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Novel beam former a

机译:新型光束形成器

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摘要

Abstract: Estimation of the direction-of-arrival (DOA), also known as direction-finding (DF) problem, has been an active research area for some time. While one DOA estimation method may be better than another depending on the application, these methods can be categorized into either subspace decomposition methods or beamforming methods. Subspace decomposition methods are usually known to provide higher resolution but most of them assume relatively high signal to noise ratio. For low array-signal-to-noise-ratio (ASNR), however, their performance degenerates in a similar way as conventional beam forming methods do. In this paper, we introduce a new method which we refer to as 'MaxMax' method for ASNR below zero. The new method does not depend entirely on either the subspace decomposition technique or the conventional beamforming technique and is attractive for extremely low ASNR environment with small number of sensors at the price of higher computational complexity. Its performance is superior to the others for multipath signals for the same number of sensors. The number of signals need not be known and more than M-1 signals can be resolved where M is the number of sensors. The increased computational complexity can be reduced through parallel processing implemented on massively parallel computers. !21
机译:摘要:到达方向(DOA)的估计,也称为方向寻找(DF)问题,在一段时间以来一直是活跃的研究领域。尽管一种DOA估计方法可能会根据应用程序优于另一种DOA估计方法,但可以将这些方法分为子空间分解方法或波束成形方法。通常已知子空间分解方法可提供更高的分辨率,但是大多数方法都假定信噪比相对较高。但是,对于低阵列信噪比(ASNR),其性能会以与常规波束形成方法相似的方式退化。在本文中,我们介绍了一种新方法,对于ASNR低于零,我们将其称为“ MaxMax”方法。新方法不完全依赖于子空间分解技术或常规波束成形技术,并且以较高的计算复杂度为代价,对于具有少量传感器的极低ASNR环境具有吸引力。对于相同数量的传感器,其性能优于其他多路径信号。信号的数量不需要知道,并且可以解析M-1个以上的信号,其中M是传感器的数量。通过在大规模并行计算机上实现的并行处理,可以减少增加的计算复杂性。 !21

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