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

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

Issues of radio electronics ◽

10.21778/2218-5453-2021-3-49-56 ◽

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.

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Performance-traffic-trade-off of two novel hard decision and two soft decision fusion periodogram-based algorithms for cooperative spectrum sensing under unreliable reporting channel

IET Microwaves Antennas & Propagation ◽

10.1049/iet-map.2019.0907 ◽

2020 ◽

Vol 14 (14) ◽

pp. 1683-1695

Author(s):

Lucas dos Santos Costa ◽

Rausley A.A. de Souza

Keyword(s):

Spectrum Sensing ◽

Cooperative Spectrum Sensing ◽

Decision Fusion ◽

Soft Decision ◽

Trade Off ◽

Reporting Channel ◽

Hard Decision

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Two Analog Transmission Schemes for Distributed Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.803 ◽

2014 ◽

Vol 596 ◽

pp. 803-806

Author(s):

Xiang Yang Liu ◽

Pei Sheng Zhu ◽

Wei Liu

Keyword(s):

Channel State Information ◽

Fading Channel ◽

Detection System ◽

Transmission Condition ◽

Detection Performance ◽

Distributed Detection ◽

Transmission Scheme ◽

Channel State ◽

State Information ◽

Analog Transmission

In order to improve the detection performance of distributed detection system under noisy fading channel, two analog transmission schemes, i.e. an analog transmission and a modified analog transmission, were proposed. Simulations show that, when the channel SNR is high, such as larger than 20dB, the system with the proposed analog transmission scheme can approach that with ideal transmission condition in detection performance. For low channel SNR, such as less than 10dB, the modified analog transmission scheme results in better detection performance than the binary transmission scheme proposed in literature. Therefore, according to the channel state information at the sensor, the sensor can select appropriate transmission scheme to improve the system’s detection performance.

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Receiver Diversity for Distributed Detection in Wireless Sensor Networks

International Journal of Wireless Networks and Broadband Technologies ◽

10.4018/ijwnbt.2012010101 ◽

2012 ◽

Vol 2 (1) ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Mohammad A. Al-Jarrah ◽

Mohammad M. Al-Ibrahim

Keyword(s):

Detection System ◽

Block Codes ◽

Distributed Detection ◽

Wireless Sensor ◽

Fusion Center ◽

Receiver Diversity ◽

Space Time Block Codes ◽

Optimal Fusion ◽

Central Device

In this paper, parallel distributed detection in wireless sensor network (WSN) is considered where the sensors process the observations to make local decisions and send these decisions to a central device called fusion center. Receiver diversity technique is proposed here for the distributed detection system in order to enhance the system reliability by improving the detection performance. The fusion center is assumed to be multiple antennas device in order to imply the idea of receiver diversity. Different combining schemes at the fusion center side are used to reduce the fading effects in the case of receiver diversity. Transmitter diversity is also considered in this paper. Cooperative sensors are assumed in order to obtain Alamouti space time block codes. Optimal and sub-optimal fusion rules are derived for each case study. Simulation results show the performance improvement obtained as compared to the conventional distributed detection system in which no diversity is used.

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A Forest Fire Detection System

Intelligent Technologies and Techniques for Pervasive Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4038-2.ch009 ◽

2013 ◽

pp. 179-190

Author(s):

Vassileios Tsetsos ◽

Odysseas Sekkas ◽

Evagellos Zervas

Keyword(s):

Forest Fires ◽

Detection System ◽

Fire Detection ◽

Decision Fusion ◽

Visible Range ◽

Detection Algorithms ◽

Integrated Technology ◽

Detection Systems ◽

Individual Decisions ◽

Fusion Scheme

Forest fires cause immeasurable damages to indispensable resources for human survival, destroy the balance of earth ecology, and worst of all they frequently cost human lives. In recent years, early fire detection systems have emerged to provide monitoring and prevention of the disasterous forest fires. Among them, the Meleager1 system aims to offer one of the most advanced and integrated technology solutions for fire protection worldwide by integrating several innovative features. This chapter outlines one of the major components of the Meleager system, that is the visual fire detection sybsystem. Groundbased visible range PTZ cameras monitor the area of interest, and a low level decision fusion scheme is used to combine individual decisions of numerous fire detection algorithms. Personalized alerts and induced feedback is used to adapt the detection process and improve the overall system performance.

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Inherence of Hard Decision Fusion in Soft Decision Fusion and a Generalized Radix-2 Multistage Decision Fusion Strategy

IEEE Access ◽

10.1109/access.2018.2871092 ◽

2018 ◽

Vol 6 ◽

pp. 55701-55711

Author(s):

Kamlesh Gupta ◽

Shabbir N. Merchant ◽

Uday B. Desai

Keyword(s):

Decision Fusion ◽

Soft Decision ◽

Fusion Strategy ◽

Hard Decision

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A NEW APPROACH FOR MULTI-SYSTEM/SENSOR DECISION FUSION BASED ON JOINT MEASURES

International Journal of Information Acquisition ◽

10.1142/s0219878904000148 ◽

2004 ◽

Vol 01 (02) ◽

pp. 109-120 ◽

Cited By ~ 6

Author(s):

ALI J. RASHIDI ◽

M. HASSAN GHASSEMIAN

Keyword(s):

High Performance ◽

Decision Fusion ◽

Sensor Data ◽

Commission Errors ◽

Multiple Sensors ◽

Spectral Bands ◽

Mathematical Properties ◽

Fusion Methods ◽

Local Results ◽

Fusion Scheme

This article describes the joint measures method as a new powerful method for the development of a high performance multi-sensor data/image fusion scheme at the decision level. The images are received from distributed multiple sensors, which sense the targets in different spectral bands including visible, infrared, thermal and microwave. At first, we study the decision fusion methods, including voting schemes, rank based algorithm, Bayesian inference, and the Dempster-Shafer method. Then, we extract the mathematical properties of multi-sensor local classification results and use them for modeling of the classifier performances by the two new measures, i.e. the plausibility and correctness. Then we establish the plausibility and correctness distribution vectors and matrices for introducing the two improvements of the Dempster-Shafer method, i.e. the DS (CM) and DS (PM) methods. After that we introduce the joint measures decision fusion method based on using these two measures jointly. The Joint Measures Method (JMM) can deal with any decision fusion problem in the case of uncertain local classifiers results as well as clear local classifiers results. Finally, we deploy the new and previous methods for the fusion of the two different sets of multispectral image classification local results and we also compare their reliabilities, the commission errors and the omission errors. The results obviously show that the DS (PM), DS (CM) and JMM methods which use the special properties of the local classifiers and classes, have much better accuracies and reliabilities than other methods. In addition, we show that the reliability of the JMM is at least 3% higher than all other methods.

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