Mixed near-field and far-field sources localization method using sparse Bayesian learning
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摘要: 针对远、近场混合源定位,提出一种基于稀疏重构理论框架的远、近场混合源分离和定位算法。该算法充分考虑平面波导向矢量和球面波导向矢量的相关特性,利用远、近场声源在阵列上的响应机理的差异,针对远、近场区域分别构造过完备字典,采用多测量矢量模型下的稀疏贝叶斯学习算法重构远近场混合源的空间谱,同时完成远近场混合源的分离和定位。本文算法可以在半波长间距布放的线列阵下对混合源进行定位,适用于高斯和非高斯信号,且无需信源数和噪声功率等先验信息,并具有较高的分辨力和定位精度·计算机仿真结果验证了算法的有效性。Abstract: To localize mixed near-field and far-field sources, this paper develops an algorithm based on sparse reconstruction theory. The proposed method takes full account of the correlation property between plane wave steering vectors and that of spherical waves. By creating over-complete dictionaries for the near-field and far-field areas separately and utilizing sparse Bayesian learning technique, the method reconstructs the space spectrum of the mixed sources successively. The separation and localization of mixed sources are completed at the same time, which can refrain the accumulative error caused by differencing approach of near-field and far-field sources. The proposed algorithm can deal with Gaussian signals and non-Gaussian signals without knowing the number of sources. Computer simulation results validate the effectiveness and the high precision of the proposed algorithm.
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Keywords:
- passive sources localization /
- far-field /
- near-field /
- Sparse Bayesian learning
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期刊类型引用(8)
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