CAJ | 학술논문

针对一类非线性系统滤波问题，提出了一种改进的强跟踪平方根分解UKF算法。该算法通过引入自适应渐消因子改善了滤波器的鲁棒性，利用改善的平方根分解方法提高了滤波器的计算效率。通过实验仿真验证，该算法相对于传统的强跟踪UKF算法具有相近的估计精度和更快的计算效率，相对于强跟踪滤波器具有更高的精度。
침대일류비선성계통려파문제，제출료일충개진적강근종평방근분해UKF산법。해산법통과인입자괄응점소인자개선료려파기적로봉성，이용개선적평방근분해방법제고료려파기적계산효솔。통과실험방진험증，해산법상대우전통적강근종UKF산법구유상근적고계정도화경쾌적계산효솔，상대우강근종려파기구유경고적정도。
In this paper,for a class of nonlinear system filtering problem,a improved strong tracking square?root unscented kalman filtering(ISTSRUKF)algorithm is proposed. The robustness of the filter is improved by introducing adaptive fading factor and the computation efficiency of the filter is better than traditional square?root unscented kalman filter(UKF)by using modified decomposition method. The simulation results show that the algorithm has a similar estimation accuracy and faster computational efficiency with respect to the traditional strong tracking UKF algorithm and a higher accuracy relative to the strong tracking filter.