Adaptive 4-D IR clutter suppression filtering technique

Conventional infrared (IR) surveillance systems employ clutter rejection filtering techniques operating in a single processing domain (e.g., spatial for scanned arrays, or temporal for staring arrays), providing a poor performance in weak-target scenarios. The adaptive filtering technique proposed here utilizes the information contained in the spatial, temporal, and spectral dimensions to simultaneously implement the functions of frame registration and clutter suppression. In this approach, the background clutter covariance is estimated from data samples obtained via a 4D sliding window. Then, from a priori knowledge of the target-clutter crosscorrelation function, a filter is designed to minimize the clutter variance while preserving the target. Simulation results of the 4D adaptive filtering procedure, using real IR scanned- array sensor data, amply demonstrate the superiority of this algorithm over commonly employed sequential approaches.