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Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of that the traditional extended Kalman filter and unscented Kalman filter methods cannot effectively suppress the additive noise of the chaotic system, an improved particle filter algorithm is presented. The state space description of the chaotic system is derived in detail, and a noise reduction method of chaotic signal based on auxiliary sigma point particle filter is proposed. As an example，Lorenz chaotic signal is used to evaluate the efficiency of the proposed algorithm. The results show that the proposed algorithm has obvious effect on noise reduction for Lorenz chaotic signal with noise. [ABSTRACT FROM AUTHOR]

A fuzzy adaptive algorithm is proposed for the imperfections of tracking a maneuvering target using conventional algorithm based on current statistical model. Maneuvering frequency is adjusted in real time by fuzzy reasoning according to the normalized residual and its change rate. Acceleration variance is depicted using residual power function, and the mean value of current acceleration is updated by the de- viation between the estimated and predicted values of acceleration. On this basis, the weight of proposed algorithm is revised by Gauss membership function and strong tracking algorithm. Fuzzy adaptive algorithm is not restricted by maneuvering frequency given manually and extreme value of maximum acceleration, which is suitable for the different ranges and degrees of maneuvering. The performance of the proposed algorithm is tested by tracking three typical maneuvering targets, such as step maneuvering, circular maneuvering, and Jerk maneuvering. Simulated results show the tracking range is expanded using the pro- posed algorithm compared with the conventional tracking algorithm based on current model and the adaptive algorithm based on Jerk model. The proposed algorithm has good steady-state and transient characteristics, and its tracking accuracy and convergence rate are superior to those of the other two algorithms. [ABSTRACT FROM AUTHOR]