A Noise Subspace Projection Method Based on Spatial Transformation Preprocessing
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摘要: 针对基于噪声子空间投影的空间谱合成技术对输入信噪比(SNR)要求较高的问题，该文提出一种基于空间变换预处理的改进噪声子空间投影法。在子阵维度上，对阵列数据进行拆分处理，得到空间-空间-频率3维数据；利用空间投影变换，将其重新投影为空间-频率2维数据，实现子阵维度相干累积，提高空间变换后输入信噪比；采用噪声子空间投影法，对变换后数据进行处理，实现空间谱合成。数值仿真和实测数据处理结果表明：相比噪声子空间投影法，在保持方位分辨率不变的前提下，所提方法对输入信噪比的最低要求降低约6 dB，有效提升了噪声子空间投影法的弱信源检测性能。Abstract: To address the issue of high input Signal-to-Noise Ratio (SNR) in the spatial spectrum synthesis technique based on noise subspace projection, an improved noise subspace projection method based on spatial transformation preprocessing is proposed. First, the receiver array is uniformly split into sub-arrays to form three-dimensional space-space-frequency data. Then, three-dimensional data is projected into two-dimensional space-frequency data by spatial transformation, realizing coherent accumulation in the sub-array dimension and enhancing the input SNR after spatial transformation. Finally, the spatial spectrum synthesis is achieved by processing the two-dimensional transformed data, based on the noise subspace projection method. Numerical simulation and data processing results demonstrate that, compared with the noise subspace projection method, the proposed method decreases effectively the minimum input SNR by 6 dB while maintaining the bearing resolution, enhancing effectively the weak target detection performance of the noise subspace projection method.
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表 1 数值仿真参数设置
参数类型 参数设置 阵型 水平线列阵 阵元数量M 32 阵元间距$ d $ 2 m 采样频率$ {f_s} $ 5 kHz 正横方位 0° 信源方位 $ {\theta _0} $ 中心频率$ {f_0} $ 375 Hz 
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表 3 数据处理参数设置
参数类型 参数设置 阵型 水平线列阵 阵元数量M 64 阵元间距 $ d $ 4 m 采样频率${f_{\rm{s}}}$ 5 kHz 正横方位$ \theta $ 0° 未知信源方位$ {\theta _0} $ $ - 70^\circ , - 50^\circ , - 30^\circ , - 10^\circ ,10^\circ ,30^\circ ,50^\circ ,70^\circ $ 工作频段${f_{\rm{B}}}$ 100～180 Hz
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