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Aspects of optimum filtering for complex valued random processes are presented. Ordinary linear filters are complemented with conjugate linear filters. It is found that the incorporation of conjugate linear filtering improves signal-to-noise ratio by a factor of two in matched filter receivers. For optimum least squares filtering the inclusion of conjugate processing reduces mean-square error by a factor as great as two; the improvement depends primarily on the degree of correlation between the real and imaginary parts of the signal process. The analysis utilizes special correlation properties of receiver noise. Also, in the absence of phase lock, conjugate linear processing offers no improvement. Finally, it is observed that in the Gaussian case the least squares nonlinear receiver for modulations consists of the derived linear-conjugate linear receiver followed by demodulators comparable to those used in practice.