调频连续波雷达因体积小、低复杂度、易实现等优点，近年来被应用到汽车、监视、反无人机、医疗等多个领域。但在实际应用中，其信号处理平台多基于快速傅里叶变换来实现目标参数检测，不能满足对雷达目标进行超分辨率多维参数联合估计的需求。本项目以实用化为向导，拟对基于调频连续波雷达的超分辨多维参数联合估计方法展开研究。首先，从调频连续波雷达信号模型入手，分型并改进现存典型的信号模型，进而提出多种利用调频连续波雷达对多目标进行多维参数联合估计的方法。其次，针对相干源、杂波干扰、算法计算量大等问题，探索解相干、抑制干扰、降低算法复杂度的方法，优化所提出的超分辨多维参数联合估计方法。最后，根据超分辨率算法的统计特性，分析所提出多维参数联合估计算法的理论性能，并推导相应算法的估计误差解析表达式和克拉美-罗下界。本项目研究结果将进一步丰富超分辨多维参数联合估计理论，推进超分辨率调频连续波雷达技术实用化的进程。

The frequency-modulated continuous waveform (FMCW) radar is with small volume, low complexity and easy to manufacture, and have recently become available for automotive, surveillance, anti-drone, and medical applications. However, the existing FMCW radar signal processing platform generally estimate the parameters of targets through the Fast Fourier Transform (FFT) technique, which cannot satisfy the demands of super-resolution multi-dimensional parameter joint estimation. This research project focuses on developing the super-resolution multi-dimensional parameter estimation algorithms for FMCW radar targets, with high efficiency and low complexity. The first focus of this project is the analysis and modification of the narrow band signal model of FMCW radar for the multi-dimensional parameter joint estimation, and developing new approaches for different schemes of multi-dimensional parameter estimation. The further research interest of this project is the optimization techniques for the proposed super-resolution approaches, overcoming the challenges induced by correlated signal sources, strong reflections from undesired clutter and heavy calculation burden. Based on the statistical characteristics of super-resolution algorithms, the performance of proposed approaches will be theoretically analyzed through the Root Mean Square Error (RMSE) and derived Cramér-Lower bound (CRLB) for each estimated parameter. The research results of this project will significantly fertilize the contents of the super-resolution multi-dimensional parameter estimation theory, and will also give a great contribution to the design and practical implementation of super-resolution FMCW radars.