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

The separation of a signal from noisy vector measurements is obtained by taking advantage of the Middleton Class A model of noise amplitude and the correlation of components of noise processes due to their polarization. The signal is assumed to be a white Gaussian process. Noise is a superposition of M non-Gaussian processes, where each has a fixed azimuth of polarization. Neither the number of processes (M) nor their azimuths are known. The separation of signal from noise is based on the conditional mean estimators. In addition to the optimum estimator that can be derived from a knowledge of the bivariate density functions, two suboptimum solutions are discussed: the circularly symmetric and the azimuth-dependent estimator. Circular symmetry is suitable for the nonpolarized noise vector, whereas the azimuth-dependent estimator is tailored to polarized interference. The azimuth-dependent approach consists of two steps: First, the data vector process is discretized into azimuth sectors, and then, the signal is separated from noise only in those azimuths that are classified as noisy. Statistical model parameters of random processes are estimated using an optimum classification based on the likelihood ratio test (decision-directed method). Iterative whitening methods are also discussed for correlated vector signals. The simulations and the application to the cancellation of polarized noise has proven the effectiveness of the above technique. © 1997 IEEE.