Cascaded adaptive canceler using loaded SMI

A fast-converging, highly parallel/pipeline cascaded canceler which uses th e 2-input loaded sample matrix inversion (SMI) algorithm as the fundamental building block is developed which has convergence performance almost ident ical to one of the standards of a fast-converging adaptive canceler the fas t maximum likelihood (FML) canceler. Furthermore, the new algorithm, denote d as the cascaded loaded SMI (CLSMI), does not require the numerically inte nsive singular value decomposition (SVD) of the input data matrix as does t he FML algorithm, For both the FML and CLSMI developments it is assumed tha t the unknown interference covariance matrix has the structure of an identi ty matrix plus an unknown positive semi-definite Hermitian (PSDH) matrix. T he identity matrix component is associated with the known covariance matrix of the system noise and the unknown PSDH matrix is associated with the ext ernal noise environment. For narrowband (NB) jamming scenarios with J jamme rs it was shown via simulation that the CLSMI and FR IL converge on the ave rage -3 dB below the optimum in about 2J independent sample vectors per sen sor input, Both the CLSMI and FML converge much faster than the standard ca nceler technique, the SMI algorithm.