Abstract:
An adaptive Unscented Kalman Filter (UKF) for nonlinear stochastic systems is proposed and it is applied to the single observer passive location in Non-Gaussian environment. A Spherical Simplex Unscented Transformation (SSUT) is used to reduce the calculation requirement. In order to improve the filtering effect, an adaptive iterating estimation strategy is introduced to modify the gain of the algorithm update and the error covariance of the filtering is replaced by the square root of the error covariance to ensure numerical stability. The Monte Carlo simulation results show that, based on the glint noise statistical mode 1, the new algorithm has faster convergence, higher stability and accuracy.
Hailiang Song, Yongqing Fu and Xue Liu, 2012. An Adaptive UKF Algorithm for Single Observer Passive Location in Non-Gaussian
Environment. Information Technology Journal, 11: 1251-1257.
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