Delta potential well-based quantum-optimized time-modulated beamformer for scanning and jamming mitigation in radars

Abstract The design of an effective sidelobe-suppressed beamformer array antenna using ‘time modulation’ is proposed in this research for beam scanning and anti-jamming applications in radar. Time-modulated array (TMa) is the substitute of traditional phased antenna array, where the impinging signal’s phases are controlled by managing the ON and OFF timings of the array radiators. This research aims an optimal TMa beam scanner, targeting a wide scanned region of 90° in the broadside. The scanning patterns are created using delta potential well -based quantum-optimized timing sequences, and the ON durations of the radiators are optimized to create sidelobe-suppressed radiation properties. The beamformer is developed in an 8-element TMa, and the inclusion of modulated schemes enables simultaneous scanned beams over the desired sector. The scanning patterns with suppressed sidelobes are then managed to place strategic nulls pointing to the jamming signal’s directions. The beamformer is developed to point single nulls and multi-nulls in prespecified sectors for anti-jammer purposes, which shows the efficiencies, adaptiveness, and robustness of the TMa beamformer. The directional optimized anti-jammer method is also investigated in terms of convergence speed. The sidelobe-suppressing beamformer demonstrates decent performance, minimizing the sidelobes of the main beams and scanned beams around − 35 dB and − 25 dB, respectively, for all cases. The uniquely designed objective function (OFN) helps to mitigate the unwanted interferences and suppressed higher-order sidebands below − 20 dB. The proposed beamformer also creates deep nulls around − 75 dB and − 60 dB for single-point and multi-point anti-jamming applications, respectively. The radiation efficiencies are maintained above 70%. The proposed beamformer’s superiorities are also established over the recent works in this domain, as well as with a detailed statistical comparison for the quantum-based optimization algorithm.