Title :
Optimal N-P detection for multisensor system without high-dimensional integral
Author :
Tingting Wang ; Enbin Song
Author_Institution :
Dept. of Math., Sichuan Univ., Chengdu, China
Abstract :
Consider detection problem for multisensor system with Gaussian measurements. According to the NeymanPearson criterion, the detection probability was maximized subject to an upper limit on the probability of false alarm. In this case, it is well known that the optimal test admits the likelihood-ratio test (LRT) form. However the threshold computation of LRT is generally computationally intractable because of the high-dimensional integral caused by the multisensor measurements. In this paper, we equivalently transform the LRT into semidefinite positive quadratic form, whose distribution can be obtained by recursive formula of a series. More importantly, using such technique, the high-dimensional integral computation can be reduced to one dimensional iterative computation. Therefore, our method can greatly reduce computational burden. Numerical examples support the above analysis.
Keywords :
Gaussian processes; distributed sensors; integral equations; iterative methods; matrix decomposition; probability; quadratic programming; sensor fusion; signal detection; statistical testing; 1D iterative computation; Gaussian measurements; LRT threshold computation; NeymanPearson criterion; computational burden reduction; detection probability maximization; false alarm probability; high-dimensional integral computation; likelihood-ratio test form; matrix decomposition; multisensor system; optimal N-P detection problem; semidefinite positive quadratic form; Abstracts; Aerodynamics; Educational institutions; Electronic mail; Multisensor systems; Transforms; Gaussian model; false-alarm probability; matrix decomposition; the probability of detection;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561678