  Multi-target detection is one of the primary tasks in radar-based
localization and sensing, typically built on phased array antennas. However,
the bulky hardware in the phased array restricts its potential for enhancing
detection accuracy, since the cost and power of the phased array can become
unaffordable as its physical aperture scales up to pursue higher beam shaping
capabilities. To resolve this issue, we propose a radar system enabled by
reconfigurable holographic surfaces (RHSs), a novel meta-surface antenna
composed of meta-material elements with cost-effective and power-efficient
hardware, which performs multi-target detection in an adaptive manner.
Different from the phase-control structure in the phased array, the RHS is able
to apply beamforming by controlling the radiation amplitudes of its elements.
Consequently, traditional beamforming schemes designed for phased arrays cannot
be directly applied to RHSs due to this structural difference. To tackle this
challenge, a waveform and amplitude optimization algorithm (WAOA) is designed
to jointly optimize the radar waveform and RHS amplitudes in order to improve
the detection accuracy. Simulation results reveal that the proposed RHS-enabled
radar increases the probability of detection by 0.13 compared to phased array
radars when six iterations of adaptive detection are performed given the same
hardware cost.
