-We formalize clustering as a partitioning problem with a user-defined internal clustering criterion and present SINICC, an unbiased, empirical method for comparing internal clustering criteria. An application to multi-sensor fusion is described, where the data set is composed of inexact sensor "reports" pertaining to "objects" in an environment. Given these reports, the objective is to produce...

ÐWe present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the ...

Sensor data fusion is an important and di cult requirement in marine robotics. This paper examines the role of sensor fusion in three di erent application areas: navigation of autonomous underwater vehicles, acoustic scene reconstruction, and ocean data assimilation. The research issues encountered in these problems include management of uncertainty, modeling of sensor physics, vehicle and envi...

In this paper, Wireless sensor networks place sensors into an area to get data and send them back to a base station. Data fusion, in which collected data are fused before they are sent to the base station, is usually implemented over the network. Since a sensor is typically placed in locations that are accessible to malicious attackers, information assurance of the data fusion process is very i...

A peer-to-peer collaboration framework for multisensor data fusion in resource-rich radar networks is presented. In the multi-sensor data fusion, data needs to be combined in such a manner that the real-time requirement of the sensor application is met. In addition, the desired accuracy in the result of the multi-sensor fusion has to be obtained by selecting a proper set of data from multiple r...

Abstract In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively reg...

This paper proposes a heuristic method for the sensor selection problem that uses a state vector fusion approach as a data fusion method. We explain the heuristic to estimate a stationary target position. Given a first sensor with specified accuracy and by using genetic algorithm, the heuristic selects second sensor such that the fusion of two sensor measurements would yield an optimal estimati...

Sensor fusion has an important role in today’s life, especially in the smart world where devices are becoming smarter. Smart devices require reliable and different types of sensory data, fusing them to obtain better information regarding their objectives. Different types of sensors are often fused to acquire information which cannot be acquired by a single sensor alone. Working with several typ...

This paper proposes a heuristic method for the sensor selection problem that uses a state vector fusion approach as a data fusion method. We explain the heuristic to estimate a stationary target position. Given a first sensor with specified accuracy and by using genetic algorithm, the heuristic selects second sensor such that the fusion of two sensor measurements would yield an optimal estimati...