This paper presents an adaptive sequential fusion estimation method for the target tracking with nonsynchronous measurements in wireless sensor networks (WSNs). Based on Gaussian assumption and Bayesian inference, a sequential cubature Kalman filtering (SCKF) method, as well as its square root form (SR-SCKF), is presented by applying the cubature rule to approximate the function × Gaussian integrals. By taking into consideration the time-varying properties of the measurement noise and the linearization errors, some adaptive factors are introduced into the SCKF to compensate for the measurement uncertainties based on Chi-square tests. The convergence analysis of the SCKF is presented. It is proved that the adaptive SCKF (ASCKF) has a better convergence property than the SCKF. Both simulations and experiments of a target tracking example are presented to show the effectiveness and superiority of the proposed ASCKF method.