| 摘要 |
An adaptive signal processing system utilizes a a Multistage Weiner Filter having an analysis section and a synthesis section, which includes a main input channel for receiving a main input signal, an auxiliary input channel for receiving an auxiliary input signal, and a processor. The processor includes an algorithm for generating a data adaptive linear transformation, followed by computing an adaptive weighting wmed of the data, and then applying the computed adaptive weighting wmed to a function of the main input signal and the auxiliary input signal to generate an output signal. The system includes a plurality of building blocks arranged in a Gram-Schmidt cascaded canceller-type configuration for sequentially decorrelating input signals from each other to thereby yield a single filtered output signal. Each building block includes a local main input channel which receives a local main input signal, a local auxiliary input channel which receives a local auxiliary input signal, and a local output channel which sends a local filtered output signal. Each building block generates an adaptive weight wmed that preferably uses just the sample median value of the real part of a complex weight wmed, with the imaginary part set to zero, of the ratio of local main input weight training data to local auxiliary input weight training data. Each building block generates a local output signal utilizing the adaptive weight wmed. The effect of non-Gaussian noise contamination on the convergence MOE of the system is negligible. In addition, when desired signal components are included in weight training data they cause little loss of noise cancellation.
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