Efficient robust AMF using the enhanced FRACTA algorithm: results from KASSPER I & II
Shannon D. Blunt - Naval Research Laboratory, Karl Gerlach - Naval Research Laboratory
Wed, 28 April 2004, 9:30 AM - 10:20 AM
This paper presents further developments and results of the FRACTA algorithm which has been shown to be robust to nonhomogeneous environments containing outliers. The main focus here is upon the detection of targets in the KASSPER I challenge data cube which possesses dense clusters of targets and the highly nonhomogeneous KASSPER II data in which severe clutter is present over all ranges and dopplers thereby hindering the identification of a dominant clutter ridge. The KASSPER II dataset is further exacerbated by dense clusters of targets as well as the presence of several deep shadow regions that not only prevent target detection but may also skew covariance matrix estimation. A doppler-dependent thresholding technique is developed which is then incorporated into the FRACTA.E framework and then applied to the KASSPER II dataset. Simulation results are compared with the standard sliding window scheme as well as when clairvoyant knowledge of the covariance matrices is employed. For the KASSPER I data set the standard approach detects only 11 of the 268 total targets while the FRACTA.E algorithm detects 192 which is the same amount achieved when clairvoyant clutter covariance knowledge is used. For a selected CPI of the KASSPER II dataset the standard approach is found to perform poorly with only 14 targets detected out of 127 while the FRACTA.E algorithm with the proposed modification detects 51 targets; 73 targets are found when clairvoyant clutter covariance knowledge is employed.