In view of limitations of the Gaussian model for describing star energy distribution, based on the radiation characteristic of stars and the imaging properties of star tracker, an improved skewed normal distribution model of star imaging was proposed and its key parameter vector was determined. The Kalman filtering was designed to estimate the characteristics of star spot. Then, based on the characteristic that the stellar imaging process of star sensor is a stationary random process, the state space composed of the characteristic of star spot was established and the optimal estimation value of the characteristics in the least square sense was obtained. Finally, the parameter optimization of star point imaging model was achieved by using look-up table method. The result of the ground star observation shows that the Kalman filter can estimate the characteristic quantity quickly and effectively. Compared with the Gaussian model, the improved skewed normal distribution model has higher simulation accuracy for the energy distribution of the star spot.