Development of a case study: tight hybridization using linearized Kalman filtering
Integration of the GPS with integrated navigation systems
Article REF: TE6725 V1
Development of a case study: tight hybridization using linearized Kalman filtering
Integration of the GPS with integrated navigation systems

Author : Anne-Christine ESCHER

Publication date: February 10, 2009, Review date: December 11, 2020 | Lire en français

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4. Development of a case study: tight hybridization using linearized Kalman filtering

The aim of this paragraph is to show an example of how GPS and IRS information are used in a tightly coupled system. Recall that in this type of architecture, the integration filter provides the user with an estimate of inertial errors (position, velocity, attitude and error sources affecting the sensors) by observing the code pseudorange measurements provided by the GPS receiver. The integration filter developed here is a Kalman filter.

4.1 Description by state modeling

The description of the problem by state modeling is given by the two equations – dynamics and observation – nonlinear continuous time below:

x·(t)=f(x(t), t)+w(t)y(t)=h(x(t), t)+v(t)
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