HIGH-DYNAMIC DOA ESTIMATION BASED ON WEIGHTED L 1 MINIMIZATION
HIGH-DYNAMIC DOA ESTIMATION BASED ON WEIGHTED L 1 MINIMIZATION
复制标题
基于加权L 1 最小化的高动态DOA估计
DOI：
10.2528/pierc13061410
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发表时间：
2013
影响因子：
--
通讯作者：
R. Wu
中科院分区：
文献类型：
--
作者：
Wenyi Wang;R. Wu
在高动态环境中，由于接收机和发射机之间的快速相对运动，即使在两个连续的快拍之间，信号的到达方向（DOA）也会发生变化。因此，基于协方差的DOA估计算法是无效的。压缩感知算法作为一种新的DOA估计算法，在一次快拍下仍然有效。同时指出，波达方向的变化受相对运动速度和收发距离的限制。本文提出了一种基于加权L1最小化的DOA跟踪算法，该算法利用两个连续快拍之间的DOA变化范围作为先验信息来提高跟踪性能。与其他多快拍压缩感知算法假设多个连续快拍之间的DOA固定不同，该算法考虑了不同快拍之间的DOA变化。仿真结果表明了该算法的优越性。
In high dynamic environment, due to the rapid relative movement between receiver and transmitter, the DOA (Direction of Arrival) of signals will change even between two consecutive snapshots. Thus, covariance-based DOA estimation algorithms are inefiective. Compressive sensing algorithms, as a kind of novel DOA estimation algorithms, are still efiective with only one snapshot. At the same time, it is noted that the DOA changing is limited by relative moving speed and distance between receiver and transmitter. In this paper, a DOA tracking algorithm based on weighted L1 minimization is proposed which utilizing the DOA changing scope between two consecutive snapshots as a prior to improve the tracking performance. Difierent from other multiple snapshots compressive sensing algorithms which assumed flxed DOA among multiple consecutive snapshots, the proposed algorithm takes into account the DOA changing among difierent snapshots. The simulation results demonstrate the advantages of the proposed algorithm.
DOI：
10.1109/29.32276
发表时间：
1989-07-01
期刊：
IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING
影响因子：
--
作者：
ROY, R;KAILATH, T
通讯作者：
KAILATH, T
影响因子：
1.2
作者：
Candes, Emmanuel J.;Wakin, Michael B.;Boyd, Stephen P.
通讯作者：
Boyd, Stephen P.