Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.
Nonstochastic adaptive decision fusion in distributed-detection systems
IEEE Transactions on Aerospace and Electronic Systems ; 38 , 4 ; 1158-1171
2002-10-01
309412 byte
Article (Journal)
Electronic Resource
English
Nonstochastic Adaptive Decision Fusion in Distributed-Detection Systems
Online Contents | 2002
|Blind adaptive decision fusion for distributed detection
IEEE | 2003
|Blind Adaptive Decision Fusion for Distributed Detection
Online Contents | 2003
|Distributed Fault Detection with Correlated Decision Fusion
Online Contents | 2009
|Adaptive Fusion by Reinforcement Learning for Distributed Detection Systems
Online Contents | 1996
|