A Normalized Fractionally Lower-Order Moment Algorithm for Space-Time Adaptive Processing

Y. Rosa Zheng, Missouri University of Science and Technology
Genshe Chen
Erik Blasch

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/806

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A new space-time adaptive processing algorithm is proposed for clutter suppression in phased array radar systems. In contrast to the commonly used normalized least mean square (NLMS) algorithm which uses the second order moments of the data for adaptation, the proposed method uses the lower order moments of the data to adapt the weight coefficients. The normalization is also performed based on the data sample dispersion rather than the variance. Processing results using simulated and measured data show that the proposed algorithm converges faster than the NLMS algorithms in Gaussian and non-Gaussian clutter environments. It also provides better clutter suppression than the NLMS algorithm under heavy-tailed, impulsive, non-Gaussian environments. It in turn improves the target detection performance.