# Distributed Phased-Array Radar Mainlobe Interference Suppression and Cooperative Localization Based on CEEMDAN–WOBSS

## TL;DR

This paper presents a new framework to suppress interference and improve localization in radar systems using advanced signal processing techniques.

## Contribution

The novel SDCAL framework combines CEEMDAN-WOBSS with data fusion for improved antijamming and 3D localization.

## Key findings

- The CEEMDAN-WOBSS method improves signal denoising and separation under low SNR conditions.
- The proposed framework outperforms conventional methods in interference suppression and detection probability.
- Simulation results confirm enhanced localization accuracy in jamming scenarios.

## Abstract

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios.