Decision fusion in distributed detection and bioinformatics

2021 ◽  
Author(s):  
Yingqin Yuan
Keyword(s):  
Decision Fusion ◽  
Distributed Detection

Blind adaptive decision fusion for distributed detection

2003 ◽  
Vol 39 (1) ◽  
pp. 34-52 ◽  
Author(s):  
G. Mirjalily ◽  
Zhi-Quan Luo ◽  
T.N. Davidson ◽  
E. Bosse
Keyword(s):  
Decision Fusion ◽  
Distributed Detection ◽  
Blind Adaptive

scholarly journals Distributed detection basis on using soft decision scheme both in local sensors and in fusion center

2021 ◽  
pp. 49-56
Author(s):  
V. I. Parfenov ◽  
V. D. Le
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Parameter ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Distributed Detection ◽  
Fusion Center ◽  
Soft Decision ◽  
Signal To Noise ◽  
Decision Scheme ◽  
Noise Ratio

The paper considers distributed detection problem basis on using soft decision scheme both in the local sensors and in the fusion center (FC). The algorithm for making soft decisions when receiving data from local sensors in the fusion center and its performance characteristics, which are necessary for the formation decision fusion rule, are presented. The dependencies of the total error probability on the energy parameter taking into account signal-to-noise ratio at the level of local sensors and the channel’s signal-to-noise ratio are given. The gain of the fusion rule basis on the aggregation of soft decisions in the FC when receiving data about soft local decisions, in efficiency compared to hard fusion rule.

scholarly journals A Soft–Hard Combination Decision Fusion Scheme for a Clustered Distributed Detection System with Multiple Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4370 ◽  
Author(s):  
Junhai Luo ◽  
Xiaoting He
Keyword(s):  
Detection System ◽  
Decision Fusion ◽  
Detection Performance ◽  
Distributed Detection ◽  
Fusion Center ◽  
Soft Decision ◽  
Multiple Sensors ◽  
Fusion Scheme ◽  
Hard Decision ◽  
Centralized System

In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (FC): a one-bit hard decision and a multiple-bit soft decision. Compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft–hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (CH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads’ one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system.

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