The relative positions of the sensors from one another in a rigid sensor network are known and locating the network reduces to obtaining its position, orientation angle, and translational and angular velocities with respect to a global coordinate frame from the measurements with anchors. Previous solution is computationally demanding that may not be suitable in a resource constrained environment. We propose a solution for this highly nonlinear estimation problem using the divide and conquer approach in the 2-D scenario. We first obtain from the measurements the sensor positions and velocities pretending no prior knowledge among them and then exploit their relative positions to estimate the unknown parameters. Methods are available for the first step. We focus on the second step and develop a closed-form solution through nuisance variables and nonlinear transformations. The proposed estimator is computationally attractive and has CRLB performance for Gaussian noise over the small error region.