Abstract: Conventional Taylor series expansion model only considers the distances between unknown nodes and anchor nodes, without considering the distances between unknown nodes. As a result, the location information is not comprehensive enough to result in lower positioning accuracy. Thus, a novel localization algorithm based on multivariable Taylor series expansion model is proposed to further enhance positioning accuracy. Firstly, the new positioning model which considers the distances between unknown nodes in multivariable Taylor series expansion is established. In the process of model solution, the particle swarm algorithm is used to obtain the estimated position values of the unknown nodes. Then, the optimal position values are obtained by the weighted least squares method. Finally, the Cramer-Rao lower bound (CRLB) of the positioning result is derived to evaluate the performance of the proposed algorithm. Simulation results demonstrate that the proposed algorithm obtains a higher positioning accuracy, and its positioning error is very close to the CRLB.