ADRPM (Acoustic Detection Range Prediction Model) is a software program that models the propagation of acoustic energy through the atmosphere and evaluates detectability. ADRPM predicts the distance of detection for a noise source based on the acoustic signature of the source. In this paper the assessment of the acoustic signature which characterizes a vehicle is performed by the conventional Boundary Element Analysis (BEA), and by the Energy Boundary Element Analysis (EBEA). BEA is used for computing the radiated noise for the 1/3 octave bands up to 500Hz, and the EBEA is used for the remaining frequency range. By combining the conventional BEA (for low frequency) with the EBEA (for high frequency), it is possible to perform noise radiation computations over the entire frequency range in a seamless manner. Once the initial detection range is predicted, the main contributors to the acoustic detection are identified and their location on the vehicle is modified in order to assess the corresponding effect to the detectability.
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.
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