The primary goal of this work is to characterize the impact of weighting selection strategy and multistatic geometry on the multistatic radar performance. With the relationship between the multistatic ambiguity functi...The primary goal of this work is to characterize the impact of weighting selection strategy and multistatic geometry on the multistatic radar performance. With the relationship between the multistatic ambiguity function （AF） and the multistatie Cram6r-Rao lower bound （CRLB）, the problem of calculating the multistatic AF and the multistatic CRLB as a performance metric for multistatic radar system is studied. Exactly, based on the proper selection of the system parameters, the multistatic radar performance can be significantly improved. The simulation results illustrate that the multistatic AF and the multistatic CRLB can serve as guidelines for future multistatic fusion rule development and multistatic radars deployment.展开更多
现有无源定位闭式算法均考虑视距(Line of Sight,LOS)环境,无法直接应用于存在遮挡的城市环境低空无人机目标定位等场景,同时,非视距(Non-Line of Sight,NLOS)优化定位算法计算效率较低。针对这些问题,本文开展中继辅助下的单站目标定...现有无源定位闭式算法均考虑视距(Line of Sight,LOS)环境,无法直接应用于存在遮挡的城市环境低空无人机目标定位等场景,同时,非视距(Non-Line of Sight,NLOS)优化定位算法计算效率较低。针对这些问题,本文开展中继辅助下的单站目标定位研究,通过引入中继收发器对目标信号进行转发,构造两条路径从而规避遮挡问题,同时考虑中继和观测站位置存在随机误差,提出了一种闭式算法来确定未知目标位置。该算法分为3个步骤:首先利用校准目标-中继收发器-观测站这一路径的额外信息,修正中继和观测站位置;随后基于未知目标-中继收发器-观测站获取的观测信息,通过引入额外变量的方式构建伪线性方程,利用加权最小二乘技术给出目标位置粗略估计;最后进一步挖掘目标位置与额外变量的非线性关系,再次构建矩阵方程并给出目标位置最终估计解。经过理论剖析与仿真验证,所提出的算法在可接受的测量误差和观测站点位置误差范围内,能够逼近克拉美罗下界(Cramer-Rao Lower Bound,CRLB)。展开更多
分析了R ife算法的性能,指出当信号频率位于离散傅里叶变换(D iscrete Fourier T ransform,DFT)两个相邻量化频率点的中心区域时,R ife算法精度很高,其均方根误差接近克拉美-罗限(C ram er-R ao Low er Bound,CRLB),但当信号频率位于量...分析了R ife算法的性能,指出当信号频率位于离散傅里叶变换(D iscrete Fourier T ransform,DFT)两个相邻量化频率点的中心区域时,R ife算法精度很高,其均方根误差接近克拉美-罗限(C ram er-R ao Low er Bound,CRLB),但当信号频率位于量化频率点附近时,R ife算法精度降低。本文提出了一种修正R ife(M-R ife)算法,通过对信号进行频移,使新信号的频率位于两个相邻量化频率点的中心区域,然后再利用R ife算法进行频率估计。仿真结果表明本算法性能不随被估计信号的频率分布而产生波动,整体性能优于牛顿迭代法(一次迭代),接近二次迭代,在低信噪比条件下不存在发散问题,性能比牛顿迭代稳定。本算法易于硬件实现。展开更多
基金Project(61271441)supported by the National Natural Science Foundation of ChinaProject(NCET-10-0895)supported by the Program for New Century Excellent Talents in Universities of China
文摘The primary goal of this work is to characterize the impact of weighting selection strategy and multistatic geometry on the multistatic radar performance. With the relationship between the multistatic ambiguity function （AF） and the multistatie Cram6r-Rao lower bound （CRLB）, the problem of calculating the multistatic AF and the multistatic CRLB as a performance metric for multistatic radar system is studied. Exactly, based on the proper selection of the system parameters, the multistatic radar performance can be significantly improved. The simulation results illustrate that the multistatic AF and the multistatic CRLB can serve as guidelines for future multistatic fusion rule development and multistatic radars deployment.
文摘现有无源定位闭式算法均考虑视距(Line of Sight,LOS)环境,无法直接应用于存在遮挡的城市环境低空无人机目标定位等场景,同时,非视距(Non-Line of Sight,NLOS)优化定位算法计算效率较低。针对这些问题,本文开展中继辅助下的单站目标定位研究,通过引入中继收发器对目标信号进行转发,构造两条路径从而规避遮挡问题,同时考虑中继和观测站位置存在随机误差,提出了一种闭式算法来确定未知目标位置。该算法分为3个步骤:首先利用校准目标-中继收发器-观测站这一路径的额外信息,修正中继和观测站位置;随后基于未知目标-中继收发器-观测站获取的观测信息,通过引入额外变量的方式构建伪线性方程,利用加权最小二乘技术给出目标位置粗略估计;最后进一步挖掘目标位置与额外变量的非线性关系,再次构建矩阵方程并给出目标位置最终估计解。经过理论剖析与仿真验证,所提出的算法在可接受的测量误差和观测站点位置误差范围内,能够逼近克拉美罗下界(Cramer-Rao Lower Bound,CRLB)。
