🤖 AI Summary
To address the low polarization information acquisition efficiency and multi-frame measurement requirement in wireless communication and radar systems, this paper proposes a single-frame instantaneous full-polarimetric sensing method based on Zak-OTFS modulation. By simultaneously transmitting Zak-OTFS signals and mutually unbiased spreading waveforms over orthogonally polarized dual channels, the method exploits their time-frequency domain mutual unbiasedness to fully reconstruct the target’s scattering polarization response matrix within a single transmission. The proposed approach reduces the computational complexity of polarization parameter estimation from quadratic in the time–bandwidth product (typical of conventional methods) to sublinear. Simulation results demonstrate significant improvements over state-of-the-art techniques in polarization detection accuracy, parameter estimation performance, and computational efficiency. This work establishes a new paradigm for high-temporal-resolution, low-overhead full-polarimetric sensing.
📝 Abstract
Polarimetry, which is the ability to measure the scattering response of the environment across orthogonal polarizations, is fundamental to enhancing wireless communication and radar system performance. In this paper, we utilize the Zak-OTFS modulation to enable instantaneous polarimetry within a single transmission frame. We transmit a Zak-OTFS carrier waveform and a spread carrier waveform mutually unbiased to it simultaneously over orthogonal polarizations. The mutual unbiasedness of the two waveforms enables the receiver to estimate the full polarimetric response of the scattering environment from a single received frame. Unlike existing methods for instantaneous polarimetry with computational complexity quadratic in the time-bandwidth product, the proposed method enables instantaneous polarimetry at complexity that is only sublinear in the time-bandwidth product. Via numerical simulations, we show ideal polarimetric target detection and parameter estimation results with the proposed method, with improvements in performance and computational complexity over comparable baselines.