# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 3 Sensor Fusion: Loosely Coupled Algorithm Implementation
### The Initialisation Loop
Exploitation of the PointPerfect augmentation service noticeably improves the mean of the accuracy values and also reduces their variance, leading to an improved system's reliability. Although requirements of the EMERGE reference use cases are satisfied, the recommendation points towards a configuration with a Multi-constellation/multi-frequency approach plus GNSS Augmentation Service. With the perspective to support high and full automated Connected and Automated Driving (CAD) functions, as detailed in 3 GPP and ETSI latest specifications [35], a reduction of the order of magnitude (from metres to centimetres) in the accuracy parameters in the satellite navigation process is more than justified. One of the advantages of using configurations where all available satellite resources in view are utilised is the ability to select (exclude) the SVs (space vehicles) to be used. This has a direct impact on other satellite navigation KPIs. When using all the potentialities of the receiver, it can be observed doubling of the number of used satellites, as well as an improvement in the Dilution of Precision (DOP) parameters that mitigate the (mathematical) error in the calculation of the position due to the effects of the spatial distribution of the GNSS satellites. Values of the Integrity, which is a crucial element in automotive applications, may be insufficient for some specific automotive use cases, despite the usage of the _GNSS and PointPerfect_ configuration. This is why other sensors such as Lidar, Radar and Video cameras must be incorporated in specific scenarios and applications. In fact, the EMERGE project foresees the integration of sensors (as external components) in the navigation OBU. #### 5.1.1 Sensor Integration

This section presents the experimental results obtained using the sensor fusion (SF) algorithm described in Section 3. To assess the algorithm's performance under realistic conditions, we conducted tests in an open-sky environment near our laboratory, ensuring clear visibility. We intentionally opted for this setting to maintain flexibility in test conditions and trajectory definition. We decided to focus our tests on real-world scenarios characterized by urban signal degradation, therefore augmenting the GNSS data further would not have provided significant insights. Our aim was to underscore the practical utility of INS in complementing GNSS signals, particularly in scenarios where augmentation may not substantially contribute to improving accuracy or robustness. Hence, we opted to concentrate on the primary GNSS signals from GPS and Galileo, augmented by the INS data. To replicate inaccuracies typically encountered in urban settings, a fault injection operation was applied to the GNSS-only signal (GPS + GALILEO). To simulate the effect of random variations and inaccuracies in the GNSS signal, we employed a Brownian motion process, that generated random additive variations for latitude, longitude, and altitude. In this way, we obtained the estimates of position provided by a degraded GNSS. Subsequently, we evaluated the impact of data fusion in terms of accuracy, stability, and response to errors that affected GNSS without augmentation and IMU sensors. The tests were conducted near the Abruzzo Technopole, located at Strada Statale 17 Loc. Boschetto di Pile, 67100 L'Aquila (AQ), Italy. Figures 8-12 illustrate the five test scenarios under consideration. Each figure depicts the starting point of the vehicle detected by each sensor with a circle. The dashed line represents the ground truth, derived from the GNSS signal (all constellations) with PP corrections applied every 5 s. The solid purple line represents the trajectory obtained with GNSS-only, while the solid blue line represents the trajectory obtained from the sensor fusion algorithm, integrating IMU/GNSS data. A preliminary analysis reveals that in each scenario, the trajectory from GNSS-only, which has undergone degradation, deviates further from the ground truth compared to the trajectory returned by the data fusion algorithm.