小样本下基于改进的直接数据域方法的DOA估计方法

DOA Estimation Based on the Improved Direct Data Domain Method with One Sample

针对小样本甚至单个样本情况下,大部分高分辨DOA估计方法性能下降,不能对临近目标有效分辨的问题,该文提出一种新的基于直接数据域技术的DOA估计方法,基本思想是通过损失空域自由度达到提高时域样本的目的。首先对阵列进行划分,构造多个新的低维数据矢量,其次利用远场窄带信号的复包络在阵列的各个阵元上的幅度响应是近似相同的特点,构造新的约束。实验表明,在小样本下该文算法DOA的分辨能力优于传统直接数据域方法。

Many of the high-resolution Direction Of Arrival （DOA） estimation methods fail to estimate the DOAs of incoming signals and distinguish the two close signals under small sample support; especially only one snapshot is available. To handle this problem, a novel DOA method based on improved Direct Data Domain （D3） technique is proposed. The basic ideal of the proposed method is to augment the time domain samples by sacrificing the space domain degree. Firstly, by splitting the entire array into many overlapping sub-array, many low-dimension samples can be obtained. Secondly, utilizing the property of far-filed narrowband signal that its amplitude response on each array is the same, a new constraint is imposed. Experimental results indicate that the resolution of the propsed method is superior to the conventional algorithms.