Enhancing filter robustness in cascaded GPS-INS integrations

Abstract

Filter robustness is defined as the ability of the GPS-INS Kalman filter to cope with adverse environments and input conditions, to successfully identify such conditions and to take evasive action. This paper discusses the formulation of two such techniques for a cascaded GPS-INS Kalman filter integration. This is an integration in which the navigation solution from a GPS receiver is used as a measurement in the filter to estimate inertial errors and instrument biases. The first technique presented discusses the handling of GPS position biases. These are due to errors in the GPS satellite segment, and are known to be unobservable. They change levels when a satellite constellation change occurs, at which point they introduce undesirable filter response transients. The paper presents a method of suppressing these transients. The second technique presented deals with the proper identification of the filter measurement noise. Successful formulation of the noise statistics is a factor vital to the healthy estimation of the filter gains and operation. Confidence in the formulation of these statistics can lead to the proper screening and rejection of bad data in the filter. The paper discusses a method of formulating the filter noise statistics dynamically based on inputs from the CPS and the INS.<>