An effective passive source localization technique is proposed as essential for industrial applications. Extensive research has been conducted on multi-station passive localization using two-dimensional angle of arrival (2D AOA), time difference of arrival (TDOA), and their combination. However, certain practical factors limit the observation to only a restricted set of one-dimensional angles of arrival (1D AOA). Additionally, there may be perturbations in the stations' locations. To address these issues, this paper proposes a localization model that hybrid a single 1D AOA with multiple TDOA measurements, taking into account the perturbations in station locations. For clarity, we refer to this combination as hybrid measurements. Two types of localization algorithms are proposed for the hybrid measurement model. The first type approximates the localization problem as a non-convex constrained weighted least squares optimization problem. We introduce two solution algorithms: the two-step weighted least squares algorithm (TWLS) and the semi-infinite relaxation method (SDR), both of which do not require an initial guess. The second type directly transforms the localization problem into a maximum likelihood estimation (MLE) minimum optimization problem. Several iterative algorithms are introduced, including iterative weighted least squares based on Taylor expansion (IRLS-TE), Gaussian Newton (GN), adaptive moment estimation (Adam), and simplification adaptive moment estimation (SAdam). Unlike the first type, these algorithms require an initial guess. Furthermore, we derive the Cramér-Rao Lower Bound (CRLB) for hybrid measurements and analyze the computational complexity and minimum station configuration needed for each solution algorithm. Simulations and the SWellEx96-S5 ocean acoustic experiments demonstrate the effectiveness of the proposed methods. The results indicate that the proposed algorithms can achieve CRLB performance at low noise levels, with the TWLS algorithm showing the lowest complexity, while the Adam algorithm achieves the highest accuracy. These proposed solution algorithms can be selected based on computational complexity, localization accuracy and source of interest location.