## 1 Introduction  
The advantages of combining satellite-based navigation systems with inertial navigation systems (INS) have long been recognized. Due to the various errors induced by the space segment, signal propagation, receiver technology or user environment, the accuracy of global navigation satellite system (GNSS) receivers is not sufficient to address the requirements of all applications in geodesy and navigation. Therefore, many navigation or surveying systems use hybrid GNSS/INS technologies to combine the short time stability of the inertial sensors with the long time stability, yet noisy behavior, of GNSS receivers. Depending on the integration scheme and the performance of the inertial sensors, a variety of advantages in accuracy, availability, and integrity can be achieved [1]. The three possible basic GNSS/INS integration schemes are loose, tight and ultra-tight (or deep) integration [2]. In the loose approach, the position and velocity from both systems are integrated to create a third blended navigation solution. In the tight approach, GNSS pseudoranges and Doppler are blended with INS accelerometer and gyro measurements to generate a single blended navigation solution. In the ultra-tight approach, integration occurs at the GNSS tracking loops which are controlled by the blended navigation filter [3]. The GNSS receiver in this case is no longer an independent navigator since its operation is also partly dependent on INS information. The potential benefits of the deep integration are achieved at the expense of a significant increase in complexity, computational load, and tight time synchronization [4]. It is worth mentioning that there is not a standardized definition for the types of GNSS/INS integrations. While some papers refers to 'ultra-tight' or 'deep' integration when there is a feed-forward component from the navigation filter to the GNSS tracking loops, other papers (such as this one) refer to such setups as 'tight' coupling and use the terms 'ultra-tight' or 'deep' only when the tracking loops' numerically controlled oscillators (NCOs) are fully controlled by the blended navigation filter in a vector tracking approach. More details about the different naming conventions can be found in [5]. The theory and measurement models of such ultra-tight GNSS/INS schemes have already been provided in many research works. For instance, in [6; 7; 8], the authors present analytical and simulation results under specific scenarios, but they do not provide fields experiment results. In recent years, micro-electro-mechanical systems (MEMS) inertial sensors have opened the door to a wealth of new commercial applications and are present in various it's that are used on a daily basis (e.g., most smart phones on the market already include at least an accelerometer, a gyroscope or a magnetometer). Apart from size reduction, MEMS technology has offered many benefits such as batch production, cost and power reductions and design flexibility, within limits. However, the reduction in size of the sensing elements creates challenges for attaining good performance. Several papers have already discussed the integration of MEMS inertial sensors with GPS. For example, in [9], the authors described the design, operation and performance of an integrated GPS/MEMS inertial navigation package, without using the inertial measurements to aid the GPS tracking loops. In [10], the author proposes a tightly coupled MEMS INS/GPS integration with INS-aided receiver tracking loops, but he does not discuss the quality of aiding, especially when GPS outages occur or when fewer satellites are tracked. The impact on the GPS tracking loops was partially addressed and no assessment was made of the benefits of such integration on re-acquisition. Also, in [11], the author proposes a tight integration with loop aiding using a GPS/reduced inertial measurement unit (IMU) coupling and an adaptive loop filter bandwidth. However, the impact on re-acquisition and the GPS outage limitations are not assessed.