Spatial target localization using fuzzy Square-root Cubature Kalman Filter

The tracking of spatial target based on bearing-only measurements belongs to the strong nonlinear filtermodel. The uncertainty of the statistical characteristics of noise, caused by complicated space environment, leads to reduced accuracy of traditional methods. To improving the accuracy, a new method, Fuzzy Square-root Cubature Kalman Filter (FS-CKF), was presented by combining possibility techniques and Square-root Cubature Kalman Filter (SCKF). The gordian technique of this algorithm is to describe process and measurement noise by trapezoidal possibility distribution instead of Gaussian probability distribution. The simulation compares the performance in spatial target localization between FS-CKF and SCKF, which turns out that the convergence speed of the FS-CKF algorithm is 32.52%faster in positionand 18.28% faster in velocity; meanwhile, the accuracy of FS-CKF is 12.52% and 42.65% higher than SCKF in terms of position and velocity respectively.