Online DOA Estimation for Noninteger Linear Antenna Arrays in Coarray Domain

Yitian Chen, H. Nosrati, E. Aboutanios
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Abstract

We study low complexity direction of arrival (DOA) estimation in noninteger nonuniform antenna arrays with the same number as or more uncorrelated sources than sensors. We employ the maximum entropy (ME) method to solve the matrix completion problem that arises due to having an incomplete set of lags in the coarray. In order to decrease the computational complexity associated with the determinant maximization in the ME completion method, we present a projection-free online convex optimization (OCO) based on the conditional gradient method. We then frame the problem as a sequence of DOA estimation scenarios with varying directions in which a tight bound on total regret minimization is guaranteed by the employed unsupervised learning technique. We evaluate the performance using numerical examples and demonstrate that the proposed method decreases the root mean squared error (RMSE) as the iterations increase. Furthermore, our method approaches the RMSE of the offline method, exhibiting the same saturation behavior as the CRB.
共阵域非整数线性天线阵列的在线DOA估计
研究了非整数非均匀天线阵列中不相关源数与传感器数相同或更多的低复杂度DOA估计问题。我们采用最大熵(ME)方法来解决由于队列中存在不完全滞后集而引起的矩阵补全问题。为了降低ME补全方法中行列式最大化的计算复杂度,提出了一种基于条件梯度法的无投影在线凸优化方法。然后,我们将问题构建为具有不同方向的DOA估计场景序列,其中所采用的无监督学习技术保证了总遗憾最小化的严格界限。通过数值算例对算法的性能进行了评价,结果表明,随着迭代次数的增加,所提出的方法降低了均方根误差(RMSE)。此外,我们的方法接近离线方法的RMSE,表现出与CRB相同的饱和行为。
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