Airborne system for determining the position of an aerial vehicle and its applications

Claim 2

In an airborne system for determining the position of an aerial vehicle, of the type comprising sensitive means scanning the ground flown over, Kalman filter means, storage means containing the map of the ground to be flown over, means for comparison between the information delivered by said sensitive means and information from said storage means, as well as computing means delivering error information to said Kalman filter means, the improvement wherein: said storage means comprises a first memory containing the general characteristics of different types of discrete landmarks and a second memory containing the geographical position of such landmarks on the ground to be flown over; and said system further comprises: a device for extracting, from the signals delivered by said sensitive means, said general characteristics of said different types of discrete landmarks located on the ground being flown over; and a device for computing, from the signals delivered by said sensitive means, the relative positions of said vehicle with respect to said recognized landmarks, which is sends to said Kalman filter means; said comparison means comprising a first comparison device comparing the information from said extraction device with that contained in said first memory, as well as a second comparison device comparing the information from said first comparison device with that contained in said second memory.

Claim 3

The system as claimed in claim 1, wherein: said computing means computes, for the information from said first comparison device, the probability for each landmark recognized by said sensitive means to belong to each of said types of landmarks; said second memory comprises for each landmark described by its geographical position the probability for said landmark to be each of said types of landmarks; and said second comparison device compares, for each of the landmarks contained in said second memory, the probabilities of being each of said types of landmarks with the similar probabilities delivered by said first comparison device for a landmark recognized by said sensitive means.

Claim 4

The system as claimed in claim 2, wherein said sensitive means comprise a plurality of different sensors.

Claim 5

The system as claimed in claim 2, wherein said second comparison device infers said probabilities following the rules drawn from Dempster and Shafer's theory of evidence.

Claim 6

The system as claimed in claim 2, wherein determination of said error information by said computing means is implemented by Bar Shalom's PDAF technique.

Claim 7

The system as claimed in claim 2, comprising : a first computing unit, connected to said device computing the relative positions of the vehicle with respect to the recognized landmarks and to the Kalman filter properly speaking, estimating the absolute position of an observed landmark, as well as the covariance matrix of the innovation regarding the position of the observed landmark; a second computing unit, connected to said second memory and to said first computing unit, providing statistical fenestration of the landmarks of said second memory; a third computing unit, connected to said second computing unit, determining the probability of associating the detected landmark with the landmarks of said second memory, according to the PDAF technique; a fourth computing unit, connected to the first comparison device, putting into practice the rules of inference of the theory of evidence for synthesizing the information available concerning the identity of the observed landmark; a fifth computing unit, connected to the second and fourth computing units, working out the masses to be attributed to the different possible sets of hypotheses of associating the observed landmark with the landmarks of the second memory selected by the second computing unit; a sixth computing unit, connected to said third and fifth computing units, merging the masses of probabilities attributed to the hypotheses of associating the detected landmark with the different landmarks of the second memory by said third computing unit with the masses attributed to all the possible sets of hypotheses by said fifth computing unit; a seventh computing unit, connected to said sixth computing unit and evaluating the innovation of the Kalman filter using the PDAF technique; and an eighth computing unit, connected to said seventh computing unit and supplying the gain to the Kalman filter and up-dating same.

Claim 8

A navigational aid system for an aerial vehicle, comprising the system specified under claim 1 and display device on which the output signals of the Kalman filter are displayed.

Claim 9

Automatic guidance system for an aerial vehicle, comprising the system specified under claim 1 and devices using the output signals from the Kalman filter for acting on the members controlling steering of said vehicle.