Introduction. Short-wave stations for detecting airborne objects of the ionospheric type have a number of limited technical characteristics, one of which is their low azimuth resolution. This limitation is manifested in the impossibility to separately observe air objects in a group, the distance between which is less than 30 km (at an observation range of 2000 km). The technical characteristics under consideration can be improved by making changes to the dimensions of the receiving antenna array (AA); however, such changes lead, as a rule, to unjustified engineering and financial costs. In practice, space-time signal processing is carried out using conventional superresolution methods, which, although increasing the resolution of the station, decrease the rate of delivery of observation results to the operator due to an additional computational load. It is necessary to find a compromise between the maximum possible resolution indicator and the acceptable load on the system during signal processing.Aim. Analysis of the phase distribution of the incident wave scattered by objects at the AA aperture, as well as the azimuthal images of these objects when performing spacetime signal processing after extrapolating the AA aperture function by evaluating linear prediction using the least-squares method using autoregressive model coefficients.Materials and methods. Modelling of phase distributions at the AA aperture and azimuthal images of the observed objects was conducted in the MATLAB environment.Results. It is shown that the problem of increasing the azimuth resolution of a short-wave station for detecting air objects can be successfully solved using linear prediction based on the least-squares method using autoregressive model coefficients for the extrapolation of the AA aperture function. The results obtained during modelling were analysed using the example of group observation of air objects.Conclusion. The proposed approach for extrapolation of the AA aperture function for short-wave stations with large receiving AAs proved its relevance. The method proposed for increasing the resolution is characterized by a lower computational load, thereby being promising for practical application.
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