Cancellation of polarized impulsive noise using an azimuth-dependent conditional mean estimator

The separation of signals from noisy vector measurements is obtained by tak ing advantage of the Middleton Class A model of noise amplitude and the cor relation of the components of the noise process due to their polarization. The signal is assumed to be white Gaussian, Noise is a superposition of M n on-Gaussian processes, each with a fixed azimuth of polarization. Neither t he number of processes (M) nor their azimuths are known. The separation of signal from noise is based on the conditional mean estima tors. In addition to the optimum estimator, which can be derived from a kno wledge of the bivariate density functions, two suboptimum solutions for pol arized noise are discussed: the circularly symmetric estimator and the azim uth-dependent one. Circular symmetry is suitable for the nonpolarized noise vector, whereas the azimuth-dependent estimator is tailored to polarized n oise, The azimuth-dependent approach consists of two steps: First, the data vector process is discretized into azimuth sectors, and then, in those cla ssified as noisy, the signal is separated from the noise, Statistical model parameters of random processes are estimated by using the optimum classifi cation, based on the likelihood ratio test (decision-directed method). Iter ative whitening methods are also discussed for correlated vector signals. Numerical examples show the effectiveness of the above technique in canceli ng polarized noise.