Title :
Distributed sensor data fusion with binary decision trees
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, USA
fDate :
9/1/1989 12:00:00 AM
Abstract :
A distributed sensor object recognition scheme that uses object features collected by several sensors is presented. Recognition is performed by a binary decision tree generated from a training set. The scheme does not assume the availability of any probability density functions, thus it is practical for nonparametric object recognition. Simulations have been performed for Gaussian feature objects, and some of the results are presented
Keywords :
computerised pattern recognition; computerised picture processing; digital simulation; Gaussian feature objects; binary decision trees; computerised pattern recognition; computerised picture processing; digital simulation; distributed sensor object recognition; nonparametric object recognition; object features; training set; Computational modeling; Decision trees; Feature extraction; Fusion power generation; Infrared sensors; Object recognition; Probability density function; Sensor fusion; Sensor phenomena and characterization; Testing;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
