Distributed Multisensor Fusion With Outlier Clearance Technique for Sensor Networks

Abstract

One challenge faced by sensor fusion strategy designed for sensor networks is to real-time achieve high reliability estimated data with the inaccuracy and fault in raw sensor measurements. In this paper, we present an online outlier clearance technique with low computational complexity and memory usage inspired by the nearest neighbor rule that can identify and remove the spurious data in the distributed multisensor fusion process. The proposed technique is fully localized and thus saves communication overhead as well as has a good extension as deployed nodes increasing. In this technique, we define a weighted average distance-based outlier factor criterion to detect the outlier and replace it with estimated data. With the help of contrast simulations, in which we adopt other two typical techniques, it illustrates that the proposed technique provides better performance in real-time clearing outlier during the sensor fusion process without the prior knowledge of outlier.