Phase fraction low moment-based covariance difference propagation algorithm
A fractional low-order, covariance technology, applied in the field of propagation algorithm of covariance difference, can solve the problems of inaccurate classification, large amount of calculation, etc., and achieve the effect of suppressing alpha noise and high parameter estimation accuracy
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment Construction
[0085]下面结合附图对本发明具体实施方式做进一步说明。
[0086]本发明为一种基于相位分数低阶矩的协方差差分的传播算法,算法框图如图1所示,包括以下几个步骤:
[0087]步骤一:根据阵列信号接收矩阵X(t)求取基于相位分数低阶矩的协方差矩阵RPFLOM;
[0088]在图2中,假设有N=2M+1个阵元的对称均匀线阵(ULA),以中心位的阵元为相位参考阵元,阵元间距为d,波长为λ,含有K1个近场信号源和K2个远场信号源,则混合信号源个数为K=K1+K2,信号源的入射方位角为θ,近场源距离为r,远场源距离为∞。则阵列接收远-近场混合信号源定位模型的矢量表示为:
[0089]X(t)=AS(t)+N(t)=AfSf(t)+AnSn(t)+N(t) (25)
[0090]
[0091]
[0092]
[0093]
[0094]其中,X(t)是(2M+1)×1维的快拍数据矢量,Af为(2M+1)×K2维的远场源阵列流行矩阵,An为(2M+1)×K1维的近场源阵列流行矩阵,此时的相位参数γk和φk分别为:
[0095]γk=(-2πdsinθk) / λ (30)
[0096]φk=πd2cos2θk / λrk (31)
[0097]N(t)是(2M+1)×1维的alpha噪声,即满足对称α稳定(Symmetric Alpha Stable,SαS)分布,其特征函数符合如下形式:
[0098]φ(u)=exp{jau-γ|u|α[1+jβsgn(u)ω(u,α)]} (32)
[0099]
[0100]
[0101]其中α为特征指数,取值范围为0<α<2;γ为分布系数,这里取γ=1;β为对称参数,这里取β=0;a为位置参数,这里取a=0。
[0102]通过X(t)求取基于相位分数低阶矩的协方差矩阵RPFLOM,即
[0103]RPFLOM=E{XX},0 [0104]其中p为阶数,且p阶相位分数低阶矩的算子为: [0105] [0106]其中,*表示向量的共轭。 [0107]现实中是通过对接收信号进行离散采样估计,从而得到估计的协方差矩阵RPFLOM的第ij个元素为: [0108] [0109]其中,T是快拍数,xi,xj分别为X(t)的第i行和第j列。 [0110]此时已经抑制了环境中的alpha噪声,因此可以直接对RPFLOM做后续算法处理来估计目标参数。 [0111]步骤二:令距离r→∞,利用...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com