# The Local Integrity Approach for Urban Contexts:
## 1 Introduction
The general idea is to take into account not only the GNSS system, but also the local environment nearby the receiver, focusing on expected signal degradations (e.g., multipath and non-line-of-sight signals). Aggregated position- and time-dependent information can be used in order to estimate an equivalent range error essentially dependent on the local environment around the receiver (local UERE): such data aggregation represents a first important point in order to characterize the local errors in nominal conditions. As a second step, the aggregated data can be shared by means of a VANET infrastructure in order to estimate \"Local\" Protection Level (LPL) values. Further steps can include the definition of proper integrity checking strategies and related parameters (_i.e._, testing LPL values _versus_ properly identified alarm limits). These strategies are needed in order to detect and possibly mitigate local errors in non-nominal conditions. In addition, once available, the local UERE information can become fundamental for properly weighting the GNSS receiver output with respect to the position information from other sensors available on-board of a vehicle. This approach can potentially improve the robustness of the combined position shown to the car driver, for example in case of frequent GNSS signal outages. Concerning the first step for the implementation of a collaborative integrity monitoring, which is the estimation of the local UERE, a preliminary investigation has already been carried out by the authors in [11]. These analyses confirmed the feasibility of local integrity concept, focusing on the repeatability of GNSS degradations in an urban scenario and on how to properly combine the GNSS data for local UERE estimation. A live demonstration of the developed prototype has successfully been carried out in 11 December 2014, during the final event of the GLOVE project [12]. This paper then continues the previous work, analysing new results that prove the suitability of the proposed approach to vehicular applications and demonstrate its performance in a real urban scenario. The real-time prototype implementation of the local integrity algorithm is also presented. This paper is an expanded version of a previous conference paper [13], where preliminary results have been anticipated. The contents have been extended, also taking into account useful feedbacks and comments from the reviewers and the audience of the conference. Among other improvements, the performance assessment has been enhanced by means of Stanford diagrams in order to highlight the performance and conservativeness of the proposed approach. More details on the implementation of the vehicular prototype have also been provided. After this introduction, the next section describes the local integrity approach, presenting a suitable architecture and the estimation method for the local UERE. The following sections are focused on the experimental validation of the method and its implementation in a prototype demonstrator. Obtained results from field tests and vehicular data collections are then presented, followed by the final remarks.