This article introduces a mean frequency estimator based on a radio frequency (RF) domain complex cross-correlation model (C3M). The C3M estimator differs from the real cross-correlation model (CCM) estimator in two respects; it is an unbiased estimator of blood flow velocity and/or tissue motion independent of the bandwidth of the RF ultrasound signals, and it provides an estimate of the spatial bandwidth of the RF-signal. The estimators derived from the complex cross-correlation model (mean spatial frequency, mean temporal frequency, spatial bandwidth and signal-to-noise ratio) are based on three complex cross-correlation coefficients. A full derivation and mathematical description of both estimators (C3M and CCM), starting from a Gaussian model of the complex power spectral density distribution of sampled RF signals, are presented. In addition, a thorough performance evaluation of the C3M estimator in comparison with the CCM estimator is carried out by means of simulations to document the effect of signal-to-noise ratio, bandwidth and sample frequency. In the context of the specific simulation conditions considered, the quality of the C3M estimator is shown to offer the best performance (no bias, low standard deviation of the estimate). Taking into account the computational load and the robustness of the C3M estimator, it may be concluded that the C3M estimator combines high quality and modest complexity.