摘要
针对目前信号数据域直接位置估计方法对分布式信号源进行直接定位存在精度下降问题,该文提出分布式信源数据域直接位置估计方法。首先构建分布式信源直接位置估计模型,然后分别基于最大似然准则和特征结构分解思想给出分布式信源高精度直接位置估计的两种方法分布源最大似然估计方法和广义子空间方法。最后通过多维搜索完成对于分布式信源的直接位置估计。仿真分析表明,该文算法对分布式信源进行直接位置估计的精度较传统直接位置估计算法明显提升,能够在较低信噪比下逼近克拉美罗界;分布源最大似然估计方法在低信噪比下定位精度优于广义子空间方法,而广义子空间方法复杂度更低。
The traditional Direct Position Determination （DPD） methods have localization accuracy decrease when locating distributed sources. DPD methods of the distributed source is proposed in this paper to overcome mentioned above shortcoming. Firstly, a DPD model of the distributed source is constructed. Then two new DPD methods based on maximum likelihood criterion and multiple signal classification are proposed to locate the distributed source-- Maximum Likelihood estimation DPD method of the Distributed source （DML-DPD） and Generalized Subspace DPD method （GS-DPD）. Finally, target position is estimated via multidimensional grid search. The simulations show that the proposed methods have higher localization accuracy than traditional DPD methods when locating the distributed source, and are close to CRLB under the low SNR condition. DML-DPD method has higher localization accuracy than GS-DPD method in the case of low SNR, while GS-DPD method has less computational complexity than DML-DPD method.
出处
《电子与信息学报》
EI
CSCD
北大核心
2018年第2期371-377,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61401513)
国家高技术研究发展计划(2012AA01A502
2012AA01A505)~~
关键词
阵列信号处理
直接定位
分布式信源
Array signal processing
Direct Position Determination （DPD）
Distributed source
