A new parameters adaptively adjusting method of current statistical model

The fixed maximum acceleration and maneuvering frequency of current statistical model leads to the divergence of filtering algorithm. In this study, a new model which employs innovation dominated subjection function to adaptively adjust maximum acceleration and maneuvering frequency is proposed based on current statistical model. Although the new model has a better performance, a fluctuant phenomenon appears. As far as this problem is concerned, a new filter algorithm which is based on amendatory and adaptively fading kalman filtering is proposed. The results of simulation indicate the effectiveness and coherent of the new model and the new algorithm, and their well performance in maneuvering target tracking.

[1]  Chang Xiao-feng.  Research of UKF in the target tracking , 2012 .

[2]  Zhang Ping-ding.  An Adaptive Algorithm of Maneuvering Targets Tracking Based onManeuvering Frequency , 2004 .

[3]  Huijing Shi,et al.  Maneuvering frequency adaptive algorithm of maneuvering target tracking , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[4]  Yang Hao-yun,et al.  An Improved Adaptive Filtering Algorithm Based on Truncation Gauss Probability Model , 2013 .

[5]  Zhu Xinyan.  A Simaplied SAGE-HUSA Kalman Filetering Algorithm , 2011 .

[6]  K. S. P. Kumar,et al.  A 'current' statistical model and adaptive algorithm for estimating maneuvering targets , 1984 .

[7]  Michael A. Mastro.  Financial Derivative and Energy Market Valuation: Theory and Implementation in MATLAB , 2013 .

[8]  Zhao Zhi-cheng.  Modification of Environmental Function Model Based on Adaptive Kalman Filter , 2010 .

[9]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[10]  E. Cortina,et al.  Maneuvering target tracking using extended Kalman filter , 1991 .

[11]  Mohamed Boutayeb,et al.  A strong tracking extended Kalman observer for nonlinear discrete-time systems , 1999, IEEE Trans. Autom. Control..

[12]  Diao Lian-wang,et al.  An Improved Description of "Current" Statistical Model for Maneuvering Target , 2005 .

[13]  Ghaleb Hoblos,et al.  A gain modification form for Kalman filtering under state inequality constraints , 2010 .

[14]  Gai Meng,et al.  Adaptive fading Kalman filter based on innovation covariance , 2011 .

[15]  Zhang Yue.  Adaptive tracking algorithm of maneuvering targets based on current statistical model , 2011 .

[16]  Cui Lin.  Adaptive strong tracking algorithm for maneuvering targets based on current statistical model , 2011 .

[17]  Qidan Zhu,et al.  Adaptive Nonlinear Filter Algorithm Based On Current Statistical Model , 2007, 2007 International Conference on Mechatronics and Automation.