Simulation of underwater ambient noise time series
AbstractIn modern naval surveillance, ships and submarines may be tracked via their underwater acoustic signatures. In both active and passive sonar, arrays of hydrophones are used to enhance the signal-to-noise ratio and to obtain directional information. Various techniques exist to process the acoustical signals from arrays in order to maximize the sensitivity in a desired direction while minimizing the contribution of ambient noise. Two array processing techniques currently being explored at Defense Research Establishment Atlantic (DREA) are superdirective and intensity processing. In order to test these concepts for underwater applications, it is necessary to simulate the response of an array to an acoustic signal in the presence of ambient noise. The results obtained from the signal processing algorithms will be affected by the spatial and temporal characteristics of the noise field. Since real acoustic data may be unavailable or the statistics may not be fully known, simulated noise can be used to probe array performance as a function of quantifiable noise characteristics. In this paper we present three different approaches for generating synthetic ambient noise time series data which possess controlled statistical characteristics. The noise statistics which are specified are the probability density function, power spectrum, and the complex cross correlation function between pairs of noise time series. The simulated noise time series represent different types of underwater noise fields.
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