Detection with distributed sensors

Tenney,; Sandell,

doi:10.1109/cdc.1980.271833

Cited by 59 publications

(93 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They studied the case of N sensors, in each the binary decision is performed, and the resulting 2 N nonlinear coupled equations should be solved to obtain the optimum fusion rule. Thus the work reported in [13], [14] were a special case of their work. Apparently, the computational complexity of this process grows exponentially with N .…”

Section: Introductionmentioning

confidence: 87%

“…The first one, tries to design the individual detectors, assuming that the fusion center is given. Some authors have contributed to do such optimization [13], [14]. Tenney and Sandell [13] treated the distributed detection problem with no data fusion from a Bayesian point of view.…”

Section: Introductionmentioning

confidence: 99%

“…Some authors have contributed to do such optimization [13], [14]. Tenney and Sandell [13] treated the distributed detection problem with no data fusion from a Bayesian point of view. Costs were assigned to reflect the various courses of action of each local detector.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian decision for the fusion center of a distributed network in Cognitive Radio

Mosleh

Tadaion

Derakhtian

2009

2009 IEEE International Conference on Signal and Image Processing Applications

View full text Add to dashboard Cite

Cognitive Radio (CR) allows for usage of licensed frequency band by unlicensed Secondary Users (SU)s. However, these SUs need to monitor the spectrum continuously to avoid possible interference with the licensed Primary users (PU)s. In order to avoid causing harmful interference to a PU that is in operation, spectrum sensing is needed for the CRs. One of the great challenges of spectrum sensing is to detect the presence of the PU with little information about the channel and the signal transmitted from the PU. Cooperative spectrum sensing has been shown to greatly increase the probability of detecting the PU. Cooperative spectrum sensing refers to the spectrum sensing methods where local spectrum sensing information from multiple SUs are combined for PU detection. Distributed spectrum sensing methods have the potential to increase the spectral estimation reliability and decrease the probability of interference of CRs to primary communications. This paper considers the performance of a distributed Bayesian detection system consisting of N sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are mutually independent conditioned on both hypotheses. Theoretical analysis on the performance of this fusion center is carried out. We obtain the conditions for the fusion center such that achieves the minimum average cost of making a decision for the overall system is smaller than the local risk of each sensor. The performance of our detector is compared to the traditional AND, OR and Majority decision fusion rules and numerical results show that AND, OR and Majority decision fusion rules are the special cases of the Bayesian fusion rule. In addition, we derive a lower bound for the risk of the fusion center for any given priori probability of hypothesis.

show abstract

Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian decision for the fusion center of a distributed network in Cognitive Radio

Mosleh

Tadaion

Derakhtian

2009

2009 IEEE International Conference on Signal and Image Processing Applications

View full text Add to dashboard Cite

show abstract

“…The above distributed decision problem is isomorphic to the paradigm of 'Distributed Binary Hypothesis Testing' in Signal Detection Theory (Tenney and Sandell, 1981;Chair and Varshney, 1986;Tang et ai., 1993). In our extended formulation, each OM is assigned a different decision problem, whose relationship to the tasks at other OMs is determined by the Figure 2 Three different organizations task graph.…”

Section: Formulation As a Hypothesis Testing Problemmentioning

confidence: 99%

Tasks and Organizations: A Signal Detection Model of Organizational Decision Making

Pete

Kleinman

Pattipati

1993

Intelligent Systems in Accounting, Finance and Management

View full text Add to dashboard Cite

This paper presents a mathematical model to study the joint impact of organizational design and of the task environment on the decision performance of hierarchical organizations with limited internal communication. The problem context is a special class of distributed situation assessment problems, where possible patterns of binary variables are to be classified on the basis of partial and noise‐corrupted information. Structural properties of tasks and organizations are described using a graph formalism, and optimal decision strategies at all decision makers are determined. Organizational expertise is characterized in the form of a Team Relative Operating Characteristic (TROC) curve, thereby replacing the organization by an equivalent single decision maker. Implementing the model, issues of task decomposition and the process of matching organizations with tasks are discussed.

show abstract

“…Data fusion with multiple receivers in digital communication systems can be performed in three manners. In the traditional method, the multiple receivers send all observations directly to the central receiver without any processing [13], [14]. In this case, the individual receivers produce samples with very large number of bits per individual receiver observations.…”

Section: Introductionmentioning

confidence: 99%

A New Soft-Fusion Approach for Multiple-Receiver Wireless Communication Systems

Aziz¹,

El-Bakly²,

Azeem³

et al. 2011

ETRI J

View full text Add to dashboard Cite

In this paper, a new soft‐fusion approach for multiple‐receiver wireless communication systems is proposed. In the proposed approach, each individual receiver provides the central receiver with a confidence level rather than a binary decision. The confidence levels associated with the local receiver are modeled by means of soft‐membership functions. The proposed approach can be applied to wireless digital communication systems, such as amplitude shift keying, frequency shift keying, phase shift keying, multi‐carrier code division multiple access, and multiple inputs multiple outputs sensor networks. The performance of the proposed approach is evaluated and compared to the performance of the optimal diversity, majority voting, optimal partial decision, and selection diversity in case of binary noncoherent frequency shift keying on a Rayleigh faded additive white Gaussian noise channel. It is shown that the proposed approach achieves considerable performance improvement over optimal partial decision, majority voting, and selection diversity. It is also shown that the proposed approach achieves a performance comparable to the optimal diversity scheme.

show abstract

Detection with distributed sensors

Cited by 59 publications

References 0 publications

Bayesian decision for the fusion center of a distributed network in Cognitive Radio

Bayesian decision for the fusion center of a distributed network in Cognitive Radio

Tasks and Organizations: A Signal Detection Model of Organizational Decision Making

A New Soft-Fusion Approach for Multiple-Receiver Wireless Communication Systems

Contact Info

Product

Resources

About