DSm Evidence Modeling and Radar Emitter Fusion Recognition Method Based on Cloud Model

Guo Qiang; He You

doi:10.11999/JEIT150053

Volume 37 Issue 8

Aug. 2015

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Guo Qiang, He You. DSm Evidence Modeling and Radar Emitter Fusion Recognition Method Based on Cloud Model[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1779-1785. doi: 10.11999/JEIT150053

Citation:

Guo Qiang, He You. DSm Evidence Modeling and Radar Emitter Fusion Recognition Method Based on Cloud Model[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1779-1785. doi: 10.11999/JEIT150053

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DSm Evidence Modeling and Radar Emitter Fusion Recognition Method Based on Cloud Model

doi: 10.11999/JEIT150053

Guo Qiang¹,
He You¹

Received Date: 2015-01-09
Rev Recd Date: 2015-03-25
Publish Date: 2015-08-19

Abstract

Abstract

To improve the correct radar emitter recognition rate in cases that radar emitter characteristic parameters are overlapped with each other and existence of multiple modes, a DSm (Dezert-Smarandache) evidence modeling and radar emitter fusion recognition method based on cloud model is proposed. First, the radar emitter characteristic parameters which are overlapped and have multiple modes are modeled in DSm frame based on cloud model, then the degree of membership of unkonwn radar emitter signal belonging to prior radar types of each characteristic parameter is obtained by this model. Second, the basic belief assignments in DSm frame based on cloud model are obtained by the relationship between degree of membership and basic belief assignments. Thirdly, the basic belief assignments of the same characteristic parameters of multi-source unkown emitter signal are fused by DSmT+PCR5, then the fusion results of each characteristic parameters are fused to get the final recognition results. If there are only single-source unknown signal characteristic parameters, the basic belief assignments of each characteristic parameter are fused by DSmT+PCR5 to get the final recognition results. Finally, through the simulation experiments in multiple conditions, the superiority of the proposed method is testified well.
- Radar emitter recognition,
- Information fusion,
- Cloud model,
- Basic belief assignments,
- Dezert- Smarandache theory

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References(30)

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