road, a height constraint can also be enforced. The primary objective of this paper is to expand the previous work in order to investigate the benefits, if any, of adding GLONASS to GPS/INS (MEMS) systems, specifically for vehicular navigation applications. As part of this, the paper assesses the performance of loose and tight integration algorithms (_i.e._, position-level and measurement-level integration, respectively).