Collaborative Ground Vehicle Navigation Utilizing an IMM Radar Tracking Algorithm

Joseph Selikoff and David M. Bevly

Abstract:	This work proposes 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 are 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 that shows 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, maintaining the desired performance in GPS adverse conditions.
Published in:	Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018) September 24 - 28, 2018 Hyatt Regency Miami Miami, Florida
Pages:	1590 - 1604
Cite this article:	Selikoff, Joseph, Bevly, David M., "Collaborative Ground Vehicle Navigation Utilizing an IMM Radar Tracking Algorithm," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 1590-1604. https://doi.org/10.33012/2018.15844
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