在MIMO-OFDM水声通信系统中,由于信道间的相互干扰和水声信道严重时延扩展产生的频率选择性衰落,系统的通信误码率较高。针对这一问题,研究了空频编码的MIMO-OFDM通信,提出空频迭代信道估计与均衡(Spatial Frequency Iterative Channel ...在MIMO-OFDM水声通信系统中,由于信道间的相互干扰和水声信道严重时延扩展产生的频率选择性衰落,系统的通信误码率较高。针对这一问题,研究了空频编码的MIMO-OFDM通信,提出空频迭代信道估计与均衡(Spatial Frequency Iterative Channel Estimation and Equalization,SFICEE)方法。该方法通过载波间的空频正交性进行各收发阵元对的信道估计,并通过空频均衡获得符号初始估计,迭代更新信道估计,而后通过符号后验软信息反馈进行迭代空频软均衡。仿真结果表明,当误码率为10^(-3)时,文中所提出的SFICEE方法经过二次迭代与STBC方法相比具有4.8 d B的性能增益,相对于SFBC方法有2.8 d B的性能提升。当输入信噪比相同时,文中所提出方法的星座图更加收敛,可以更好地降低水下通信系统的误码率。展开更多
Hydrodynamic coefficients strongly affect the dynamic performance of underactuated unmanned surface vehicle (USV) . Towing tank test is the traditional approach to identify these coefficients,however, the obtained val...Hydrodynamic coefficients strongly affect the dynamic performance of underactuated unmanned surface vehicle (USV) . Towing tank test is the traditional approach to identify these coefficients,however, the obtained values are not completely reliable since experimental difficulties and errors are involved. In this paper,an extended Kalman filter (EKF) method and a least squares (LS) method are proposed,only using onboard sensor data for identification of a small underactuated USV. The vehicle prototype as well as the system integration is delineated. Performance of the identification is evaluated by comparing the estimated coefficients,and the feasibility and accuracy of the proposed approach is demonstrated by simulation.展开更多
Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model...Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.展开更多
文摘在MIMO-OFDM水声通信系统中,由于信道间的相互干扰和水声信道严重时延扩展产生的频率选择性衰落,系统的通信误码率较高。针对这一问题,研究了空频编码的MIMO-OFDM通信,提出空频迭代信道估计与均衡(Spatial Frequency Iterative Channel Estimation and Equalization,SFICEE)方法。该方法通过载波间的空频正交性进行各收发阵元对的信道估计,并通过空频均衡获得符号初始估计,迭代更新信道估计,而后通过符号后验软信息反馈进行迭代空频软均衡。仿真结果表明,当误码率为10^(-3)时,文中所提出的SFICEE方法经过二次迭代与STBC方法相比具有4.8 d B的性能增益,相对于SFBC方法有2.8 d B的性能提升。当输入信噪比相同时,文中所提出方法的星座图更加收敛,可以更好地降低水下通信系统的误码率。
文摘Hydrodynamic coefficients strongly affect the dynamic performance of underactuated unmanned surface vehicle (USV) . Towing tank test is the traditional approach to identify these coefficients,however, the obtained values are not completely reliable since experimental difficulties and errors are involved. In this paper,an extended Kalman filter (EKF) method and a least squares (LS) method are proposed,only using onboard sensor data for identification of a small underactuated USV. The vehicle prototype as well as the system integration is delineated. Performance of the identification is evaluated by comparing the estimated coefficients,and the feasibility and accuracy of the proposed approach is demonstrated by simulation.
基金supported by the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space(KF20202109)the National Natural Science Foundation of China(82004259)the Young Talent Training Project of Guangzhou University of Chinese Medicine(QNYC20190110).
文摘Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.
