Design and evaluation of integrity algorithms for PPP in kinematic applications Kazuma Gunning, Juan Blanch, Todd Walter, Stanford University Lance de Groot, Laura Norman, Hexagon Positioning Intelligence ABSTRACT UAV and autonomous platforms can greatly benefit from an assured position solution with high integrity error bounds. The expected high degree of connectivity in these vehicles will allow users to receive real time precise clock and ephemeris corrections, which enable the use of Precise Point Positioning techniques. Up to now, these techniques have mostly been used to provide high accuracy, rather than focusing on high integrity applications. In this paper we apply the methodology and algorithms used in aviation to determine position error bounds with high integrity (or protection levels) for a PPP position solution. 0. INTRODUCTION Precise Point Positioning techniques [1] can provide centimeter accuracy without local reference stations in kinematic applications. These techniques have so far mostly been used to provide high accuracy, and it is only recently that they have been proposed to provide integrity, that is, position error bounds with a very low probability of exceeding them. There has been preliminary work [2] on the application of integrity to PPP, but it remains a challenge to translate the benefits of PPP to accuracy while maintaining high integrity. Most of the integrity work in PPP and RTK has dealt with the ambiguity resolution process under nominal error conditions [3], [4], and less on the integrity of the position solution under fault conditions. We start (Section 1) with an overview of our PPP filter implementation, and a description of the threat model (Section 2). In Section 3, we describe two classes of integrity algorithms: solution separation and sum of squared residuals based (also called residual based (RB) –which is a misnomer, as all autonomous integrity monitors are based on the residuals.) Section 4 presents the data sets that were used to evaluate the algorithms. In Section 5, we compare the PLs obtained with different algorithms. In section 6, we present the results obtained with the most promising PL formulation in four different data sets: static, dynamic in open sky conditions, dynamic in midtown suburban conditions, and in flight. 1. PPP ALGORITHM OVERVIEW PPP techniques achieve small position solution errors by using precise clock and ephemeris corrections and applying models of the remaining errors (both temporal and spatial). Our sequential PPP filter implementation is based on a simple extended Kalman filter (EKF) with estimated parameters comprising the receiver position, clock biases for each constellation in use, a tropospheric delay, float ambiguities for each tracked carrier phase, and multipath contributions. Dual-frequency measurements can be incorporated from GPS, GLONASS, Galileo, and BeiDou. The precise orbit and clock estimates are drawn from the Center for Orbit Determination in Europe (CODE) which is an IGS MGEX analysis center. We also implemented a batch least squares PPP that, while not useful for real-time applications, can be used for static and kinematic truth position generation. The PPP filters developed have been shown to perform with centimeter-level accuracy for static scenarios and decimeter-level accuracy for kinematic scenarios.