2018 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桂 冠 秋田県立大学, システム科学技術学部, 特任助教 (80734904) [Withdrawn]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 – 2020-03-31
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| Keywords | compressive sensing / sparse representation / sparse signal processing / adaptive algorithm / MIMO radar system / communication system / DOA estimation |
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| Outline of Annual Research Achievements |
The main results obtained in the FY2018 are briefly summarized as follows:
A 2D data model for pulse Doppler radar system with random demodulation has been established, where the data is under-sampled by a low rate ADC. 2D-CS (i.e., 2D-ZAP,2D-IHT,2D-ISTA and 2D-FISTA) algorithms have been proposed for detecting the sparse targets from the under-sampled data. Since the 2D-CS algorithms solve the 2D data model without vectorizing, the memory requirement and complexity are significantly reduced. Moreover, robust 2D-CS algorithms (2D-RZAP and 2D-RIHT) have been given for non-Gaussian impulsive noise environment.
A new algorithm with a pre-estimation has been proposed for Direction-of-arrival (DOA) estimation with noisy snapshots. First,the range of interest is divided into a relatively low-resolution grid and the conventional beam former is used to quickly identify the candidate areas. Then, the candidate areas are divided into a denser sampling grid, and the l_(2,1)-norm penalty is used to solve the corresponding multiple measurement vectors problem.
A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal.A novel dynamic grouping method has been proposed to classify the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the adaptive block-sparse signal recovery algorithms, the corresponding new algorithms, i.e., the BZA-LMS-D and B10-LMS-D algorithms, have been established, which can achieve better recovery performance.
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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
As stated above, following the research plan, significant progress has been made during the FY2018, and some of the obtained results have been
published as journal/conference papers.
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| Strategy for Future Research Activity |
The next tasks are mainly to refine and summarize the latest results and to prepare/publish the corresponding coference/journal papers as well as to revise the papers under review process.
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| Causes of Carryover |
It is needed in the next FY for
(1) attending international conferences to present the new results obtained in this research project; (2) publishing the corresponding journal papers.
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