Application of Square-Root Unscented Kalman Filter Smoothing Algorithm in Tracking Underwater Target

DOI

10.2991/macmc-17.2018.98How to use a DOI?

Keywords

Target Tracking, Square-root Unscented Kalman Filter, Smoothing Algorithm; Forward-filtering, Backward- smoothing

Abstract

In passive tracking, the nonlinearity may cause computational complication and precision degradation. To solve this problem, a novel filtering-smoothing algorithm based on Square-Root Unscented Kalman Filter (SR-UKFS) is proposed to track underwater target. In the SR-UKFS algorithm, the Square-Root Unscented Kalman Filter (SR-UKF) is used as forward-filtering algorithm to provide current location results, and the Rauch-Tung-Striebel (RTS) algorithm smoothes the previous state vector and covariance matrix using the current location results. Comparative analysis and validation are made on the tracking performances of SR-UKFS algorithm and SR-UKF algorithm, and the simulation results show that, under the same conditions, the SR-UKFS can more effectively improve the tracking precision than the SR-UKF algorithm. The SR-UKFS algorithm can reduce nearly 59% of the position error and nearly 54% of the velocity error.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiguo Yao
AU  - Yuxiang Su
AU  - Lili Li
PY  - 2018/01
DA  - 2018/01
TI  - Application of Square-Root Unscented Kalman Filter Smoothing Algorithm in Tracking Underwater Target
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 526
EP  - 531
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.98
DO  - 10.2991/macmc-17.2018.98
ID  - Yao2018/01
ER  -