Safran Electronics & Defense

Association Pour La Recherche Developpement Des Methodes Et Processus Industriels-a.r.m.in.e.s.

Association Pour La Recherche Et Le Developpement De Methodes Et Processus Industriels “Armines”

Axel Barrau

Silvere Bonnabel

Emmanuel Robert

Silvère Bonnabel

Thomas Bernal

A method for assisting with the navigation of a fleet of vehicles including a main vehicle and a secondary vehicle that is mobile in relation to the main vehicle, the method including receiving relative movement data, acquired by one or more sensors, between the main vehicle and the secondary vehicle, estimating a navigation status of the fleet of vehicles by an invariant Kalman filter using the received data as observations, the navigation status including first variables representing a first rigid transformation linking a location mark associated with the main vehicle to a reference point, and second variables representing a second rigid transformation linking a location mark associated with the main vehicle to a location mark associated with the secondary vehicle, the invariant Kalman filter using, as an internal composition law, a law including a term-by term composition of the first rigid transformation and the second rigid transformation.

Vehicle navigation assistance method and device using an invariant Kalman filter and a navigation status of a second vehicle

The invention relates to an alignment method based on a simplified mode allowing processing using an invariant Kalman filter, in which each speed involved in the navigation equations is expressed in a work reference frame (Rt) translated with respect to an inertial reference frame (Ri) and for which the origin moves along a reference inertial trajectory, the carrier of which is assumed to be close (geographic origin for alignment with the sun at known position, GPS trajectory for alignment in-motion, etc.). This simplified mode comprises the repetition of the following steps to estimate a mobile carrier state (P): &#x2014;propagation (PROP) determining an estimated current state from a preceding estimated state, inertial sensor measurements and theoretical information on the carrier trajectory (P) &#x2014;updating (MAJ) the estimated state using theoretical information on the carrier trajectory (P). The deterministic uncertainties of the sensors (bias/drift/scale factors, etc.) are only estimated during the propagation and update steps. A perturbation step (PERT) then allows inclusion of the values ignored in the simplified mode and estimation of these values by an optimisation method.

Alignment method for an inertial unit

A method for estimating the state of a moving system, using a particle filter. The method implements on one reference particle an estimation of the state and includes a first set of variables and a second set of variables. A random mutation, by the particle filter, of the first set of variables is performed followed by parameterization of an extended Kalman filter using the mutated first set of variables. The Kalman filter produces a new particle using the second set of variables and data measured by at least one sensor. The second set of variables includes at least the variables: orientation, speed and position of the moving system. The Kalman filter is configured assuming that the pair of variables orientation and position has a property of invariance upon a rotation or translation and their orientation and speed has a property of invariance on application of a rotation or translation.

Invariant particle filtering

A method for tracking the navigation of a mobile carrier, in which an extended Kalman filter estimates, over successive iterations, a navigation state of the carrier. Each iteration propagates a previous navigation state of the carrier into a propagated state, updates the propagated state according to measurements acquired by at least one navigation sensor. The updating includes: calculating a linear correction term from an innovation representative of a difference between the measurements acquired by the navigation sensor and the propagated state, calculating an exponential of the linear correction in terms of a Lie group, calculating a first correction term expressed in a reference frame that is fixed relative to the carrier, calculating a second correction term expressed in an inertial reference frame in which the carrier is mobile, and adding the second correction term to the value of the variable contained in the propagated state.

Growing community of inventors

Boulogne-Billancourt, France

Axel Barrau