A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks

TL;DR

This paper introduces a new radar network framework that improves the accuracy of reconstructing target scattering centers using wide-angle sensor data.

Contribution

A novel collaborative framework combining EM and EAPO algorithms for non-coherent integration of radar echoes in RSNs.

Findings

The framework achieves adaptive angular segmentation of TSC models using HRRP similarity evaluation.

The proposed method outperforms existing approaches in estimation accuracy and stability.

Experiments on measured datasets confirm robustness and adaptability to complex scattering characteristics.

Abstract

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value.

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