Sparse reconstruction based direction of arrival estimation of underwater targets

M. R. Devi, N. S. Kumar, Jinu Joseph
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引用次数: 5

Abstract

Compressed sensing is a revolutionary paradigm in data acquisition and processing, having impact in several fields. In the complex environment of ocean medium, where early detection of underwater targets is vital, compressed sensing assumes great significance as it brings down the computational resource requirements by reducing the data rates and hardware.In this paper DOA estimation is attempted with a linear array targeting underwater vessels. Array processing is investigated with compressed sensing based techniques and adaptive beam forming. Sparse reconstruction models are developed in this context for signal recovery. Finally the algorithms are validated on a real data set collected from an ocean experiment.
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基于稀疏重建的水下目标到达方向估计
压缩感知是一种革命性的数据采集和处理范式,在许多领域都有影响。在复杂的海洋介质环境中,对水下目标的早期检测至关重要,压缩感知通过降低数据速率和硬件来降低计算资源需求,具有重要意义。本文尝试用线性阵列对水下舰船进行DOA估计。采用压缩感知技术和自适应波束形成技术对阵列处理进行了研究。在这种情况下,稀疏重建模型被用于信号恢复。最后,在海洋实验数据集上对算法进行了验证。
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Sparse reconstruction based direction of arrival estimation of underwater targets Experimental observation of direction-of-arrival (DOA) estimation algorithms in a tank environment for sonar application Data-model validation of broadband normal mode reverberation model An l 2-norm regularized underwater target classifier with improved generalization capability Real time target recognition using Labview
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