摘要
目标在定位空间中具有稀疏特性,基于该特点提出了一种稀疏重构的时延定位算法;已有的来波到达时间(time-of-arrival,TOA)算法大部分只利用了单次TOA进行估计,其定位结果受噪声影响较大,因此进一步提出对多样本的到达时间进行联合估计,从而提高算法对噪声的稳健性,并提高算法的定位精度。与已有算法相比,所提算法的优点是定位精度更高,对噪声有更强的稳健性。仿真结果验证了所提算法的有效性。
Based on the feature that the targets are sparse in space,a positioning algorithm based on sparse reconstruction is proposed.Most of the existing time-of-arrival(TOA)algorithms use only one sample to estimate targets and they are sensitive to noise.The multi-sample joint estimation algorithm is further proposed to improve the noise robustness and the positioning accuracy of the algorithm.Compared with the existing algorithms,the proposed algorithm has a higher positioning accuracy and stronger noise robustness.Simulation results demonstrate the effectiveness of the proposed algorithm.
作者
胡进峰
谢浩
李朝海
李会勇
谢菊兰
HU Jinfeng;XIE Hao;LI Chaohai;LI Huiyong;XIE Julan(School of Electronic Engineering, University of Electronic Science and Technology of China,Chengdu 611731, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2018年第4期746-750,共5页
Systems Engineering and Electronics
基金
国家自然科学基金(61371184
61671137
61731006)
四川省科技厅项目(2017GZ0344)资助课题
关键词
到达时间
稀疏重构
贝叶斯准则
多样本
time-of-arrival (TOA)
sparse reconstruction
Beyesian inference
multi-sample
