fpga和dsp中的频域声纳处理

P. Graham, B. Nelson
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引用次数: 11

摘要

波束形成是一种对传感器阵列(如天线、麦克风或水听器)接收的数据执行的空间滤波操作。它为系统提供了定向“监听”的能力,即使阵列中的单个传感器是全向的。在过去的一年里,我们一直在探索将基于FPGA的定制计算机器用于几种声纳波束形成应用,包括时域波束形成(P. Graham和B. Nelson, 1998)、频域波束形成和匹配场处理。在许多方面,声纳处理符合W. mangioni - smith和B. Hutchings(1997)为良好的FPGA应用所发现的标准——计算是数据并行的,它们需要很少的控制,数据集很大(无限流),原始传感器数据最多为12位。然而,他们有三个特点,使他们具有挑战性。首先,它们涉及对真实和/或复杂数据的密集算术(乘法累加和三角函数)。其次,它们需要大量的记忆支持,远远超出了之前发表的许多研究成果。第三,计算的规模很大,需要(可能)数百个fpga和高带宽互连来满足实时限制。我们解决第一个问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency-domain sonar processing in FPGAs and DSPs
Beamforming is a spatial filtering operation performed on data received by an array of sensors, such as antennas, microphones, or hydrophones. It provides a system with the ability to "listen" directionally even when the individual sensors in the array are omnidirectional. Over the past year we have been exploring the use of FPGA based custom computing machines for several sonar beamforming applications, including time domain beamforming (P. Graham nd B. Nelson, 1998), frequency domain beamforming, and matched field processing. In many ways sonar processing fits the criteria found by W. Mangione-Smith and B. Hutchings (1997) for good FPGA applications-the computations are data parallel, they require little control, the data sets are large (infinite streams), and the raw sensor data is at most 12 bits. However, they have three characteristics which make them challenging. First, they involve intensive arithmetic (multiply accumulates and trigonometric functions) on real and/or complex data. Second, they require significant memory support, far beyond that indicated in much previously published work. Third, the scale of the computation is large, requiring (possibly) hundreds of FPGAs and high bandwidth interconnections to meet real time constraints. We address the first issue.
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