Strong interference suppression for subspace judgment analysis
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摘要:
针对海洋中存在的强干扰和环境噪声导致水下目标方位估计算法性能剧烈下降的问题, 提出了一种子空间判决分析的强干扰抑制方法(SSJ), 可实现多个强干扰下的目标方位估计。根据常规波束形成粗估的目标角度区间, 利用目标−干扰−噪声子空间与导向矢量的相关性, 设置判决项和估计合适的判决阈值来分离和抑制样本协方差矩阵中的非目标信息, 降低干扰和噪声的输出功率, 同时提高输出信干噪比, 为增强阵列的目标方位分辨能力提供方法支撑。仿真和海试数据处理结果显示, SSJ方法可抑制目标角度区间外的强干扰和噪声, 明显降低了干扰的输出功率和目标主瓣附近的旁瓣级, 提高了目标方位角度的分辨力。相比于现有的子空间干扰抑制方法, 所提方法具有更加稳健的干扰抑制能力。
Abstract:In the presence of strong underwater interferences, the performance of existing target of interest (TOI) bearing estimation algorithms significantly degrades. In this paper, a subspace judgment (SSJ)-based interference suppression method is proposed, which aims to enhance the ability of TOI-bearing estimation under multiple strong interferences. Specifically, with prior knowledge of the TOI bearing interval, the proposed method builds a judgment item exploiting the correlation between the TOI-interference-noise subspace and steering vector. Then the eigenvectors not dominated by the TOI can be accurately identified with a comparison of the aforementioned threshold. Finally, the identified ones will be subtracted from the sample covariance matrix (SCM). As a result, a residual SCM that primarily contains TOI is obtained, which provides methodical support for improving the capability of TOI-bearing resolution. Simulation and experimental results demonstrate that the method effectively suppresses strong interferences outside TOI-bearing intervals. It also reduces the output power of interference and sidelobe levels while improving the capability of TOI-bearing resolution. The proposed method outperforms other state-of-the-art subspace-based interference suppression methods.
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Key words:
- Subspace judgment /
- Judgment item /
- Judgment threshold /
- Interference suppression
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