Title :
A stochastic geometric approach to sensor array processing
Author :
Ba Ngu Vo ; Ba Tuong Vo
Author_Institution :
Dept. of Electr. & Comput. Eng., Curtin Univ., Bentley, WA, Australia
fDate :
June 29 2014-July 2 2014
Abstract :
A new unified mathematical framework for sensor array processing is proposed. The proposed framework combines Bayesian estimation with stochastic geometry to accommodate prior information, uncertainty in array parameters, and unknown and stochastically time-varying number of nonstationary sources. A system model for a common signal setting is constructed to demonstrate the proposed framework.
Keywords :
array signal processing; geometry; stochastic processes; Bayesian estimation; array parameters; common signal setting; nonstationary sources; prior information; sensor array processing; stochastic geometric approach; stochastically time-varying number; system model; unified mathematical framework; Arrays; Bayes methods; Geometry; Kernel; Signal processing; Stochastic processes; Uncertainty; Bayesian estimation; random sets; sensor array processing;
Conference_Titel :
Statistical Signal Processing (SSP), 2014 IEEE Workshop on
Conference_Location :
Gold Coast, VIC
DOI :
10.1109/SSP.2014.6884619
