An Online Multisensor Data Fusion Framework for Radar Emitter Classification

Abstract

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric warfare. The framework is composed of local processing and multisensor fusion processing, from which the rough and precise classification results are obtained, respectively. What is more, the proposed algorithm does not need prior knowledge and training process; it can dynamically update the number of the clusters and the cluster centers when new pulses arrive. At last, the experimental results show that the proposed framework is an efficacious way to solve radar emitter classification problem in networked warfare.

Radar emitters classification and clustering with a scale mixture of normal distributions

“…[11] develop a dynamic clustering algorithm that uses designed distances and dynamic cluster centres and does not require fixing the number of classes which depends on the input data, Zhou et al . [12] also introduce a clustering framework composed of local processing and multi‐sensor fusion processing and use a minimum description length criterion to update dynamically the number of clusters rather than setup in advance.…”