Measurement Level Integration of Multiple Low-Cost GPS Receivers for UAVs

UAVs are increasingly being used outdoors for surveillance, exploration, search, rescue and other purposes. A good position estimate allows us to precisely navigate and avoid objects, thus maximising the potential of autonomous applications. There is a growing need for an inexpensive, high performance GPS solution providing better accuracy and robustness. In this paper we incorporate measurements from multiple low-cost, lightweight receivers using an Extended Kalman Filter (EKF). The estimates from the EKF are then compared to the estimates from just one receiver. Experiments are conducted by placing five u-blox receivers on a quadrotor, one on each arm and one at the center. The results show an improvement in accuracy and robustness while using multiple receivers. INTRODUCTION Many autonomous tasks depend on the level of accuracy of position estimates given by the respective sensors. In recent times, there has been a sharp increase in the use of Unmanned Aerial Vehicles for outdoor tasks. For such applications, GPS receivers are an easily available option to estimate one’s position. However the accuracy of the estimates depends on the quality of the receiver being used, with low-cost GPS receivers providing limited accuracy and robustness. Thus there is a growing demand for a GPS solution providing better accuracy, signal availability and robustness against multipath and other sources of errors. While integrating the measurements for long durations is an option, it is not suitable for highly dynamical autonomous systems like UAVs. Further, due to the changing orientation of the UAV the antennas face different directions and hence different subsets of the visible satellites. The weight of GPS receivers has to be considered as well. Heavy GPS receivers are not practical for UAVs with limited payload capacity. Generally, GPS receivers are considered to be black boxes which provide us with position updates. However there are several receivers that allow researchers access to the raw signals as well as the measurements collected by the receivers. There are numerous techniques of using the measurements or raw signals to obtain an improved navigation solution [1,3]. Using an Extended Kalman Filter to incorporate GPS measurements [2] with the Inertial Navigation System is a technique that has been explored in depth [5-8]. The use of multiple low-cost receivers is a field that is gaining importance [3,4]. Different methods of incorporating multiple receivers, like an averaging the latitude-longitude [4] and a raw signal-level integration using Kalman Filter [3], have been previously explored. The objective of this paper is to propose multiple-receiver architecture with measurement level integration using an Extended Kalman Filter. We proceed to validate this method by conducting an experiment with multiple lowcost receivers mounted on a UAV. In the rest of the paper, we will first discuss the algorithm and describe all the matrices being used in the Extended Kalman Filter. We will then look into the experimental setup being used, followed by an analysis of the results. APPROACH AND ALGORITHM In this paper, we propose to use multiple low-cost, lightweight receivers from u-blox instead of just one. We place one receiver on each arm of a quadrotor and another one at the center. We then carry out a measurement level integration of all the receivers to obtain a better estimate for the position of the center of the quadrotor. An Extended Kalman Filter is used for integrating the measurements and estimating the states, which includes the position of the quadrotor. Since the frame of the quadrotor is rigid and the GPS receivers are fixed on it, the position and dynamics of the receivers are related to each other and hence constrained. Figure 1 shows a simple block diagram for the multireceiver architecture. Figure 1. Approach overview. Multiple antennas track the signals from visible GPS satellites. These signals are sent to the respective receivers. Each receiver then applies acquisition and tracking algorithms to generate measurements like the pseudorange and the carrier phase. Finally the pseudorange measurements from all the receivers are then used in an extended Kalman filter, which estimates the position of the quadrotor. The following is the state vector being used in the EKF: