Title :
Detection of point objects in spatially correlated clutter using two dimensional adaptive prediction filtering
Author :
Soni, T. ; Zeidler, James R. ; Ku, W.H.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
Abstract :
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
Keywords :
adaptive filters; clutter; correlation methods; filtering and prediction theory; image processing; least squares approximations; signal detection; detection gains; infrared image data; least-mean-square adaptive filter; output energy; point objects detection; prewhitening filter; short correlation targets; spatially correlated clutter; thermal infrared sensor data; two dimensional adaptive prediction filtering; Adaptive filters; Adaptive signal detection; Clutter; Data mining; Filtering; Infrared detectors; Infrared sensors; Narrowband; Object detection; Oceans;
Conference_Titel :
Signals, Systems and Computers, 1992. 1992 Conference Record of The Twenty-Sixth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-3160-0
DOI :
10.1109/ACSSC.1992.269153
