Multi-channel differencing adaptive noise cancellation with multi-kernel method

Abstract

Although a various of existing techniques are able to improve the performance of detection of the weak interesting signal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingredient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the classical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least-mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously consider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the output signal-to-noise ratio (SNR) of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least-mean-square method using the realistic noise data measured in the lake experiment.