To address the target tracking problem in resource-constrained mobile wireless sensor networks, considering the uncertainties in motion modeling and received signal strength (RSS) quantization, this paper proposes a robust unscented Kalman fusion filtering method based on the RSS environment-adaptive event-driven mechanism. First, an environment-adaptive event-driven mechanism is designed to enable the mobile target to dynamically schedule an expected number of anchor nodes according to their distribution, while adaptively triggering anchor node data transmission to reduce the computational, energy, and communication burdens on wireless sensor network nodes. Then, a randomly uniformly distributed noise covariance is introduced to characterize the uncertainties in motion modeling and RSS quantization. And a robust square-root unscented Kalman fusion filtering algorithm is constructed by integrating multiple Sigma-point uniform random sampling with weighted averaging to mitigate the adverse effects of these uncertainties on tracking performance. Finally, numerical simulation results demonstrate that the proposed method effectively enhances the stability, robustness, and accuracy of target tracking while ensuring that the number of triggered anchor nodes is close to the expected value.