Joint Estimation of Signal Frequency, Azimuth Angle, and Elevation Angle Using Parallel Uniform Linear Arrays

This article explores the methodology for achieving high-precision measurements through joint estimation of signal frequency, azimuth angle, and elevation angle using parallel uniform linear arrays (ULAs). The implementation leverages the MUSIC (Multiple Signal Classification) algorithm, a well-established subspace-based technique that significantly enhances accuracy in signal processing and parameter estimation. The core implementation involves constructing a covariance matrix from received array data and performing eigenvalue decomposition to separate signal and noise subspaces. The MUSIC spatial spectrum is then computed by scanning through potential parameter combinations, with peaks indicating estimated signal parameters. Key implementation aspects include: - Array manifold matrix construction for parallel ULAs - Efficient eigenvalue decomposition using SVD or dedicated matrix functions - Joint parameter search algorithm optimization - Peak detection and parameter resolution techniques We provide detailed explanations of these technical approaches and demonstrate their practical application scenarios, enabling readers to comprehensively understand and effectively implement these advanced signal processing techniques. The methodology is particularly valuable for applications requiring high-resolution direction finding and frequency estimation in array signal processing systems.