Enhanced Capon-MUSIC Integration for improved DOA Estimation with Coprime Arrays in Disaster Management

This paper introduces an enhanced Capon beamforming approach that is further integrated with the Multiple Signal Classification (MUSIC) method to achieve superior Direction of Arrival (DOA) estimation using coprime arrays. The proposed enhancement to the standard Capon beamformer focuses on improving its robustness against steering vector mismatches, which significantly boosts its performance in complex signal environments. By optimizing the beamformer's spatial filtering capability, the improved method mitigates signal distortion and enhances resolution. Building on this enhanced Capon beamformer, this study integrates it with the MUSIC method to leverage the strengths of both approaches. The coprime array configuration allows for an increased number of virtual sensors, enabling higher degrees of freedom and improving the resolution of both coherent and uncorrelated signals. This combined Capon-MUSIC framework provides an efficient solution for accurate DOA estimation, even in scenarios where traditional methods fail. The effectiveness of this hybrid approach is evaluated in disaster management applications, where precise signal localization is crucial for tasks such as emergency communication, search and rescue operations, and resource deployment. The simulation results demonstrate that the integrated method outperforms conventional techniques, delivering improved accuracy, robustness, and computational efficiency, making it ideal for real-world disaster response scenarios.