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This paper presents a low-complexity incremental (LCI) algorithm for target positioning and tracking in wireless sensor networks. The proposed positioning scheme calculates the estimated location of a target as a nonlinear least squares (NLS) problem based on the distance estimation and solves the optimization problem in an iterative manner using the LCI algorithm. In the decentralized scheme, a participating sensor estimates a target's current location, and then passes that information on to the next participating sensor, which calculates the target's newest location estimate. In addition, a convergence analysis is given to show the convergence behavior of the LCI-based method. Computer simulation results demonstrate that the proposed algorithm has higher estimation accuracy than previous related algorithms in both stationary and moving target scenarios.