电光与控制, 2019, 26 (11): 6, 网络出版: 2020-02-24   

传感网时空数据凸优化稳健波形成方法

A Robust Wave Formation Method Based on Convex Optimization About Spatio-Temporal Data in Sensor Network
作者单位
摘要
利用凸优化融合图论建模分析, 构建传感器阵列信号模型, 设计噪声和干扰子空间导向向量校正。采用松弛半正定(SDR)和S-过程凸优化分析, 将非线性非凸问题建模为半定规划(SDP)凸优化问题, 构建空间功率谱匹配的协方差矩阵模型, 得到传感网时空数据凸优化稳健波形成方法。提出抵达时差(TDOA)和频率相位差异(FDOA)多态融合算法, 显著提高多目标跟踪质量, 实现精确定位。仿真实验结果表明, 时空数据凸优化稳健波形成方法具有明显优势, 分辨能力优于传统定位算法。
Abstract
The convex optimization fusion graph theory is used for modeling analysis, the sensor array signal model is constructed, and the noise and interference subspace steering vector correction is designed.The Semidefinite Relaxation(SDR) and S-process convex optimization analysis are used to model the nonlinear non-convex problem as the Semidefinite Programming(SDP)convex optimization problem, and the covariance matrix model of spatial power spectrum matching is constructed.The robust wave formation method based on convex optimization about spatio-temporal data in the sensor network is obtained.The TDOA and FDOA polymorphic fusion algorithm is proposed to significantly improve the quality of multi-target tracking and achieve precise positioning.Through simulation experiments, it is shown that the proposed method has obvious advantages, and the resolution ability is superior to that of traditional positioning algorithms.

. 传感网时空数据凸优化稳健波形成方法[J]. 电光与控制, 2019, 26(11): 6. . A Robust Wave Formation Method Based on Convex Optimization About Spatio-Temporal Data in Sensor Network[J]. Electronics Optics & Control, 2019, 26(11): 6.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!