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首页> 外文期刊>Wireless personal communications: An Internaional Journal >Reduced-Dimensional PARAFAC-Based Algorithm for Joint Angle and Doppler Frequency Estimation in Monostatic MIMO Radar
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Reduced-Dimensional PARAFAC-Based Algorithm for Joint Angle and Doppler Frequency Estimation in Monostatic MIMO Radar

机译：基于降维PARAFAC的单基地MIMO雷达联合角和多普勒频率估计算法

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摘要

In this article, the problem of joint direction of arrival (DOA) and Doppler frequency estimation using parallel factor (PARAFAC) analysis in the monostatic multiple-input multiple-output radar is investigated and a reduced-dimensional PARAFAC (RD-PARAFAC) algorithm is proposed. In order to overcome the shortcoming of the heavy computational load in conventional PARAFAC algorithm, we firstly utilize a RD transformation by employing the property of uniform linear arrays, which can remove the redundant entries of steering matrix. By means of the reduced-dimensional transformation, the proposed algorithm can reduce the computational complexity, save memory capacity significantly and has very close joint DOA and Doppler frequency estimation performance when compared with conventional PARAFAC algorithm. Meanwhile, it outperforms the estimation of signal parameters via rotational invariance technique and the propagator method. Furthermore, our algorithm needs no spectral peak searching or pair matching. The complexity analysis and the Cramer-Rao Bound of the DOA and frequency estimation is derived. Simulations are provided to verify its effectiveness and superiority.
机译：本文研究了单静态多输入多输出雷达的联合到达方向（DOA）和使用并行因子（PARAFAC）分析的多普勒频率估计问题，并提出了降维PARAFAC（RD-PARAFAC）算法建议。为了克服传统PARAFAC算法运算量大的缺点，我们首先利用均匀线性阵列的特性来进行RD变换，从而消除了转向矩阵的冗余项。与传统的PARAFAC算法相比，该算法通过降维变换可以降低计算复杂度，显着节省存储容量，并具有非常紧密的联合DOA和多普勒频率估计性能。同时，它优于通过旋转不变技术和传播器方法对信号参数的估计。此外，我们的算法不需要频谱峰值搜索或配对。推导了DOA的复杂度分析以及Cramer-Rao界和频率估计。提供仿真以验证其有效性和优越性。

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