Amended Kalman Filter for Maneuvering Target Tracking

YANG Yongjian; FAN Xiaoguang; ZHUO Zhenfu; WANG Shengda; NAN Jianguo; XU Yunshan

doi:10.1049/cje.2016.08.036

Volume 25 Issue 6

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YANG Yongjian, FAN Xiaoguang, ZHUO Zhenfu, et al., “Amended Kalman Filter for Maneuvering Target Tracking,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1166-1171, 2016, doi: 10.1049/cje.2016.08.036

Citation:

YANG Yongjian, FAN Xiaoguang, ZHUO Zhenfu, et al., “Amended Kalman Filter for Maneuvering Target Tracking,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1166-1171, 2016, doi: 10.1049/cje.2016.08.036

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Amended Kalman Filter for Maneuvering Target Tracking

doi: 10.1049/cje.2016.08.036

Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China

Funds: This work is supported by the Aeronautical Science Fund of Shaanxi province of China (No.20145596025).

Received Date: 2015-11-25
Rev Recd Date: 2016-03-11
Publish Date: 2016-11-10

Abstract

Abstract

The conventional Kalman filter (KF) which uses the current measurement to estimate the current state is a posterior estimation. KF is identified as the optimal estimation in linear models with Gaussian noise. However, the performance of KF with incomplete information may be degraded or diverged. In order to improve the performance of KF, an Amended KF (AKF) is proposed by using more posterior measurements. The principle, derivation and recursive process of AKF are presented. The differences among Kalman smoother, adaptive fading method and AKF are analyzed. The simulation results of target tracking with different covariance of motion model indicate the high precision and robustness of AKF.
- Kalman filter (KF),
- Incomplete information,
- Amended KF (AKF),
- Maneuvering target tracking

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References(18)

References

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