陈建峰2
1.西安邮电大学计算机学院 西安 710121
2.西北工业大学航海学院 西安 710072
基金项目:国家自然科学基金-浙江两化融合联合基金(U1609204)
详细信息
作者简介:闫青丽：女，1990年生，讲师，研究方向为信号与信息处理、无线传感器网络数据融合、目标定位与跟踪
陈建峰：男，1972年生，教授，研究方向为阵列信号处理、水下信号处理、声源定位、无线传感器网络信息融合等
通讯作者:闫青丽 gongchyy@163.com
中图分类号:TN911计量
文章访问数:362
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被引次数:0
出版历程
收稿日期:2019-12-24
修回日期:2020-10-21
网络出版日期:2020-10-23
刊出日期:2021-02-23
Sensor Selection Method Based on Multi-objective Optimal Optimization for Mixture Gaussian Noise
Qingli YAN1,,,Jianfeng CHEN2
1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Funds:The National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Information (U1609204)
摘要
摘要:为解决非高斯噪声背景下，基于贝叶斯Fisher信息矩阵和基于互信息的节点选择不一致的问题，该文提出一种基于多目标优化的节点选择方法。推导出节点噪声为混合高斯分布时的贝叶斯Fisher信息矩阵和互信息，将节点个数、选择的节点对应的Fisher信息矩阵和互信息共同作为优化的目标函数。提出利用基于分解的多目标优化方法寻找Pareto最优解，并采用与理想解相似的偏好排序技术(TOPSIS)从所有Pareto最优解中选择最终的节点选择方案。仿真实验结果表明，基于多目标优化的节点选择方法选择的节点具有更优更稳健的定位精度。
关键词:声源定位/
到达方向角/
节点选择/
多目标优化
Abstract:To overcome the flaw that the sensor selection methods based on either of Bayesian Fisher information matrix or mutual information could not provide coincident results, the multiple objective optimal technology is developed for sensor selection by minimizing the number of sensors, maximizing corresponding Bayesian Fisher information matrix and mutual information of the selected sensors. Then, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach is proposed to find the candidate that can better trade off the cost and two performance metrics. Comparison results demonstrate that the proposed method can find a better sensor group, and ultimately, its overall localization performance is more stable and accurate.
Key words:Acoustic source localization/
Angle Of Arrival(AOA)/
Sensor selection/
Multi-objective optimization
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