We developed spatiotemporal fusion techniques for improving target detection and automatic target recognition. We also investigated real IR (infrared) sensor clutter noise. The sensor noise was collected by an IR (256 x 256) sensor looking at various scenes (trees, grass, roads, buildings, etc.). More than 95% of the sensor pixels showed near-stationary sensor clutter noise that was uncorrelated between pixels as well as across time frames. However, in a few pixels (covering the grass near the road) the sensor noise showed nonstationary properties (with increasing or decreasing mean across time frames). The natural noise extracted from the IR sensor, as well as the computer-generated noise with Gaussian and Rayleigh distributions, was used to test and compare different spatiotemporal fusion strategies. Finally, we proposed two advanced detection schemes: the double-thresholding the reverse-thresholding techniques. These techniques may be applied to complicated clutter situations (e.g., very-high clutter or nonstationary clutter situations) where the traditional constant-false-alarm-ratio technique may fail.