首页> 外文会议>Conference on Targets and Backgrounds VIII: Characterization and Representation, Apr 1-3, 2002, Orlando, USA >Comparison of the TACOM Acoustic Detection Range Prediction Model and the UK Dstl Acoustic Prediction Propagation Model
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Comparison of the TACOM Acoustic Detection Range Prediction Model and the UK Dstl Acoustic Prediction Propagation Model

机译:TACOM声探测距离预测模型与UK Dstl声预测传播模型的比较

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摘要

Acoustic signatures are being exploited more and more by new technology in the battlefield as a way of detecting and identifying potential targets. An understanding of the way in which the acoustic signature of a land platform propagates through the atmosphere enables one to target suppression techniques to those acoustic sources on the vehicle that will provide the greatest military benefit in terms of reducing the detection range of the platform. Dstl Chertsey (UK) and TACOM (US) have developed acoustic propagation models which can predict the far-field sound pressure levels (SPLs) and associated detection ranges of land platforms under a variety of meteorological conditions over different terrain types. The Acoustic Prediction Propagation Model (APPM, UK) and Acoustic Detection Range Prediction Model (ADRPM, US) have previously been compared and have been found to produce similar results for simple scenarios. With recent developments in both models, this comparison has been carried out again, looking at the introduction of Fast-Field Programs (FFP) to both models and, in more detail, the differences between the results at certain frequencies. This paper represents the results found from this comparison study, showing the differences, similarities and potential of these models for the future.
机译:战场上的新技术正越来越多地利用声学特征,以检测和识别潜在目标。对陆上平台的声学信号在大气中传播的方式的理解使人们能够将抑制技术的目标对准车辆上的那些声源,这将在减小平台的探测范围方面提供最大的军事利益。 Dstl Chertsey(英国)和TACOM(美国)已经开发了声传播模型,可以预测在不同地形类型的各种气象条件下的远场声压级(SPL)和相关的陆地平台探测范围。之前已经对声学预测传播模型(英国APPM)和声学检测范围预测模型(美国ADRPM)进行了比较,发现对于简单场景,它可以产生相似的结果。随着两个模型的最新发展,再次进行了这种比较,着眼于将快速字段程序(FFP)引入两个模型,更详细地讲,在某些频率下结果之间的差异。本文代表了这项比较研究的结果,显示了这些模型在未来的差异,相似性和潜力。

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