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.
Highlights
Radar emitter classification is an important subject in electromagnetism surveillance [1,2,3], which is widely used in the airborne electronic support (ES) system [4]
We focus our research on radar emitter classification of the airborne ES system after the radar emitter pulses are corrupted
The high-tech and complicated radars used in civilian and military application increase the difficulty in radar emitter classification
Summary
Radar emitter classification is an important subject in electromagnetism surveillance [1,2,3], which is widely used in the airborne electronic support (ES) system [4] It is a clustering problem which is used to process the sampled pulses radiated from unknown radars. We investigate the radar emitter classification problem on the basis of mixture fusion architecture in the airborne multiple ES systems to achieve appropriate stability and precision. We investigate the radar emitter classification problem and develop an online multisensor data fusion framework. This fusion framework is based on mixture fusion architecture, which makes full use of the sensors in the network to guarantee the efficiency and precision of classification result.
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