Echo cancellation method based on convex combination for M-estimaion proportional affine projection

This method multiplies the input vector by a proportional matrix that is positively correlated with the input vector, so that the input signal is assigned to different adaptive filter weight update parameters that are positively correlated with the input vector, thereby accelerating the convergence speed of the algorithm when identifying a sparse system; Through the idea of ​​M estimation, a threshold parameter is set. When the error is smaller than the threshold parameter, the weight vector is updated normally. When the error is greater than the threshold parameter, the threshold parameter replaces the error to update the weight vector, so that the algorithm has a good performance. Anti-impulse noise ability; but this method only considers the improvement of filter weight update parameters, but the update step size of its adaptive filter is fixed, the step size used is large, the convergence speed is fast, and the steady-state error is also large ; If the step size is small, the steady-state error will be small and the convergence speed will be slow; the problem of convergence speed and steady-state error cannot be considered