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Low-cost adaptive square-root cubature Kalman filter forsystems with process model uncertainty 被引量:6
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作者 an zhang shuida bao +1 位作者 wenhao bi yuan yuan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第5期945-953,共9页
A novel low-cost adaptive square-root cubature Kalmanfilter (LCASCKF) is proposed to enhance the robustness of processmodels while only increasing the computational load slightly.It is well-known that the Kalman fil... A novel low-cost adaptive square-root cubature Kalmanfilter (LCASCKF) is proposed to enhance the robustness of processmodels while only increasing the computational load slightly.It is well-known that the Kalman filter cannot handle uncertainties ina process model, such as initial state estimation errors, parametermismatch and abrupt state changes. These uncertainties severelyaffect filter performance and may even provoke divergence. Astrong tracking filter (STF), which utilizes a suboptimal fading factor,is an adaptive approach that is commonly adopted to solvethis problem. However, if the strong tracking SCKF (STSCKF)uses the same method as the extended Kalman filter (EKF) tointroduce the suboptimal fading factor, it greatly increases thecomputational load. To avoid this problem, a low-cost introductorymethod is proposed and a hypothesis testing theory is applied todetect uncertainties. The computational load analysis is performedby counting the total number of floating-point operations and it isfound that the computational load of LCASCKF is close to that ofSCKF. Experimental results prove that the LCASCKF performs aswell as STSCKF, while the increase in computational load is muchlower than STSCKF. 展开更多
关键词 square-root cubature Kalman filter strong tracking filter robustness computational load.
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