Simulation of nonstationary multibeam sonar reverberation sequences

ResearchWorks/Manakin Repository

Search ResearchWorks


Advanced Search

Browse

My Account

Statistics

Related Information

Simulation of nonstationary multibeam sonar reverberation sequences

Show full item record

Title: Simulation of nonstationary multibeam sonar reverberation sequences
Author: Luby, James Craig
Abstract: Modern sonar systems employ sophisticated signal processing methods. In order to verify system performance, data of the type expected in actual system operation is required. However, because of the prohibitively high cost of sea experiments, simulated data is often used. New methods of simulating nonstationary, Gaussian, multibeam sonar reverberation sequences are presented.A continuous, first-order scattering model is used to relate sonar, environmental and geometrical factors to the reverberation power spectral density at a set of ranges. Then stationary, multibeam, correlated realizations of the reverberation are generated for each range by one of two methods. The two methods are called the Cholesky factor method and the autoregressive spectral factorization method. Primary emphasis is placed on the autoregressive method.The Cholesky factor method involves factoring the power spectrum matrix at each of a set of frequencies spanning the sonar bandwidth. The resultant Cholesky factors are then used together with a random number generator to synthesize frequency domain realizations. Finally the fast Fourier transform (FFT) is used to recover the time domain realizations from the frequency domain realizations.The autoregressive method is based on the fact that autoregressive models provide an approximate canonical factorization of the reverberation power spectrum. Regardless of the method used the next step is to window, overlap and add the stationary sequences from different ranges together. This yields a time series with time varying spectrum as is appropriate for reverberation. The final step is to apply a range(time) varying scaling to adjust the time varying mean square power of the time series.
Description:
URI: http://hdl.handle.net/1773/15466

Files in this item

Files Size Format View
8501067.pdf 3.461Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record