.png){kind=icon}
返回
Bi-Level l1 Optimization-Based Interference Reduction for Millimeter Wave Radars
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2022.3215636.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the increasing number of radar-equipped vehicles in dense traffic situations, millimeter wave radars are suffering from serious interference problems. Therefore, this article presents a novel bi-level l(1) optimization based approach for reducing interferences between automotive radars for range, velocity and angle measurement. Firstly, sparse difference analysis between the interfering signal and the target signal is investigated. According to the analysis results, one bi-level based signal optimization model is further derived by using l(1)-norm penalized least squares. This bi-level optimization enables a trade-off between suppressing interference and preserving radar targets. Specifically, in the first l(1) level, the interfering signal is first optimized as the desired signal, while the target is considered as noise. Meanwhile, sparse optimization is applied on the target signals in the frequency domain at the second l(1) level. Finally, the iterated soft-thresholding algorithm is used to optimize the proposed model. In real road interference suppression experiments, the proposed method improves the signal to interference plus noise ratio (SINR) for the target from 5.06 dB to 19.26 dB in the range-Doppler domain and from 6.86 dB to 22.10 dB in the azimuth spatial domain.
Keyword:
Bi-level l(1) optimization
millimeter wave radar
interference reduction
iterated soft-thresholding algorithm
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
分享收藏Crystallization kinetics of polyacrylonitrile: single crystal growth rate and thermodynamic considerations.png)
Polymer
PolymerIF0
PREAI
分享收藏Habitat Use and Population Dynamics of Blue Crabs,Callinectes sapidus, in a High-Salinity Embayment
PREAI
分享收藏
查看更多内容