Coupling GPS/INS and IMM Radar Tracking Algorithms for Precise Collaborative Ground Vehicle Navigation

Abstract

This thesis describes a method of collaborative ground vehicle navigation utilizing shared radar data to provide observations during periods of GPS degradation. Navigational errors that typically arise from degraded GPS signals can be reduced by providing relative observations between vehicles from an Interacting Multiple Model (IMM) radar tracking filter. Loosely coupled GPS/INS Extended Kalman Filters provide navigation solutions for each vehicle. When a vehicle experiences GPS outages, other vehicles provide external observations from the IMM tracking filter to correct the INS solution and bound error growth during the outage. The IMM tracking filter uses constant velocity, constant acceleration, and constant turn models in combination to generate a tracking solution. An evaluation of the performance of the proposed method is presented using both simulated and experimental data. The IMM tracking algorithm is implemented using range, range-rate, and azimuth data from a Delphi electronically scanning radar. Results show improved navigation performance when utilizing the relative observations during GPS outages. Specifically, the drift of the INS solution is bounded by the external measurements provided by the IMM tracking filter when GPS is unavailable. Results from both simulated and experimental data sets show that the system provides drastic improvements over standalone INS navigation, with up to a 94% decrease in error on position. These results demonstrate that the proposed combination of GPS/INS and Radar IMM algorithms constitute a feasible method of maintaing navigational accuracy during GPS outages.