Pubblicazioni

Versione: 1.0 Approvato

LDO-ID 2017-10-02-02

Titolo Adaptive beamforming algorithms performance evaluation for active array radars

Abstract

The capability to maximize the signal of interest while minimizing possible interferences that could be present in operational environments is crucial for modern active array antenna radars. The use of active array antennas allows to sample signals in the proximity of the antenna by using high speed Analogue to Digital Converters (ADC). The digital signal processing carried out afterwards can find the optimum weights for every receiving channel to maximize the signal of interest while forming nulls in the antenna array pattern to suppress interferences either in the sidelobes or in the main beam. Before the advent of digitally scanned array antennas the possibility to implement digital beamforming algorithms was confined to digital signal processing on formed beams. Today, the design of radar systems equipped with hundreds of receiving channels each with a dedicated ADC, makes it feasible to accomplish adaptive digital beamforming algorithms before the beams’ formation. Valuable capabilities for nowadays active array multifunctional radars can be summed up as follows: (i) maximization of the signal of interest while adaptively nulling possible multiple interferences, (ii) low sidelobes for clutter rejection, (iii) good directivity for the main beam lobe and good performances even in presence of phase and amplitude mismatches in the receivers. This article examines and compares the performances of various digital Adaptive Beam Forming (ABF) algorithms to determine which of them could be successfully employed in modern Active Electronically Scanned Array (AESA) radar and performs best in harsh environments such as those usually found where radars operate

Autori Tocca Valerio, Vigilante Domenico, Timmoneri Luca, Farina Alfonso

Tipologia Paper for Seminar/Symposium/Conference

Media 2018 IEEE Radar Conference (23-27 April 2018, Oklaoma City, OK, USA)

Sito web

Anno 2017