A Model and Data Dual-driven Approach for Multitargets Detection under Mainlobe Jamming

TL;DR

This paper introduces a dual-driven approach combining diffusion modeling and sparse Bayesian learning to improve multitarget detection and jamming suppression in radar systems, demonstrating superior performance in structured jamming scenarios.

Contribution

It proposes a novel DMDD framework that models jamming with diffusion processes and targets with sparse Bayesian learning, enhancing detection accuracy and computational efficiency.

Findings

Outperforms existing methods in structured jamming scenarios

Accurately estimates jamming and target parameters simultaneously

Improves detection performance with efficient posterior inference

Abstract

In modern radar systems, target detection and parameter estimation face significant challenges when confronted with mainlobe jamming. This paper presents a Diffusion-based Model and Data Dual-driven (DMDD) approach to estimate and detect multitargets and suppress structured jamming. In DMDD, the jamming prior is modeled through a score-based diffusion process with its score learned from the pure jamming data, enabling posterior sampling without requiring detailed knowledge of jamming. Meanwhile, the target signal is usually sparse in the range space, which can be modeled via a sparse Bayesian learning (SBL) framework, and hyperparameter is updated through the expectation-maximization (EM) algorithm. A single diffusion process is constructed for the jamming, while the state of targets are estimated through direct posterior inference, enhancing computational efficiency. The noise variance is also estimated through EM algorithm. Numerical experiments demonstrate the effectiveness of the proposed method in structured jamming scenarios. The proposed DMDD algorithm achieves superior target detection performance, compared with existing methods.