International Conference on Information Fusion 

About: International Conference on Information Fusion is an academic conference. The conference publishes majorly in the area(s): Sensor fusion & Kalman filter. Over the lifetime, 5332 publications have been published by the conference receiving 69107 citations.

Continuous finite-time control for robotic manipulators with terminal sliding modes

Abstract: A continuous finite-time control scheme for rigid robotic manipulators is proposed using a new form of terminal sliding modes. The robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show that faster and high-precision tracking performance is obtained compared with the conventional continuous sliding mode control method.

On performance evaluation of multi-object filters

Abstract: The concept of a miss-distance, or error, between a reference quantity and its estimated/controlled value, plays a fundamental role in any filtering/control problem. Yet there is no satisfactory notion of a miss-distance in the well-established field of multi-object filtering. In this paper, we outline the inconsistencies of existing metrics in the context of multi-object miss-distances for performance evaluation. We then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.

Comparison and evaluation of advanced motion models for vehicle tracking

Abstract: The estimation of a vehiclepsilas dynamic state is one of the most fundamental data fusion tasks for intelligent traffic applications. For that, motion models are applied in order to increase the accuracy and robustness of the estimation. This paper surveys numerous (especially curvilinear) models and compares their performance using a tracking tasks which includes the fusion of GPS and odometry data with an Unscented Kalman Filter. For evaluation purposes, a highly accurate reference trajectory has been recorded using an RTK-supported DGPS receiver. With this ground truth data, the performance of the models is evaluated in different scenarios and driving situations.

Sensor placement for grid coverage under imprecise detections

Abstract: We present a resource-bounded optimization framework for sensor resource management under the constraints of sufficient grid coverage of the sensor field We offer a unique "minimalistic" view of distributed sensor networks in which sensors transmit/report a minimum amount of sensed data The proposed theory is aimed at optimizing the number of sensors and determine their placement to support such minimalistic sensor networks We represent the sensor field as a grid (two- or three-dimensional) of points The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections The proposed algorithm addresses coverage optimization under constraints of imprecise detections and terrain properties The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled Experimental results for an example sensor field with obstacles demonstrate the application of our approach

Data fusion in the transferable belief model

Abstract: When Shafer introduced his theory of evidence based on the use of belief functions, he proposed a rule to combine belief functions induced by distinct pieces of evidence. Since then, theoretical justifications of this so-called Dempster's rule of combination have been produced and the meaning of distinctness has been assessed. The author presents practical applications where the fusion of uncertain data is well achieved by Dempster's rule of combination. It is essential that the meaning of the belief functions used to represent uncertainty be well fixed, as the adequacy of the rule depends strongly on a correct understanding of the context in which they are applied. Missing to distinguish between the upper and lower probabilities theory and the transferable belief model can lead to serious confusion, as Dempster's rule of combination is central in the transferable belief model whereas it hardly fits with the upper and lower probabilities theory.