| Project/Area Number |
11650381
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報通信工学
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| Research Institution | Osaka University |
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Principal Investigator |
ITO Yoshimichi (2001) Graduate School of Engineering, Osaka University, Assistant Professor, 大学院・工学研究科, 助手 (10263203)
前田 肇 (1999-2000) 大阪大学, 大学院・工学研究科, 教授 (60029535)
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| Co-Investigator(Kenkyū-buntansha) |
IIGUNI Youji Graduate School of Engineering, Osaka University, Associate Professor, 大学院・工学研究科, 助教授 (80168054)
伊藤 義道 大阪大学, 大学院・工学研究科, 助手 (10263203)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2001: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2000: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1999: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | DOA estimation / clustering / database retrival / adaptive quantization / κ-d tree / Root-MUSIC |
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| Research Abstract |
We have presented a multiple DOA estimation method based on a κ-d tree database retrieval technique. It has been shown through several simulation results using two wave sources that the proposed method can estimate DOAs much faster than the Root-MUSIC. An improvement of the estimation accuracy is accomplished by picking data close to the previous estimations out of the retrieved nodes.
We have analyzed the relation between the quantization interval and the estimation accuracy. Based on the obtained relation, we have proposed an adaptive quantization method which can reduce the size of the database without sacrificing the estimation accuracy. We have shown through several computer simulations that the use of the new database can keep the processing time per snapshot to almost constant. This property is appropriate for real-time estimation. We have also developed the clustering technique for improving the estimation accuracy in the case of half-wavelength antenna interval. This clustering method is also adopted to remove the bias in the estimation results. A cluster with the most nodes are chosen and the result of local estimation in this cluster is used as the estimation. Furthermore, the previous estimation results are also considered in estimating the present DOA. We obtain a more stable result although we have to sacrifice the estimation response speed.
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