The problem of channel estimation for multiple an- tenna orthogonal frequency division multiplexing (OFDM) systems subject to unknown carrier frequency offset (CFO) is addressed. Multiple signal classification (M...The problem of channel estimation for multiple an- tenna orthogonal frequency division multiplexing (OFDM) systems subject to unknown carrier frequency offset (CFO) is addressed. Multiple signal classification (MUSIC)-Iike algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional (RD)-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cramer-Rao bound (CRB) of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.展开更多
Root-MUSIC (MUltiple SIgnal Classification) is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the ...Root-MUSIC (MUltiple SIgnal Classification) is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements. By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger exaltation. The particular simulations are given and attest correctness of the theory analysis and also indicate that the proposed algorithm has preferable estimating performance.展开更多
针对传统的多重信号分类(multiple signal classification,简称MUSIC)算法定位声源位置时存在计算量大的问题,提出了一种基于宏微导向的蚁群(ant colony optimization,简称ACO)-MUSIC两级相控声源定位算法。首先,利用ACO估算出声源所在...针对传统的多重信号分类(multiple signal classification,简称MUSIC)算法定位声源位置时存在计算量大的问题,提出了一种基于宏微导向的蚁群(ant colony optimization,简称ACO)-MUSIC两级相控声源定位算法。首先,利用ACO估算出声源所在的宏观位置,再用MUSIC算法精确搜索声源所在的微观方位;其次,对提出的算法进行数值仿真,并搭建实验系统进行验证。仿真和实验结果表明,所提出的算法可以高精度、快速地定位出声源所在的位置;在搜索步距为0.05°时,算法的计算复杂度和计算时间仅为传统MUSIC算法的0.25%和2.8%。展开更多
This paper addresses the issue of the direction of arrival (DOA) estimation under the compressive sampling (CS) framework. A novel approach, modified multiple signal classification (MMUSIC) based on the CS array...This paper addresses the issue of the direction of arrival (DOA) estimation under the compressive sampling (CS) framework. A novel approach, modified multiple signal classification (MMUSIC) based on the CS array (CSA-MMUSIC), is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.展开更多
In this paper,we propose a beam space coversion(BSC)-based approach to achieve a single near-field signal local-ization under uniform circular array(UCA).By employing the centro-symmetric geometry of UCA,we apply BSC ...In this paper,we propose a beam space coversion(BSC)-based approach to achieve a single near-field signal local-ization under uniform circular array(UCA).By employing the centro-symmetric geometry of UCA,we apply BSC to extract the two-dimensional(2-D)angles of near-field signal in the Van-dermonde form,which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal para-meters via rotational invariance techniques(ESPRIT)algorithm.By substituting the calculated 2-D angles into the direction vec-tor of near-field signal,the range parameter can be conse-quently obtained by the 1-D multiple signal classification(MU-SIC)method.Simulations demonstrate that the proposed al-gorithm can achieve a single near-field signal localization,which can provide satisfactory performance and reduce computational complexity.展开更多
针对非均匀地面参数引起的短波测向误差问题,导出了含有地面参数的测向阵列流形模型,给出了水平极化和垂直极化两种圆形阵列流形的具体表达式,并利用多重信号分类(multiple signal classification,MUSIC)方法对地面参数不均匀性导致的...针对非均匀地面参数引起的短波测向误差问题,导出了含有地面参数的测向阵列流形模型,给出了水平极化和垂直极化两种圆形阵列流形的具体表达式,并利用多重信号分类(multiple signal classification,MUSIC)方法对地面参数不均匀性导致的两种阵列测向误差进行了仿真.仿真结果表明:对沿着地面参数不均匀区分界方向的来波信号,方位角测向明显存在较大的测向误差;在低电导率的非均匀地面,地面相对介电常数的变化对方位角测向误差有着显著影响,可达到2°~3°;非均匀地面参数对垂直极化阵列测向误差要比水平极化阵列高出1°~2°.因此在短波固定测向站建设时,尽可能选址高电导率区域,并铺设地网改善天线场地的均匀性,来消除非均匀地面参数变化引起的测向误差.展开更多
针对载频重频联合捷变体制雷达目标参数估计问题,提出了一种新的基于多重信号分类(multiple signal classification,MUSIC)算法的载频重频联合捷变雷达目标参数估计方法。通过信号模型的空时等效,将时域信号的处理等效成空域阵列信号的...针对载频重频联合捷变体制雷达目标参数估计问题,提出了一种新的基于多重信号分类(multiple signal classification,MUSIC)算法的载频重频联合捷变雷达目标参数估计方法。通过信号模型的空时等效,将时域信号的处理等效成空域阵列信号的处理,并将超分辨阵列信号处理方法应用到目标的参数估计中,从而把目标距离和速度的估计等效成阵列中二维参数的估计,解决了由于载频重频联合捷变所带来的目标参数估计难题。仿真实验表明,所提方法能有效实现对目标距离和速度的超分辨估计。展开更多
基金supported by the National Natural Science Foundation of China(6137116961301108+1 种基金61071164)the Fundamental Research Funds for the Central Universities(NS2013024)
文摘The problem of channel estimation for multiple an- tenna orthogonal frequency division multiplexing (OFDM) systems subject to unknown carrier frequency offset (CFO) is addressed. Multiple signal classification (MUSIC)-Iike algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional (RD)-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cramer-Rao bound (CRB) of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.
基金Supported by the National Outstanding Young Foundation (No.60825104)the National Natural Science Foundation of China (No.60736009)
文摘Root-MUSIC (MUltiple SIgnal Classification) is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements. By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger exaltation. The particular simulations are given and attest correctness of the theory analysis and also indicate that the proposed algorithm has preferable estimating performance.
文摘针对传统的多重信号分类(multiple signal classification,简称MUSIC)算法定位声源位置时存在计算量大的问题,提出了一种基于宏微导向的蚁群(ant colony optimization,简称ACO)-MUSIC两级相控声源定位算法。首先,利用ACO估算出声源所在的宏观位置,再用MUSIC算法精确搜索声源所在的微观方位;其次,对提出的算法进行数值仿真,并搭建实验系统进行验证。仿真和实验结果表明,所提出的算法可以高精度、快速地定位出声源所在的位置;在搜索步距为0.05°时,算法的计算复杂度和计算时间仅为传统MUSIC算法的0.25%和2.8%。
基金supported by the National Natural Science Foundation of China(6117119761371045+2 种基金61201307)the Shandong Provincial Natural Science Foundation(ZR2011FM005)the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-aged Scientists(BS2010DX001)
文摘This paper addresses the issue of the direction of arrival (DOA) estimation under the compressive sampling (CS) framework. A novel approach, modified multiple signal classification (MMUSIC) based on the CS array (CSA-MMUSIC), is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.
基金supported by the National Natural Science Foundation of China(6192100162022091)the Natural Science Foundation of Hunan Province(2017JJ3368).
文摘In this paper,we propose a beam space coversion(BSC)-based approach to achieve a single near-field signal local-ization under uniform circular array(UCA).By employing the centro-symmetric geometry of UCA,we apply BSC to extract the two-dimensional(2-D)angles of near-field signal in the Van-dermonde form,which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal para-meters via rotational invariance techniques(ESPRIT)algorithm.By substituting the calculated 2-D angles into the direction vec-tor of near-field signal,the range parameter can be conse-quently obtained by the 1-D multiple signal classification(MU-SIC)method.Simulations demonstrate that the proposed al-gorithm can achieve a single near-field signal localization,which can provide satisfactory performance and reduce computational complexity.
文摘针对非均匀地面参数引起的短波测向误差问题,导出了含有地面参数的测向阵列流形模型,给出了水平极化和垂直极化两种圆形阵列流形的具体表达式,并利用多重信号分类(multiple signal classification,MUSIC)方法对地面参数不均匀性导致的两种阵列测向误差进行了仿真.仿真结果表明:对沿着地面参数不均匀区分界方向的来波信号,方位角测向明显存在较大的测向误差;在低电导率的非均匀地面,地面相对介电常数的变化对方位角测向误差有着显著影响,可达到2°~3°;非均匀地面参数对垂直极化阵列测向误差要比水平极化阵列高出1°~2°.因此在短波固定测向站建设时,尽可能选址高电导率区域,并铺设地网改善天线场地的均匀性,来消除非均匀地面参数变化引起的测向误差.
文摘针对载频重频联合捷变体制雷达目标参数估计问题,提出了一种新的基于多重信号分类(multiple signal classification,MUSIC)算法的载频重频联合捷变雷达目标参数估计方法。通过信号模型的空时等效,将时域信号的处理等效成空域阵列信号的处理,并将超分辨阵列信号处理方法应用到目标的参数估计中,从而把目标距离和速度的估计等效成阵列中二维参数的估计,解决了由于载频重频联合捷变所带来的目标参数估计难题。仿真实验表明,所提方法能有效实现对目标距离和速度的超分辨估计。