For target tracking under conditions of inaccurate noise covariance and non-uniform sampling, this paper proposes an adaptive filter. During the measurement modeling phase, this filter incorporates an dimensionality expansion strategy to reconstruct the measurement model under non-uniform sampling, thereby effectively preventing measurement information omission and temporal mismatch issues. To achieve precise estimation of dynamic targets, this paper models the target state as an inverse Wishart distribution, where the target′s kinematic state is modeled as a Gaussian distribution and the noise covariance as an inverse Wishart distribution. By introducing a variational inference mechanism, a Bayesian recursive process is derived for optimal estimation of the target state under conditions of inaccurate noise covariance and non-uniform sampling. Simulation results validate the proposed algorithm′s robustness and estimation accuracy in complex observation environments. Compared to existing Kalman filters, it demonstrates significant advantages in handling inaccurate noise covariance and non-uniform sampling, exhibiting superior adaptability and tracking performance.