频控阵雷达研究进展:从系统架构到参数估计

Research Progress on Frequency Diversity Array Radar:from System Framework to Parameter Estimation

频控阵(frequency diversity array,FDA)利用发送端不同阵元间较小的频率偏移量实现目标角度和距离相关的系统模型,为波束赋形、角度与距离联合估计和干扰抑制等奠定基础。该文分析了FDA的研究背景与研究现状;给出了基于FDA的多进多出(multi-input multi-output,MIMO)雷达实现参数估计的原理以及系统模型;提出了展开互质线阵-展开互质频偏架构、虚拟互质面阵-展开互质频偏架构和合成孔径-多互质频偏架构;提出了降维求根多信号分类算法、快速收敛三线性分解算法、级联迭代算法、二级降维MUSIC算法和降维解耦合MUSIC算法;最后实验仿真验证了所提架构和算法的有效性和优越性。

The received signal of frequency diversity array(FDA)is a function of the angle and range of target by using little frequency offsets among array units in transmitter,which can be utilized to conduct beamforming,joint angle and range estimation and interference suppression.In this paper,the research background and progress are presented,and the principle and signal model of parameter estimation utilizing FDA-MIMO radar are introduced.After that,three frequency offset frameworks,including unfolded coprime linear array-unfolded coprime frequency offset(UCLA-UCFO),virtual coprime planar arrayunfolded coprime frequency offset(VCPA-UCFO),and synthetic aperture multi-coprime frequency offset(SA-MCFO)frameworks are proposed.In addition,several new works on algorithms,including reduce dimension root multi-signal classification(RD-root-MUSIC),fast convergence-trilinear decomposition(FC-TD),successive iteration(SUIT),twice reduce dimension-MUSIC(TRD-MUSIC)and reduce dimension MUSIC with decoupling(RDMD)algorithms are also presented.Finally,the effectiveness and superiority of the frameworks and algorithms are verified by simulations.