IMPROVED RELATIVE POSITIONING FOR PATH FOLLOWING IN AUTONOMOUS CONVOYS

This work presents the development of an algorithm to incorporate measurements from multiple antennas to improve the relative position solution between convoying vehicles provided by Global Positioning System (GPS) measurements. The technique presented, incorporates measurements from multiple antennas with a known fixed-baseline between a base antenna and auxiliary antenna on a base vehicle, and a rover antenna on a rover vehicle. The additional information provided by the fixed-baseline distance is used to provide an additional measurement with low uncertainty for improved integer ambiguity resolution between the base and auxiliary receiver, which in turn, provides additional measurements for determining the integer ambiguity difference between the base and rover receivers for the computation of a high-precision relative position vector (HPRPV). INTRODUCTION Autonomous vehicle convoying requires precise path following independent of the ability to maintain a constant line-of-sight between vehicles. Common technologies used for path generation, namely cameras and LiDAR, must be in view the of the preceding vehicle and can be susceptible to debris like dust or smoke. Relative position determination provided by GPS techniques do not depend on maintaining a constant line-of-sight with the leading vehicle and are negligibly affected by fog, dust, and smoke. GPS carrier phase observables are widely used to provide precise relative position solutions in kinematic applications. Changes in the carrier signal phase from epoch to epoch can be measured with an accuracy of 2-4 mm; however, the number of whole carrier cycles in the propagation path between the antenna and the satellite is ambiguous. Determining the number of whole carrier cycles in Proceedings of the 2018 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS) IMPROVED RELATIVE POSITIONING FOR PATH FOLLOWING IN AUTONOMOUS CONVOYS, Tabb, et al. UNCLASSIFIED Page 2 of 10 the propagation path is known as carrier phase ambiguity resolution and is crucial for applications requiring centimeter-level positioning. In real-time applications, determination of the carrier phase integer ambiguities on-the-fly is required. A common technique applied to utilize the carrier phase measurement in real-time is differential GPS (DGPS). This approach removes error sources from the atmosphere, clocks, and ephemeris to more easily determine the bias in the measurement as a result of the integer ambiguities. Real-time kinematic positioning (RTK) is an application of DGPS that uses a static receiver, known as a base station, with a well surveyed global position, and a dynamic receiver, commonly known as a rover, that serve as the two receivers for differencing measurements. In this work, this concept is extended to the relative position between two vehicles without the need for a static base station, known as dynamic base real-time kinematic (DRTK) positioning. Improvements in the accuracy of the relative position solution and a reduction in the time required to fix ambiguities to a correct integer value (time-to-fix) by adding a measurement of a known fixed-baseline between antennas on a vehicle are explored. In this work, the coordinate system used is Cartesian earthcentered, earth-fixed (ECEF). DRTK Overview The DRTK method does not require a static base station to provide centimeter-level precision. This is advantageous for convoying where base stations are not available and where an available base station will lose efficacy after distances become too great for benefit (> 2 km) [3]. The traditional DRTK algorithm is a multi-step process that begins with the combination of pseudorange and carrier phase measurements from multiple receivers, as described in Equation (3-4), in a discrete, linear Kalman filter to estimate the relative ambiguities (float solution) between the receivers. Next, the floating-point estimates are fixed to integer values, and finally, the unambiguous carrier phase measurements are used to compute a high-precision relative position vector (HPRPV) solution within a least-squares routine. Measurement Models Equation (1) is the measurement model of the pseudorange measurement. This measurement is effectively a measurement of the signal propagation time from satellite to receiver. This time interval is scaled by the vacuous speed of light to give distance in units of meters. The pseudorange observation between a user and satellite i can be related to the user position and clock states shown below.