Abstract
Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers.
Highlights
In the modern world, to make optimum decisions in economics, industry, science, aeronautics, manufacturing, traffic control, and many other military and civilian applications we are extremely dependent on useful and crucial information which is drawn from messages or data via transformation, classification and/or some other processing
A nonlinear aggregation based on a nonlinear integral (NANI) model with respect to a non-additive set function is a powerful way of coping with these kinds of systems
The weighted nonlinear aggregation based on the Choquet integral (NACI) model is successful in describing the interaction among hybrid feature attributes, at the same time, more parameters have to be estimated than in the original NACI model, but for a system with n-dimensional feature attributes, there are 2 n n parameters that must be determined and it is obvious that the amount of parameters increases exponentially with the dimensions of the feature attributes
Summary
To make optimum decisions in economics, industry, science, aeronautics, manufacturing, traffic control, and many other military and civilian applications we are extremely dependent on useful and crucial information which is drawn from messages or data via transformation, classification and/or some other processing. The weighted NACI model is the generalized form of the original one In these two models, the interaction among the feature attributes toward the objective attributes (i.e., outputs) is described as non-additive set functions and is essentially derived from the co-relationship in the statistics. In order to make use of the merits of quick convergence and conquer premature in the traditional PSO, we proposed a QPSO algorithm with elitist crossover mechanism of the GA (named MQPSO) in our previous work [14] and demonstrated a superior performance than the GA in estimations of model parameters. The most significant improvement is that the LTS-MQPSO algorithm combines the concepts of the simulated annealing (SA) and the GA within the QPSO algorithm to achieve global search and overcome prematurity in optimal processes, respectively; the LTS estimator is performed to eliminate the inference from outliers.
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