[I5] Synthetic Noise
The capability to generate spatially and temporally correlated non-stationary noise is becoming an increasingly important requirement for the assessment of advanced signal processing algorithms in the laboratory, for examination of usefulness of virtual sensor and for factory acceptance tests (FATS) of equipment. In the past, common methods of performing laboratory simulations utilised either measured data and/or spatially uncorrelated Gaussian noise. However, each of these methods have advantages and disadvantages for their use in signal processing schemes. For example, whilst white Gaussian, spatially uncorrelated noise is simple to generate, it suffers from a lack of realism. Measured noise includes realism, but it has other disadvantages such as; the potential for unknown events to occur; the inability to perform many numbers of tests and it is usually expensive to obtain. Therefore, there is a need for an intermediate stage of synthetic noise generation which retains the advantages of uncorrelated noise and real data whilst removing some of the disadvantages. This requirement is becoming more significant since signal processing schemes are becoming increasingly sophisticated and the emphasis is moving away from collecting real data due to cost/risk; sometimes real data is impossible to collect. And synthetic noise is even more important. This proposal is aimed at the requirement to produce synthetic non-stationary spatial/temporal noise.
Project Supervisor
Timothy Clarke, Defence Science and Research Laboratory (Dstl). Tim Clarke has a wealth of experience in acoustics, particularly in the underwater domain, having worked in academia, private industry and government research laboratories. One area of Tim’s expertise is the link and exploitation between the environment and signal processing for acoustic systems.
