This paper presents some theoretical analysis and applications of adaptive filtering techniques for the detection of dim targets in the presence of highly structured background clutter. The computer algorithms carry out spatial, temporal, and multispectral filtering processes of forward looking infrared (FLIR) images taken at various times and in different spectral bands. These images were obtained from the Air Force TABILS data base. The basic criterion to drive these adaptive processes is based upon the minimum-mean-square error algorithm. The solution to the problem is to find a set of filter coefficients that will achieve the minimum mean square error at the output of the adaptive filter. The resulting Wiener-Hopf equation involves inverting the covariance matrix. Direct inversion of the covariance matrix, however, is a time-consuming process. To relax the computation complexity, a rapidly convergent adaptive algorithm based upon iteratively estimating the inverse of the covariance matrix was developed. The filtering operation consists of updating the inverse matrix in a sample-by-sample manner. This algorithm lends to a simplified hardware structure because it eliminates the matrix inversion process.
C. David Wang,
"Adaptive Spatial/Temporal/Spectral Filters For Background Clutter Suppression And Target Detection," Optical Engineering 21(6), 216033 (1 December 1982). https://doi.org/10.1117/12.7973028