The Conventional Beamforming (CBF) Method has been widely used for underwater target detection. However, the performance and resolution of the CBF are insufficient in low signal-to-noise ratio (SNR) cases. To circumvent such a situation, this article proposes a new short angle weight (SAW) algorithm to improve the performance and resolution for target detection. Compared with the CBF, the proposed approach only uses the main lobe of the azimuth spectrum, while cutting off the side lobes due to redundancy. For obtaining the spatial power distribution and estimating the target azimuth, a weighted dictionary matrix is constructed to deconvolute the azimuth spectrum. Since the resolution of the azimuth spectrum is determined by the rate at which it changes, by properly selecting the size of the support set of its Fourier-transformed function, the resolution of the azimuth spectrum can apparently be enhanced. Moreover, the performance of the proposed SAW method is validated by pool trials and lake trials. The corresponding results show that the proposed algorithm provides much better resolution than other representative methods, at least for all the tested instances.

中文翻译：

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声纳系统中的高分辨率短角权重算法

传统波束形成（CBF）方法已广泛用于水下目标检测。然而，CBF 的性能和分辨率在低信噪比 (SNR) 情况下不足。为了避免这种情况，本文提出了一种新的短角权重（SAW）算法来提高目标检测的性能和分辨率。与CBF相比，所提出的方法仅使用方位谱的主瓣，同时由于冗余而切除了旁瓣。为了获得空间功率分布并估计目标方位角，构造加权字典矩阵来对方位角谱进行解卷积。由于方位谱的分辨率是由其变化的速率决定的，通过适当选择其傅里叶变换函数的支持集大小，可以明显提高方位谱的分辨率。此外，所提出的 SAW 方法的性能通过池试验和湖泊试验进行了验证。相应的结果表明，至少对于所有测试实例，所提出的算法比其他代表性方法提供了更好的分辨率。