DocumentCode :
916091
Title :
Dim target detection using high order correlation method
Author :
Liou, Ren-Jean ; Azimi-Sadjadi, Mahmood R.
Author_Institution :
Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA
Volume :
29
Issue :
3
fYear :
1993
fDate :
7/1/1993 12:00:00 AM
Firstpage :
841
Lastpage :
856
Abstract :
This work presents a method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A high-order correlation method which recursively computes the spatio-temporal cross-correlations between data of several consecutive scans is developed. The implementation of this scheme using a connectionist network is presented. Several important properties of the high-order correlation method which indicate that the resultant filtered images capture all the target information are established. The simulation results obtained with this approach show at least 93% clutter rejection. Further improvement in the clutter rejection rate is achieved by modifying the high-order correlation method to incorporate the target motion dynamics. The implementation of this modified high-order correlation using a high-order neural network architecture is demonstrated. The simulation results indicate at least 97% clutter rejection rate for this method. A comparison is also made between the methods developed here and the conventional frequency domain three-dimensional (3-D) filtering scheme, and the simulation results are provided
Keywords :
clutter; correlation methods; digital simulation; image recognition; infrared imaging; interference suppression; neural nets; signal detection; tracking; 3D filtering; IR satellite data; clutter rejection; connectionist network; dim target track detection; frequency domain; high-order correlation; neural networks; simulation; simulation results; spatio-temporal cross-correlations; target motion dynamics; Computational modeling; Correlation; Frequency domain analysis; Information filtering; Information filters; Infrared detectors; Neural networks; Object detection; Satellites; Target tracking;
fLanguage :
English
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9251
Type :
jour
DOI :
10.1109/7.220935
Filename :
220935
Link To Document :
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