2015 Fiscal Year Research-status Report
Multidimensional compressive sensing based technologies for next-generation MIMO radar with SL3: Super-resolution, Low-complexity, Low-cost and Low-consumption
| Project/Area Number |
15K06072
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| Research Institution | Akita Prefectural University |
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Principal Investigator |
徐 粒 秋田県立大学, システム科学技術学部, 教授 (40252324)
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| Co-Investigator(Kenkyū-buntansha) |
桂 冠 秋田県立大学, システム科学技術学部, 特任助教 (80734904) [Withdrawn]
松下 慎也 秋田県立大学, システム科学技術学部, 准教授 (20435449)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Keywords | sub-Nyquist sampling / MIMO comunication system / least mean square (LMS) / sparse signal processing / adaptive filtering / channel identification |
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| Outline of Annual Research Achievements |
As the main task in the FY2015, sub-Nyquist adaptive sampling techniques and adaptive sparse signal identification techniques have been intensively investigated, and the obtained results are briefly summarized as follows:
A compressive sensing (CS) based sub-Nyquist adaptive sampling technique has been developed, by utilizing l_0-NLMS (normalized least mean square) algorithm and DHT (discrete Hartley transform), for random demodulation sampling in the frequency domain. The proposed method has low computational complexity and better robustness to noise than the existing methods, and can be applied to the realization of low-speed ADC based MIMO radar systems.
CS-based low-complexity identification techniques have been proposed by using adaptive sparse algorithms to achieve better performance for large-scale MIMO systems under different circumstances. Specifically, an effective large-scale MIMO channel identification method by using affine combination of sparse adaptive filtering has been developed to achieve low-complexity, and, by utilizing the mixed square/fourth error criterion, improved adaptive sparse signal identification methods, e.g., zero-attracting least mean square/fourth (ZA-LMS/F) algorithm and reweighted ZA-LMS/F (RZA-LMS/F) algorithm, have been established.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The main goals of developing new sub-Nyquist adaptive sampling techniques and adaptive sparse signal identification techniques for FY2015 have been fulfilled smoothly almost as expected and some of the obtained results have already been published as journal/conference papers (see the publication list for the details), which will provide a fundamental and sound base for further exploration of this research.
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| Strategy for Future Research Activity |
In the next stage of this research project, the main goal is, just as scheduled in the detailed research plan, to develop super-resolution large-scale MIMO radar technologies with the SL3 requirements, via establishing advanced beamforming strategies for large-scale MIMO radar transmitter, modeling low-speed ADC based large-scale MIMO radar system, developing super-resolution identification strategies and algorithms, and verifying the proposed techniques by theoretical analysis as well as numerical simulation.
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| Causes of Carryover |
A very little amount (\71) was left just because it was difficult to find an item that exactly cost this amount.
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| Expenditure Plan for Carryover Budget |
This small amount will be used up together with the budget of the next fiscal year.
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Research Products
(18 results)
