Detection of point objects in spatially correlated clutter using two dimensional adaptive prediction filtering

The performance of a two-dimensional least-mean-square adaptive filter as a prewhitening filter for the detection of small signals in infrared image data is studied. The spatially broad clutter with long correlation length is seen to be narrowband in the two dimensional frequency domain. This narrowband clutter is predicted and subtracted from the input, leaving the spatially small signal in the residual output. The output energy in the residual and prediction channels of such a filter is seen to depend on the correlation length of the various components in the input signal, thus permitting the separation of short correlation targets from the longer correlation clutter. False alarm improvements and detection gains obtained by using this detection scheme on thermal infrared sensor data with known target points are presented