基于云模型的DSm證據(jù)建模及雷達(dá)輻射源識別方法

郭強(qiáng); 何友

doi:10.11999/JEIT150053

基于云模型的DSm證據(jù)建模及雷達(dá)輻射源識別方法

doi: 10.11999/JEIT150053

郭強(qiáng)¹,
何友¹

基金項(xiàng)目:

國家自然科學(xué)基金(61102166, 61471379)和山東省優(yōu)秀中青年科學(xué)家科研獎(jiǎng)勵(lì)基金(BS2013DX003)

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出版歷程
- 收稿日期: 2015-01-09
- 修回日期: 2015-03-25
- 刊出日期: 2015-08-19

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

Guo Qiang¹,
He You¹

摘要

摘要: 為了提高雷達(dá)輻射源特征參數(shù)存在互相交疊和多個(gè)模式情況的雷達(dá)輻射源正確識別率，該文提出一種基于云模型的DSm(Dezert-Smarandache)證據(jù)建模及雷達(dá)輻射源識別方法。該方法首先將存在互相交疊和多個(gè)模式的先驗(yàn)雷達(dá)輻射源特征參數(shù)進(jìn)行基于云模型的DSm建模，然后將含有噪聲的測量信號特征參數(shù)進(jìn)行基于云模型的DSm隸屬度賦值，再通過隸屬度與基本信度賦值的關(guān)系求得DSm模型的基本信度賦值，最后通過DSmT+PCR5的方法將多傳感器測量信號的同特征的基本信度賦值進(jìn)行融合，再將各特征的融合結(jié)果進(jìn)行DSmT+PCR5融合得到最終的識別結(jié)果，如果僅為單傳感器測量信號的特征參數(shù)，則僅將不同特征參數(shù)的基本信度賦值進(jìn)行DSmT+PCR5得到融合識別結(jié)果。最后通過多種情況下的仿真實(shí)驗(yàn)，驗(yàn)證了該文方法的優(yōu)越性。
- 雷達(dá)輻射源識別 /
- 信息融合 /
- 云模型 /
- 基本信度賦值 /
- Dezert-Smarandache理論
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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