In IRST applications, cluttered backgrounds are typically much more intensive than the equivalent sensor noise and intensity of the targets to be detected. This necessitates the development of efficient clutter rejection technology for track initialization and reliable target detection. Experimental study shows that the best existing spatial filtering techniques allow for clutter suppression up to 10 dB, while the desired level (for the reliable detection/tracking) is 25-30 dB or higher. This level of clutter suppression can be achieved only by implementing spatial-temporal rather than spatial filtering. In addition, the clutter rejection algorithm should be supplemented by a jitter compensation technique. Otherwise, due to the blurring effect, temporal filtering cannot be applied effectively. This paper discusses a novel adaptive spatial-temporal technique for clutter rejection. The algorithm is developed on the basis of the application of robust and adaptive methods that are invariant to the prior uncertainty with respect to statistical properties of clutter and adaptive with respect to its variability. The developed clutter rejection technique is based on a multi-parametric approximation of clutter which, after estimation of parameters, leads to an adaptive spatial-temporal filter. The coefficients of the filter are calculated adaptively to guarantee a minimum of empirical mean-square values of the filtering residual noise for every time moment. The adaptive spatial-temporal filtering allows one to suppress any background, regardless of its spatial variation. Simultaneously, the algorithm estimates LOS drift and allows for jitter compensation. Results of simulation show that the algorithm gives a tremendous gain compared to the best existing spatial techniques.