Document Type
Article
Date
5-17-2012
Embargo Period
11-7-2012
Keywords
Multimodal signals, Statistical dependence, Copula theory, Hypothesis testing, Multisensor fusion, Quantization, Stochastic Resonance
Disciplines
Electrical and Computer Engineering
Description/Abstract
In this paper, we consider a binary decentralized detection problem where the local sensor observations are quantized before their transmission to the fusion center. Sensor observations, and hence their quantized versions, may be heterogeneous as well as statistically dependent. A composite binary hypothesis testing problem is formulated, and a copula-based generalized likelihood ratio test (GLRT) based fusion rule is derived given that the local sensors are uniform multi-level quantizers. An alternative computationally efficient fusion rule is also designed which involves injecting a deliberate random disturbance to the local sensor decisions before fusion. Although the introduction of external noise causes a reduction in the received signal to noise ratio, it is shown that the proposed approach can result in a detection performance comparable to the GLRT detector without external noise, especially when the number of quantization levels is large
Recommended Citation
Iyengar, Satish Giridhar; Niu, Ruixin; and Varshney, Pramod, "Fusing Dependent Decisions for Hypothesis Testing with Heterogeneous Sensors" (2012). Electrical Engineering and Computer Science. Paper 232.
http://surface.syr.edu/eecs/232
Source
local input