Multiple Direction Interference Suppression by Uniform Linear Phased Array Sidelobe Efficient Canceller

Abstract

Background. For radar systems, the beam pattern of a uniform linear array (ULA) is synthesized to ensure signal selectivity by direction. A specific ULA sidelobe is cancelled by rescaling the beam weights. In particular, this is done by increasing the number of sensors and shortening the scanning step. However, a noticeable limitation is a loss of the transmitted power. Therefore, the problem is to optimally balance the number of sensors versus effective ULA sidelobe cancellation.
 Objective. In order to ensure multiple direction interference suppression, the goal is to find an optimal number of ULA radar sensors for the beam pattern synthesis. The criterion is to determine such a minimum of these sensors at which mainlobes towards useful signal directions are evened as much as possible.
 Methods. To achieve the said goal, the ULA sidelobe cancellation is simulated. The simulation is configured and carried out by using MATLAB® R2020b Phased Array System ToolboxTM functions based on an algorithm of the sidelobe cancellation.
 Results. By increasing the number of ULA sensors, the beam pattern lobes are not only thinned but also change in their power. In particular, the interference direction sidelobes become relatively stronger. The number of sensors is limited by the three influencing factors: the thinned-array curse transmitted power loss, the aperture size, and the sidelobes intensification.
 Conclusions. An optimal number of ULA radar sensors for the beam pattern synthesis can be found when the scanning step is equal to the least distance between adjacent interference directions. At the start, the number of sensors is set at the number of useful signal directions. If the mainlobes towards useful signal directions are not evened enough, the set of interference directions is corrected.
 Keywords: radar phased array; beam pattern; interference direction; sidelobe cancellation; aperture size.

Three-Point Iterated Interval Half-Cutting for Finding All Local Minima of Unknown Single-Variable Function

“…The most prominent examples of the inapplicability of the exact and numerical methods are the problem of fine-tuning hyperparameters and training parameters of neural networks [9], [10], continuous and discrete parameter adjustment for various algorithms [8], [11], [12], the problem of optimizing the phased array size and parameters of beamforming [13], [14], etc. In such examples, an objective function is unknown and its evaluation is either time-consuming or resource-consuming, or both (just like the cases of neural networks and phased arrays).…”