期刊
RADIOENGINEERING
卷 27, 期 2, 页码 580-586出版社
SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI
DOI: 10.13164/re.2018.0580
关键词
MIMO radar; Compressive Sensing (CS); tensor decomposition; sparse imaging; greedy algorithm
资金
- National Natural Science Foundation [61571148, 61403091]
- China postdoctoral special funding [2015T80328]
- China Postdoctoral Science Foundation [2014M550182]
- Heilongjiang Natural Science Foundation [QC2015049]
- Heilongjiang Postdoctoral Special Fund [LBH-TZ0410, LBH-Q16065]
- Innovation of Science and Technology Talents in Harbin [2013RFXXJ016]
- Research and development project of application technology in Harbin [2017RAQXJ095]
In MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline. In this paper, the sparsity characteristic and multi-dimension characteristic of signals are considered simultaneously and a new compressive sensing imaging algorithm named tensor-based match pursuit (TMP) is proposed. Firstly, MIMO radar tensor signal model is established to eliminate dimension disaster. Then, exploiting tensor decomposition to process tensor data sets, tensor-based match pursuit is formulated for multi-dimension sparse signal recovery. Simulation results validates that the proposed method can accomplish high-resolution imaging correctly compared with conventional greedy sparse recovery algorithms. Additionally, under fewer snapshots condition, RMSE of proposed method is lower than other sparse recovery algorithms.
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