Adaptive transmit beamspace design for cognitive FDA radar tracking

摘要

In this study, the authors propose an energy-efficient adaptive transmit beamspace design for cognitive frequency diverse array (FDA) radar to efficiently detect and track the target in a cognitive way, particularly, for the case when the target is lost in one scan. Additionally, an algorithm is presented for a selection of the illumination range-angle sector to recover a lost target. Moreover, an algorithm is proposed to discriminate the target from interferers, while an FDA beampattern time-variance that is normally ignored in the literature is also considered. Finally, the transmit beamspace is adaptively updated to illuminate the predicted range-angle-based area of interest in each cognitive scanning cycle. The proposed design generates a single maximum beam pattern by using a set of non-uniform frequency offsets along an FDA. Numerical results show that the proposed method achieves improved target detection and tracking performance as compared to the standard frequency diverse and phased-array radars due to its increased output signal-to-interference-plus-noise ratio, while improved energy efficiency is achieved due to an adaptive range-angle sector selection algorithm.