Discrete Radar based on Modulo Arithmetic

Abstract

Zak-OTFS is modulation scheme where signals are formed in the delay-Doppler (DD) domain, converted to the time domain (DD) for transmission and reception, then returned to the DD domain for processing. We describe how to use the same architecture for radar sensing. The intended delay resolution is $\frac{1}{B}$ where $B$ is the radar bandwidth, and the intended Doppler resolution is $\frac{1}{T}$ where $T$ is the transmission time. We form a radar waveform in the DD domain, illuminate the scattering environment, match filter the return, then correlate with delay and Doppler shifts of the transmitted waveform. This produces an image of the scattering environment, and the radar ambiguity function expresses the blurriness of this image. The possible delay and Doppler shifts generate the continuous Heisenberg-Weyl group which has been widely studied in the theory of radar. We describe how to approach the problem of waveform design, not from the perspective of this continuous group, but from the perspective of a discrete group of delay and Doppler shifts, where the discretization is determined by the intended delay and Doppler resolution of the radar. We describe how to approach the problem of shaping the ambiguity surface through symplectic transformations that normalize our discrete Heisenberg-Weyl group. The complexity of traditional continuous radar signal processing is $\mathcal{O}\big(B^2T^2\big)$. We describe how to reduce this complexity to $\mathcal{O}\big(BT\log T\big)$ by choosing the radar waveform to be a common eigenvector of a maximal commutative subgroup of our discrete Heisenberg-Weyl group. The theory of symplectic transformations also enables defining libraries of optimal radar waveforms with small peak-to-average power ratios.