Graduate College, Air Force Engineering University, Xi'an 710051, China.
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.
Sensors (Basel). 2023 Jan 12;23(2):906. doi: 10.3390/s23020906.
To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference-sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance.
为了解决差分协阵列 (DCA) 仅在有限程度上增强自由度 (DOFs) 的弱点，提出了一种新的配置，称为通过差分和协阵列的广义嵌套阵列 (GNA-DSCA)，用于到达方向 (DOA) 估计。我们同时考虑了阵列输出的时间和空间信息，以构建 DSCA 模型，在此基础上系统地分析了 GNA 的 DCA 和和协阵列 (SCA)。在确定的扩展因子下，导出了 GNA-DSCA 的自由度的闭式表达式。最优结果表明，GNA-DSCA 比 GNA-DCA 具有更灵活的配置和更多的自由度。此外，较大的扩展因子产生了明显更宽的虚拟孔径，这表明它比报道的基于 DSCA 的稀疏阵更具吸引力。最后，利用基于原子范数最小化 (ANM) 的填充策略来克服由于非均匀虚拟阵而导致的估计性能下降，从而实现准确的 DOA 估计。仿真结果验证了所提出的配置在虚拟阵特性和估计性能方面的优越性。
