Fast-Convergent Adaptive Noise Control System

The minimization of noise is of primary importance in optimizing system performance. Active noise cancellation is one of the most promising concepts for achieving noise reduction. This cancellation is generally obtained by means of some type of an adaptive filter. The key component of such a filter is the search algorithm (typically least meansquared) by means of which the filter adapts its coefficients to produce the desired "antinoise". Rapid convergence, combined with stability and realistic system simulation, are the most important performance characteristics for the algorithm. AI Signal Research proposes to develop a new advanced filtering technique for active noise cancellation. The proposed active noise controlled system will utilize our proven high-performance Recursive Least Square (RLS) and fractional calculus adaptive filtering techniques to provide accurate prediction of the desired "antinoise" to achieve noise cancellation through an active secondary source. The ultimate effectiveness of this system will be attributed to its fast convergent ability to optimally adjust its filter characteristic for signal prediction. This high-performance adaptive filtering technology, proven in the SSME program for signal enhancement and noise cancellation for engine diagnostics, has a wide range of industrial application and can be placed into a commercial product to service US industry.