研究目的
To address the high computational complexity and single snapshot problem in direction-of-arrival (DOA) estimation for coprime arrays by proposing a fast iterative interpolated beamforming algorithm.
研究成果
The FIIB algorithm provides a fast and accurate method for DOA estimation in coprime arrays, outperforming Root-MUSIC in computational complexity and estimation accuracy. It enables reliable detection of more sources than sensors and exhibits super-resolution capabilities, making it suitable for practical applications requiring rapid processing.
研究不足
The study is based on simulations and may not account for all real-world conditions such as hardware limitations, environmental factors, or non-Gaussian noise. The algorithm's performance is evaluated for specific coprime configurations and SNR ranges, and further optimization may be needed for other array types or lower SNRs.
1：Experimental Design and Method Selection:
The study employs a simulation-based approach to compare the proposed Fast Iterative Interpolated Beamforming (FIIB) algorithm with the Root-MUSIC algorithm for DOA estimation in coprime arrays. The FIIB algorithm uses an estimate-and-subtract strategy with FFT for coarse estimation and iterative interpolation for refinement.
2：Sample Selection and Data Sources:
Synthetic data is generated for uncorrelated signals impinging on coprime arrays, with noise modeled as zero-mean Gaussian. Scenarios include detecting more sources than sensors, RMSE evaluation, super-resolution capability, and computational complexity analysis.
3：List of Experimental Equipment and Materials:
No physical equipment is used; simulations are conducted using computational tools (implied MATLAB for optimized implementations).
4：Experimental Procedures and Operational Workflow:
For each scenario, 1000 independent simulation runs are performed. The covariance matrix is estimated using snapshots, and algorithms are applied to the virtual signal vector derived from the coprime array structure.
5：Data Analysis Methods:
Performance metrics include spectrum analysis, root mean square error (RMSE) calculation, and execution time measurement normalized to FFT complexity. Results are averaged over runs and compared between FIIB and Root-MUSIC.
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