divided difference filter
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2018 ◽  
Vol 18 (23) ◽  
pp. 9612-9618 ◽  
Author(s):  
Siyuan Zou ◽  
Jianli Li ◽  
Zhaoxing Lu ◽  
Quanpu Liu ◽  
Jiancheng Fang

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 41720-41727 ◽  
Author(s):  
Chengjiao Sun ◽  
Yonggang Zhang ◽  
Guoqing Wang ◽  
Wei Gao

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hongjian Wang ◽  
Jinlong Xu ◽  
Aihua Zhang ◽  
Cun Li ◽  
Hongfei Yao

We present a support vector regression-based adaptive divided difference filter (SVRADDF) algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR) is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i) an underwater nonmaneuvering target bearing-only tracking system and (ii) maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.


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