INS/GPS for High-Dynamic UAV-Based Applications

The carrier-phase-derived delta pseudorange measurements are often used for velocity determination. However, it is a type of integrated measurements with errors strongly related to pseudorange errors at the start and end of the integration interval. Conventional methods circumvent these errors with approximations, which may lead to large velocity estimation errors in high-dynamic applications. In this paper, we employ the extra states to “remember” the pseudorange errors at the start point of the integration interval. Sequential processing is employed for reducing the processing load. Simulations are performed based on a field-collected UAV trajectory. Numerical results show that the correct handling of errors involved in the delta pseudorange measurements is critical for high-dynamic applications. Besides, sequential processing can update different types of measurements without degrading the system estimation accuracy, if certain conditions are met. 1. Introduction GPS receivers are widely used in navigation. However, the system performance largely depends on the signal environment, and the measurement update rate is low. This raises the need to integrate GPS with the inertial navigation system (INS) to have a robust continuous navigation solution regardless of the environment. Among several integration architectures, the tightly coupled INS/GPS integration is one of the most promising methods to fuse the GPS and INS data, where the code-derived pseudorange and carrier-phase-derived delta pseudorange measurements are often exploited. However, the delta pseudorange is an integrated measurement with errors strictly related to the pseudorange errors at the endpoints of the integration interval. In practice, various approximations are made to handle these errors. The weakest but often used approach is to simply consider the integral of velocity divided by time (an average value) as the instantaneous velocity measurement at the endpoint of the integration interval [1]. It may fulfill the accuracy requirements in static or low dynamic applications. Nevertheless, if the vehicle is maneuvering under high dynamics with low GPS data update rate, large velocity estimation errors will appear. That is, the velocity errors will be strongly correlated to the accelerations and jerks involved in the trajectory [2]. In order to tackle this problem, we use delay states to “remember” the pseudorange errors at the start of the integration interval. Besides, for reducing the processing load, sequential processing is utilized to avoid the time consuming computation of matrix