2017 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桂 冠 秋田県立大学, システム科学技術学部, 特任助教 (80734904) [Withdrawn]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 | 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 FY2017 can be briefly summarized as follows:
Stochastic gradient-based adaptive algorithm has been recognised as one of the best algorithms for compressive sensing (CS) due to two obvious advantages: low complexity and robust performance. To further improve the reconstruction accuracy under Gaussian noise, two novel sparse fourth-order error criterion adaptive algorithms, i.e., the l_0-norm normalized least mean fourth (l_0-NLMF) and l_0-norm exponentially forgetting window NLMF (l_0-EFWNLMF) algorithms, have been proposed. In addition, these results have been extended to non-Gaussian noise environment as the sign l_0-NLMF (l_0-SNLMF) algorithm and the sign l_0-EFWNLMF (l_0-EFWSNLMF) algorithm, which can effectively mitigate certain impulsive noises occurring in radar systems.
In order to further improve channel estimation accuracy, a correntropy induced metric (CIM)-penalized RLS (CIM-RLS) based sparse channel estimation algorithm has been proposed, where sparse constraint is performed by CIM function while error constraint term is computed by RLS. In particular, Gaussian kernel is adopted for computing the CIM, and its variable kernel width (VKW) is computed for adaptively exploiting the channel sparsity. Monte Carlo simulation results demonstrate the effectiveness of the proposed algorithm in different scenarios.
A two-dimensional zero-attraction projection (2D-ZAP) algorithm for single snapshot direction of arrival (DOA) estimation has also been proposed, which can achieve exact DOA estimation and reduce the noise interference.
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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 FY2017, and some of the obtained results have been published as journal/conference papers, 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 tasks are to extend the obtained results to the low-speed ADC based large-scale MIMO case, to develop new corresponding techniques including, particularly, the discrete characterization of the low-speed ADC based large-scale MIMO radar system and the related super-resolution identification strategies and algorithms, and to verify the proposed techniques by theoretical analysis as well as numerical simulation.
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