Method of reference image selection to provide high-speed aircraft navigation under conditions of rapid change of flight trajectory

It is reasonable to find a new approach to the selection of the Reference Image (RI) to determine the spatial position of Unmanned Aerial Vehicles (UAVs) with Correlation-Extreme Navigation System (CENS). The selection has to be made from a set of images available on board. This is due to the need to increase the speed of secondary processing of information by the CENS. This is due to high flight speeds and possible intensive maneuvering of UAVs. The use of multi-spectral sensors with different resolution also leads to the need to increase the speed of secondary processing systems of combined CENS. The paper presents an improved model of the Decisive Function (DF) formation process for a set of reference images. Using this model, the problem of method and algorithm development for rational choice of RI in the CENS secondary processing system is formulated. The results of the development of an iterative method and algorithm for the selection of RI from the set of RI recorded on board the UAV is presented. The method consists in the use of iterative procedure of RI selection from the multidimensional matrix representation of the set of RI for different altitudes. Then the selection is carried out according to the angular parameters. The effectiveness of the method was confirmed by simulations. The simulation was performed using the brightness distribution of a typical fragment of the Sighting Surface (SS) image. Influence of observation conditions and sensors' resolution on the difference between the fragments of the Current Image (CI) and the reference image was studied. An algorithm for the rational choice of the RI based on the proposed method was developed. It is shown that the computational complexity of image matching in the CENS can be reduced by tens of times without loss of accuracy in determining the spatial position of an UAV. The application of the algorithm developed for the example considered in the article allows to reduce the computational complexity by 210 times.