多目标辐射源TDOA/FDOA联合定位能获得性能增益，但因观测模型的高度非线性和非凸性，难以保证收敛至全局最优解。针对这一问题，本文充分挖掘多目标辐射源之间的空间布局特征与协同运动规律，提出一种基于半定松弛的多源TDOA/FDOA协同定位新方法。首先建立包含目标间距离和速度约束的非线性观测模型，在此约束下推导TDOA/FDOA协同定位的克拉美罗界（CRB），定量刻画不等式约束带来的性能增益。其次，针对约束最大似然估计问题的非凸特性，将原始非线性观测模型转化为约束加权最小二乘（CWLS）问题，并采用半定松弛技术进行凸松弛处理，将非凸问题转化为可求解的凸优化问题，该方法降低了算法对初始值的依赖，避免了传统迭代算法易陷入局部最优的问题，并可获得半定松弛后凸优化问题的全局最优解。最后，通过仿真实验验证了所提方法的定位性能优势。

Joint TDOA/FDOA localization of multiple sources can achieve performance gains. However, due to the high nonlinearity and nonconvexity of the observation model, it is difficult to guarantee convergence to the global optimum. To address this issue, this paper fully exploits the spatial configuration characteristics and cooperative motion patterns among multiple sources, and proposes a semidefinite relaxation approach for cooperative multi-source TDOA/FDOA localization. First, a nonlinear observation model incorporating inter-source distance and velocity constraints is established. Based on these constraints, the Cramér–Rao Bound (CRB) for cooperative TDOA/FDOA localization is derived, thereby quantitatively characterizing the performance gains introduced by the inequality constraints. Second, to address the nonconvexity of the constrained maximum likelihood estimation problem, the original nonlinear observation model is transformed into a constrained weighted least squares (CWLS) problem. Semidefinite relaxation is then employed to perform convex relaxation, transforming the nonconvex problem into a tractable convex optimization problem. This method reduces the dependence of the algorithm on initial values, avoids the tendency of conventional iterative algorithms to become trapped in local optima, and enables the global optimum of the relaxed convex optimization problem to be obtained. Finally, simulation results validate the superior localization performance of the proposed method.