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引用次数: 2
摘要
本文提出了一种基于并行因子分析(PARAFAC)的双基地多输入多输出雷达在加性脉冲噪声条件下的出发方向和到达方向联合估计方法。由于PARAFAC模型中现有的方法大多是基于Frobenius范数,对离群值敏感,我们利用p -范数测量残差张量,其中1 < p < 2,并将其转化为一个迭代的最小化问题。我们首先用张量结构构造接收到的数据,然后应用一种基于迭代加权最小二乘的替代方法来恢复因子矩阵。最后,提出了标准子空间技术,即MUSIC,用于目标估计。仿真结果表明,该方法在α稳定噪声下的平均角误差优于现有方法。
ℓp-PARAFAC for joint DOD and DOA estimation in bistatic MIMO radar
In this paper, a new method to jointly estimate the direction-of-departures and direction-of-arrivals of bistatic multiple-input multiple-output radar in additive impulsive noise is proposed based on parallel factor analysis (PARAFAC). Since most of the existing method in PARAFAC model are based on the Frobenius norm, which are sensitive to outliers, we utilize the ℓP-norm to measure the residual error tensor, where 1 < p < 2, and transform it to an iterative ℓ2 minimization problem. We first construct the received data with the tensorial structure and then apply an alternative approach based on the iteratively reweighted least squares to recover the factor matrices. In the end, standard subspace techniques, i.e., MUSIC, is proposed for target estimation. Simulation results show that our proposed method outperforms the state-of-the-art methods in terms of mean angular error under α-stable noise.