School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China.
Sensors (Basel). 2019 Mar 21;19(6):1398. doi: 10.3390/s19061398.
In this paper, a novel direction-of-arrival (DOA) estimation for unknown (anonymous) emitter signal (ES) based on time reversal (TR) and coprime array (CA) is proposed. The resolution and accuracy of DOA estimation are enhanced from two aspects: one is from the view of array arrangement: the new distribution of CA is designed to reduce the holes, increase the degree of freedom (DOF) and apertures by rotating and translating only one subarray, which simplifies the operation. The other one is from the view of the algorithm: a neoteric DOA estimation algorithm with noise suppression based on TR, Capon and adaptive neuro-fuzzy inference system (ANFIS) is proposed for solving the wide sidelobe, multipath effect, low resolution and accuracy produced by conventional algorithms, in particular, those cannot work effectively under the existed hole condition. Furthermore, the resubmitting distorted noise and channel noise are suppressed effectively, which is not taken into considered in the conventional Capon algorithm. Simulation results including the resolution, accuracy, root mean square error (RMSE), Cramér-Rao lower bound (CRLB) and the compared analyses on uniform linear array (ULA), nested array (NA) and minimum redundancy array(MRA) demonstrate the performance advantages of the proposed DOA estimation algorithm even at very low signal-to-noise ratio (SNR) condition.
本文提出了一种基于时反(TR)和复共形阵列(CA)的未知(匿名)发射信号(ES)的新到达方向(DOA)估计方法。从两个方面提高了 DOA 估计的分辨率和精度:一是从阵列布置的角度:新的 CA 分布通过仅旋转和移动一个子阵来减少孔、增加自由度(DOF)和孔径,简化了操作。另一个是从算法的角度:提出了一种基于 TR、Capon 和自适应神经模糊推理系统(ANFIS)的具有噪声抑制功能的新型 DOA 估计算法,用于解决传统算法产生的宽旁瓣、多径效应、低分辨率和精度问题,特别是在存在孔的情况下,传统算法无法有效工作。此外,有效抑制了重发失真噪声和通道噪声,而传统的 Capon 算法没有考虑到这一点。仿真结果包括分辨率、精度、均方根误差(RMSE)、克拉美-罗下限(CRLB)以及对均匀线性阵列(ULA)、嵌套阵列(NA)和最小冗余阵列(MRA)的比较分析,证明了即使在非常低的信噪比(SNR)条件下,所提出的 DOA 估计算法也具有性能优势。