This paper presents a robust and high-resolution 2D DOA (Direction of Arrival) algorithm in the field of wireless communication such as radar and sonar,solving the problem that IAA (Iterative Adaptive Aproach) fails to provide reliable DOA estimation performance in impulsive noise environment. In the algorithm,we replace WLS ( Weighted Least Squares) with least p order norm (lp-norm) as the cost function. Furthermore,to decrease the complexity of the proposed method,a fast implementation of 2D lp-IAA is also developed based on simplifying the calculation of inverse TBT ( To-eplitz-Block-Toeplitz) matrix and fast Fourier transform. Simulation results indicate that,under the condition of low GSNR ( Generalized Signal-to-Noise Ratio) SαS distributed noise with single snapshot,2D lp-IAA algorithm performs better in dis-tinguishing adjacent multi-target signals than CRCO-MUSIC ( CoRrentropy based COrrelation-MUltiple Signal Classifica-tion) and MUSIC-FLOM (MUltiple Signal Classification-Fractional Lower-Order Moment). Moreover,the average compu-tation time of the fast method is reduced to nearly 1/40 of the lp-IAA algorithm.%本文设计出一种针对脉冲噪声的二维鲁棒高分辨率波达方向( DOA,Direction of Arrival)估计算法,以解决雷达、声纳等无线通信领域中脉冲噪声环境下IAA( Iterative Adaptive Approach)无法准确估计出DOA的问题.该算法中,用最小p阶范数代替WLS(Weighted Least Squares)作为最优化求解的代价函数.此外,根据Toeplitz-Block-To-eplitz(TBT)矩阵性质和FFT简化计算过程,提出该算法的快速实现方法,提高算法的计算效率.该算法在对称α-稳定(SαS,Symmetric Alpha-Stable)分布噪声环境下建模,仿真结果表明:与 CRCO-MUSIC( CoRrentropy based COrrelation-MUltiple Signal Classification)算法和MUSIC-FLOM(MUltiple Signal Classification-Fractional Lower-Order Moment)算法相比,二维lp-IAA算法可以在低信噪比、单快拍条件下有效分辨出相邻多目标信号;快速算法可以在保证高分辨率的前提下,算法平均运算时间降低至原来的约1/40.
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