Identification of Parametric Underspread Linear Systems and Super-Resolution Radar

TL;DR

This paper presents a method for identifying underspread linear systems with time-varying delays and Doppler shifts using a single input, enabling super-resolution radar detection beyond traditional limits.

Contribution

It introduces a polynomial-time algorithm for identifying parametric ULSs from a single pulse train, leveraging sub-Nyquist sampling and frequency recovery techniques.

Findings

Identifiability of parametric ULSs from a single observation with sufficient time bandwidth.

Development of a polynomial-time identification algorithm.

Enhanced super-resolution radar target detection capabilities.

Abstract

Identification of time-varying linear systems, which introduce both time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task in many engineering applications. This paper studies the problem of identification of underspread linear systems (ULSs), whose responses lie within a unit-area region in the delay Doppler space, by probing them with a known input signal. It is shown that sufficiently-underspread parametric linear systems, described by a finite set of delays and Doppler-shifts, are identifiable from a single observation as long as the time bandwidth product of the input signal is proportional to the square of the total number of delay Doppler pairs in the system. In addition, an algorithm is developed that enables identification of parametric ULSs from an input train of pulses in polynomial time by exploiting recent results on sub-Nyquist sampling for time delay estimation and classical results on recovery of frequencies from a sum of complex exponentials. Finally, application of these results to super-resolution target detection using radar is discussed. Specifically, it is shown that the proposed procedure allows to distinguish between multiple targets with very close proximity in the delay Doppler space, resulting in a resolution that substantially exceeds that of standard matched-filtering based techniques without introducing leakage effects inherent in recently proposed compressed sensing-based radar methods.