Title :
Locally optimum distributed detection of dependent random signals based on ranks
Author_Institution :
Dept. of Comput. Sci. & Electr. Eng., Lehigh Univ., Bethlehem, PA, USA
fDate :
27 Jun-1 Jul 1994
Abstract :
Distributed signal detection schemes based on observations which are dependent from sensor to sensor are studied. Cases where weak random signals are observed in possibly non-Gaussian additive noise are considered. The focus is on cases where the sensor tests are based only on the ranks and signs of the observations. We find analytical forms for the best (locally optimum) sensor test statistics for such cases, and we use these to find the best distributed detection schemes for some cases
Keywords :
array signal processing; noise; random processes; signal detection; signal sampling; statistical analysis; locally optimum distributed detection; noise samples; non-Gaussian additive noise; observations; ranks; sensor dependent random signals; sensor test statistics; sensor tests; signal samples; signs; weak random signals; Additive noise; Computer science; Detectors; Probability density function; Random variables; Sensor fusion; Signal detection; Statistical analysis; Statistical distributions; Testing;
Conference_Titel :
Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on
Conference_Location :
Trondheim
Print_ISBN :
0-7803-2015-8
DOI :
10.1109/ISIT.1994.394993
