基于FastICA的DOA估计和幅相误差校正算法

      Direction-of-arrival estimation and sensor array error calibration based on FastICA

      • 摘要: 针对传感器阵列存在幅相误差导致传统超分辨测向算法性能恶化的问题,提出了一种基于快速独立成分分析(fast independent component analysis, FastICA)的波达方向(direction of arrival, DOA)估计和幅相误差校正算法。该算法将幅相误差分解为增益误差与相位误差,并分别进行估计。首先利用协方差矩阵对角元素特征估计增益误差,并借助FastICA算法从阵列时域接收数据中分离出由阵列流形矩阵和幅相误差矩阵组成的混合矩阵,将其投影到信号子空间去除伪信号得到真实导向矢量的估计值;再将快拍数据分别斜投影到各真实导向矢量空间,得到各信号单独入射的快拍数据;最后利用子空间正交性谱搜索得到DOA估计值,并在此基础上估计出相位误差。本文算法在相位误差大的应用场景下,增益误差估计与DOA估计也能保持较好的性能。校正过程无需额外设置辅助信号源,且避免了经典自校正WF(Weiss-Friedlander)方法中出现的次优收敛问题。数值仿真验证了所提算法的有效性。

         

        Abstract: In this paper, a new method of direction of arrival(DOA)estimation and gain-phase error calibration based on FastICA is proposed to address the issue of performance deterioration in traditional high-resolution DOA estimation algorithms caused by gain-phase error. This method decomposes the gain-phase error into gain error and phase error, and estimates them separately. Our method utilizes the characteristics of the diagonal elements of the covariance matrix to estimate the gain error. The mixing matrix, composed of the array manifold matrix and gain-phase error matrix, is extracted from the snapshot samples using the FastICA algorithm. The mixing matrix is then projected onto the signal subspace to eliminate spurious signals and obtain the true steering vector. The algorithm utilizes the true steering vector to construct an oblique projection operator to acquire the single-component received data. The DOA estimation is obtained through subspace orthogonality spectral search, and the phase error is estimated based on it. The algorithm in this paper can maintain good performance in phase error large scenarios for both gain error estimation and DOA estimation. The correction process does not require the setting of additional auxiliary signal sources and avoids the issue of suboptimal convergence that occurs in the Weiss-Friedlander (WF) method. Numerical simulations validate the effectiveness of the proposed method.

         

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