###### Abstract
However, none of the aforementioned research considered the position error of the ANs, which may significantly deteriorate localization and synchronization performances. One-way TOA does not require transmission back to the ANs, unlike two-way TOA, thus saving half of the communication overhead in the TOA measuring process. UNs do not need to equip transmitters designed for measuring TOA, reducing the complexity of hardware design. Moreover, because UNs only need to passively receive signals from ANs and then complete positioning at the UN end, the number of locatable UNs will not be limited. For a WSN using the TD scheme to generate sequential one-way TOA measurements, researchers have proposed various methods. Ref. [19] introduced a distributed state estimation method utilizing inter-node information through time division broadcasting to address the JLAS problem. While their method supports an unlimited number of UNs, it is computationally complex and unsuitable for moving UNs. To reduce complexity, ref. [7] further explored the use of a two-step weighted least squares (TSWLS) method to jointly estimate the positions, velocities, and clock parameters of UNs, considering the challenge of AN position uncertainty. However, TSWLS only achieves the CRLB under low-noise conditions and performs inadequately when noise levels are high. The best achievable estimator, the maximum likelihood estimator (MLE), was used in [8], which resorted to Gauss-Newton iteration to asymptotically implement optimal localization and synchronization parameter estimation. However, this method is sensitive to initial values. Ref. [11] proposed a closed-form solution that avoids dependence on initial values, achieving an asymptotically optimal solution without iteration. Nonetheless, the closed-form solution in [11] only reaches the CRLB under low-noise conditions, and its root mean square error (RMSE) deviates from the CRLB if the measurement noise or AN position error is relatively large. [PERSON]. [27] extended the study of JLAS to cases where ANs are not synchronized, and the motion of UNs is arbitrary. It proposed three methods for different UN motion models, with three of them being simple variants of the Gauss-Newton (GN) method, thus suffering from similar divergence problems or local minima. This paper focuses on the JLAS problem using sequential one-way TOAs when the positions of ANs are not accurately known. A new approach to estimating the location of the UN and clock parameters is proposed. We transform the nonlinear relationship between TOAs, UN position, clock offset, and clock drift by squaring the measurement equation to pseudo-linearize the problem. The pseudo-linearization introduces redundant variables, which are eliminated by projecting the equation to the nullspace of the space of their coefficients. In ignoring the constraints that would make the problem complex to solve, a WLS solution of the UN position, clock offset, and clock drift is obtained straightforwardly. We then formulate a new equation with respect to the estimation error by applying the constraints, generating the correction that improves the accuracy. The corrected solution is not optimal and cannot attain the CRLB; thus, further refinement is necessary. The solution is referred to as the closed-form projection solution (CFPS). To enhance robustness to noise and convergence to the optimal solution, a new iterative solution called the robust iterative solution (RIS) is proposed. The RIS is formulated through factor graphs and derived using message passing. It relaxes the requirement of initialization, so the CFPS can serve as an adequate initial guess that guarantees convergence to the optimum. In comparison with the GN initialized by the CFPS, the proposed RIS is superior in robustness and convergence assurance. Even when the noise or AN position error is relatively large, where the CFPS does not provide a guess close to the true value, the RIS still has a higher probability of converging to the global minimum.