.png){kind=icon}
返回
Direct Position Determination With a Moving Extended Nested Array by Spatial Sparsity
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2023.3312013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Direct position determination (DPD) has received much attention in emitter localization, owing to its better accuracy than conventional two-step positioning. Most of the existing DPD algorithms are developed for circular signals (CS) by using uniform linear arrays (ULAs). However, these algorithms may ignore other characters of the signals, e.g., noncircularity. The use of ULAs limits the accuracy of source localization and the number of sources that can be estimated. In this article, a weighted $\ell_{0}$ -norm sparse reconstruction algorithm for noncircular signals (NCS) is developed for DPD with a designed sparse array in motion. First, a sparse array configuration named extended nested array (ENA) is devised for NCS, which consists of three subarrays. Theoretical analysis proves that the designed array can obtain higher degrees of freedom (DOFs) effectively, and reduce the mutual coupling effects between antennas. Then, a weighted $\ell_{0}$ -norm sparse reconstruction algorithm is developed to improve the accuracy of DPD. Finally, simulation results are provided to demonstrate the superiority of the proposed algorithm with the designed sparse array. Our scheme can provide better localization performance than the state-of-the-art methods.
Keyword:
Direct position determination (DPD)
joint sparse reconstruction
noncircular signal
sparse array
weighted l-norm
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
分享收藏Effect of low oxygen pressure on structural and magnetic properties of quenched SrFe12O19 thin films
PREAI
分享收藏A sparse representation scheme for angle estimation in monostatic MIMO radar.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Direct Position Determination of Non-Circular Sources Based on a Doppler-Extended Aperture With a Moving Coprime ArrayIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容