基于l0范数的匹配场源定位方法

南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (4): 675–.

基于l0范数的匹配场源定位方法

王　彪1，2*，蒋亚立1，戴跃伟1

出版日期:2017-08-02 发布日期:2017-08-02
作者简介: 1.江苏科技大学电子信息学院，镇江，212003；
2．中国船舶工业系统工程研究院水声对抗技术重点实验室，北京，100036
基金资助:
基金项目：国家自然科学基金(11574120)，江苏高校高技术船舶协同创新中心/江苏科技大学海洋装备研究院基金(HZ2016010)，东南大学水声信号处理教育部重点实验室开放研究基金(UASP1503)，水声对抗技术重点实验室基金，江苏省“青蓝工程”，江苏省自然科学基金(BK20161359)
收稿日期：2016－09－25
*通讯联系人，E－mail：237873905@qq.com

A matched field source localization method based on sparse structure

Wang Biao1，2*，Jiang Yali1，Dai Yuewei1

Online:2017-08-02 Published:2017-08-02
About author: 1.College of Electronic Information，Jiangsu University of Science and Technology，Zhenjiang，212003，China；
2．Key Laboratory of Underwater Acoustic Warfare Technology，System Engineering Research Institute，Beijing，100036，China

摘要/Abstract

摘要： 传统的匹配场处理方法存在分辨率低、抗噪性能差、不适用低快拍等问题．近年来出现了一类利用匹配场的空间稀疏性，将源定位转化为物理空间的稀疏重构的定位方法，能够实现高精度的匹配场定位．通常求解这些问题时是将l0范数转换为l1范数．虽然该方法能解决常规的NP－hard问题，在优化求解方面具有一定的优势，但是与直接通过l0范数求解的方法相比，不能很好地描述空间稀疏特性，以至于难以充分体现和利用声场冗余字典的稀疏特点．因此，相比于传统的压缩感知算法，通过分析匹配场的空域稀疏特性，在学习平滑l0范数重构算法的基础上，提出了基于平滑l0范数的匹配场源定位方法．在分析了水下目标定位的稀疏数学模型的基础上，逐渐降低数值逼近参数的方式来得到数学模型的最优解，在保证高精度匹配场定位的同时，减少了运算的时间，提高了匹配场定位的效率．

Abstract: Matched field processing techniques have been studied extensively in recent decades，and a lot of detail algorithms have been put forward for practical use.With the rapid development of the theory and algorithms for sparse recovery in finite dimension，compressive sensing(CS)has become an exciting field that has attracted considerable attention in signal processing，such as sound imaging and reconstruction，compressive sensor networks，and so on.We try to locate and match the field sound source by using the compressed sensing method，and find that it can get a better positioning effect than traditional methods.Prior research has established CS as a valuable tool for array signal processing，but it is mainly from a theoretical point of view，and its application to underwater acoustic sources localization has been developed only for very limited scenarios.In this paper，In order to solve the problems of the traditional Matched Field Processing(MFP)method，such as for a long time and which is not suitable for applications with small number of snapshot and array elements，etc.Compared with the traditional compressive sensing algorithm，Matched Field Processing based on smooth l0 norm is proposed from analysis of the characteristics of uniform linear array model and sparse characteristics of matched field，which is combining with smooth l0 norm algorithm.For reconstructing the signal，the method uses combined continuous function as the approximation to smooth l0 norm by sparse matrix processing array manifold，which improves the convergence speed.This method avoids feature decomposition and spectral peak searching process of the traditional algorithm.On the premise of guarantee the estimation precision，it effectively reduces the amount of calculation and the number of the antenna array.At last，by comparing with other source localization methods，such as Bartlett，Minimum variance distortionless response(MVDR)，Basis Pursuit(BP) and smooth l0 norm algorithm，simulation results show the effectiveness of the proposed algorithm.

王　彪1，2*，蒋亚立1，戴跃伟1. 基于l0范数的匹配场源定位方法[J]. 南京大学学报(自然科学版), 2017, 53(4): 675–.

Wang Biao1，2*，Jiang Yali1，Dai Yuewei1. A matched field source localization method based on sparse structure[J]. Journal of Nanjing University(Natural Sciences), 2017, 53(4): 675–.

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