Identification of Linear Time-Varying Systems Through Waveform Diversity

TL;DR

This paper introduces a resource-efficient method for identifying linear time-varying systems using waveform diversity, enabling accurate parameter estimation from sampled responses, especially in radar applications.

Contribution

It presents a novel approach for system identification that leverages diverse LFM waveforms and parametric estimation techniques for high accuracy.

Findings

Perfect identification in noiseless conditions

High accuracy with noisy measurements using denoising algorithms

Applicable to radar processing with diverse waveforms

Abstract

Linear, time-varying (LTV) systems composed of time shifts, frequency shifts, and complex amplitude scalings are operators that act on continuous finite-energy waveforms. This paper presents a novel, resource-efficient method for identifying the parametric description of such systems, i.e., the time shifts, frequency shifts, and scalings, from the sampled response to linear frequency modulated (LFM) waveforms, with emphasis on the application to radar processing. If the LTV operator is probed with a sufficiently diverse set of LFM waveforms, then the system can be identified with high accuracy. In the case of noiseless measurements, the identification is perfect, while in the case of noisy measurements, the accuracy is inversely proportional to the noise level. The use of parametric estimation techniques with recently proposed denoising algorithms allows the estimation of the parameters with high accuracy.