High Range Resolution Profiling in Missing Data Case: A New Approach

TL;DR

This paper introduces a new method for high range resolution profiling that effectively handles missing frequency data by estimating autocovariance functions, demonstrating robustness and improved performance over existing methods through simulations and real radar experiments.

Contribution

A novel approach for HRR profiling that accurately estimates autocovariance with missing data, enhancing robustness and performance.

Findings

Robust profiling results despite large data gaps

Superior performance over existing methods in simulations

Validated effectiveness through real radar experiments

Abstract

We have proposed a novel method for Synthetic High Range Resolution (HRR) profiling, under the condition of missing frequency domain samples. This new approach estimates the autocovariance function (ACF) of the signal by valid sample pairs. Autocovariance matrix is formed from ACF estimations. Even with large part of data missing, new approach exhibits robust profiling result. Simulations are presented to show a advantage over other approaches in missing data case. Moreover, a real radar experiment was conducted to validate the new approach.