A new type of ensemble Kalman filter for data assimilation is developed, based on fast Fourier transform and wavelet transform. The method can work with minimal computational resources. We develop variants for several general types of observations, give a rigorous proof that the method improves the approximation of the state covariance, and present computational experiments showing that the new technique works reliably with very small ensembles and is stable over multiple analysis cycles.