Optimal stepsize schedule for a projection-based blind deconvolution algorithm

The present paper illustrates a gradient-update-type projection-based adaptation algorithm over a curved parameter-space (namely, the unit hypershpere) for blind deconvolution application. The deconvolving structure is an FIR adaptive filter whose adaptation rule arises from criterion-function minimization over the smooth parameter-manifold of unit-norm vectors. In particular, the present paper explains an adaptive stepsize theory for the algorithm at hand. The blind deconvolution performances of the algorithm as well as its computational burden are discussed. Also, a numerical comparison with seven blinddeconvolution algorithms known from the scientific literature is illustrated and discussed. Results of numerical tests conducted on a noiseless as well as a noisy channel will confirm that the algorithm discussed in the present paper performs in a satisfactory way.