研究目的
The problem of joint direction-of-arrival (DOA) and range estimation of near-field multiple sources under α-stable distributed impulsive noise environments is studied.
研究成果
The proposed PM_FLOS_MUSIC algorithm effectively estimates DOA and range for near-field sources in impulsive noise environments by leveraging fractional lower-order statistics and the propagator method, reducing computational complexity and improving performance over second-order statistics-based methods. Simulation results confirm its superiority in suppressing impulsive noise and reducing false peaks, making it suitable for applications like sound source localization and radar.
研究不足
The study is based on simulations and may not account for all real-world complexities. The algorithm's performance is evaluated under specific noise models (α-stable distribution), and its applicability to other noise types or array configurations is not explored. Computational aspects, while improved, might still be significant for large arrays or high-dimensional searches.
1：Experimental Design and Method Selection:
The study uses a propagator method applied to the fractional lower order statistical (FLOS) matrix of array received signals to estimate noise subspace, avoiding eigenvalue decomposition. A 2D MUSIC spectral peak search is employed for DOA and range estimation.
2：Sample Selection and Data Sources:
Simulations are conducted with two independent near-field sources at specific locations (e.g., (20°,
3：2λ) and (40°, 4λ)) using a uniform linear array (ULA) with 15 sensors and inter-sensor spacing of λ/Data is generated under α-stable impulsive noise conditions. List of Experimental Equipment and Materials:
A computer for simulations; no specific physical equipment is mentioned.
4：Experimental Procedures and Operational Workflow:
Numerical experiments are performed with varying parameters such as generalized signal-to-noise ratio (GSNR), characteristic exponent α of noise, and number of snapshots L. Spatial spectra are plotted and compared between the proposed PM_FLOS_MUSIC algorithm and the PM_MUSIC algorithm.
5：Data Analysis Methods:
Performance is evaluated based on the reduction of false peaks in spatial spectra and comparison under different noise conditions and snapshots.
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