对于布设在分布式移动平台上的雷达节点，当前技术手段无法实现雷达节点间精准的时钟同步，非理想时钟同步将导致信号时延、多普勒频率测量不准确，现有方法无法有效定位目标。本文针对分布式雷达授时同步后雷达节点间存在时钟同步误差下的运动目标定位问题，提出了一种时钟自同步的运动目标定位方法，即在估计出目标位置速度的同时校正雷达节点间时钟同步误差，优化时钟同步参数。具体的，本文基于最大似然-最大后验概率估计理论提出了一种运动目标定位与时钟同步算法，算法首先给出了先运动目标位置速度后时钟同步误差的估计流程；然后针对运动目标位置速度估计，提出了一种解析初始值求解和参数估计迭代优化的目标定位方法，并在运动目标参数估计值的基础上实现了对雷达节点间时钟同步误差的校正估计。实验结果表明，本文提出的时钟自同步运动目标定位方法能够以低运算复杂度有效地估计出目标位置速度以及雷达节点间时钟同步误差。

It is may not be feasible to achieve precise clock synchronization among radar nodes when they are deployed on dis-tributed mobile platforms by using current technological means. The non ideal clock synchronization among the radar nodes will result in inaccurate time delay and Doppler frequency measurements of the signal, and existing methods cannot effectively locate target. For the moving target localization problem in presence of clock synchronization errors among radar nodes after they are synchronized, a clock self-synchronized target localization method is proposed, i.e. the proposed method estimates the position and velocity of the moving target while correcting the clock synchronization errors among the radar nodes, optimizing the clock synchronization parameters. Specifically, a moving target localiza-tion and clock synchronization algorithm based on the hybrid maximum likelihood and maximum a posteriori probability estimation theory is recommended, which lists the estimation process of estimating the moving target position and ve-locity first and then estimating the clock synchronization errors. To estimate the moving target position and velocity, a localization method based on analytical initial value solving and parameter estimation iterative optimization is proposed. Based on the estimate of the moving target, the clock synchronization errors among the radar nodes are figured out. The experimental results show that the proposed clock self-synchronized target localization method can effectively estimate the position and velocity of the moving target and the clock synchronization errors among the radar nodes with low computational complexity.