NeRF-enabled Analysis-Through-Synthesis for ISAR Imaging of Small Everyday Objects with Sparse and Noisy UWB Radar Data

TL;DR

This paper introduces a NeRF-based Analysis-through-Synthesis framework for high-resolution ISAR imaging of small objects using sparse, noisy UWB radar data, enabling practical and cost-effective imaging in real-world scenarios.

Contribution

The paper presents a novel NeRF-enabled ATS framework that improves ISAR imaging of small objects with sparse, noisy data, reducing reliance on complex setups.

Findings

Outperforms traditional ISAR reconstruction methods in noisy, NLOS scenarios.

Produces high-resolution images with limited views and sparse radar scans.

Effective for complex scenes with multiple targets and structures.

Abstract

Inverse Synthetic Aperture Radar (ISAR) imaging presents a formidable challenge when it comes to small everyday objects due to their limited Radar Cross-Section (RCS) and the inherent resolution constraints of radar systems. Existing ISAR reconstruction methods including backprojection (BP) often require complex setups and controlled environments, rendering them impractical for many real-world noisy scenarios. In this paper, we propose a novel Analysis-through-Synthesis (ATS) framework enabled by Neural Radiance Fields (NeRF) for high-resolution coherent ISAR imaging of small objects using sparse and noisy Ultra-Wideband (UWB) radar data with an inexpensive and portable setup. Our end-to-end framework integrates ultra-wideband radar wave propagation, reflection characteristics, and scene priors, enabling efficient 2D scene reconstruction without the need for costly anechoic chambers or complex measurement test beds. With qualitative and quantitative comparisons, we demonstrate that the proposed method outperforms traditional techniques and generates ISAR images of complex scenes with multiple targets and complex structures in Non-Line-of-Sight (NLOS) and noisy scenarios, particularly with limited number of views and sparse UWB radar scans. This work represents a significant step towards practical, cost-effective ISAR imaging of small everyday objects, with broad implications for robotics and mobile sensing applications.