| 研究課題/領域番号 |
15K06072
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| 研究機関 | 秋田県立大学 |
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研究代表者 |
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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| 研究分担者 |
桂 冠 秋田県立大学, システム科学技術学部, 特任助教 (80734904) [辞退]
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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| 研究期間 (年度) |
2015-04-01 – 2019-03-31
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| キーワード | compressive sensing / sparse representation / sparse signal processing / adaptive algorithm / MIMO radar system / communication system / DOA estimation |
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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
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理由
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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| 今後の研究の推進方策 |
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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