Improving bearings-only target state estimation tracking problem by using adaptive and nonlinear kalman algorithms
Finding the best estimate of the process state from noisy data is the main problem in tracking systems, many efforts and researches have been done to remove this noise. More useful information about the target's state can be extracted from observations by using a more appropriate model for the...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-07-01.
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| Online Access: | Get fulltext |
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| LEADER | 02217 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs16928_12552 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Khadour, Tammam |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Saba, Michel Al |e author |
| 700 | 1 | 0 | |a Saleh, Louay |e author |
| 245 | 0 | 0 | |a Improving bearings-only target state estimation tracking problem by using adaptive and nonlinear kalman algorithms |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16928 | ||
| 520 | |a Finding the best estimate of the process state from noisy data is the main problem in tracking systems, many efforts and researches have been done to remove this noise. More useful information about the target's state can be extracted from observations by using a more appropriate model for the target's motion or using additional sensors. In this paper, we will introduce two methods to improve the estimation of bearing-only target tracking problem in two dimensions (2D). The first method is by adding a third sensor and making a good alignment of those sensors, and at the same time an extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) are implemented. The second method is by applying an adaptive nonlinear Kalman filter (ANKF) for two sensors to solve the problem of measurement variance uncertainty. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Control;Engineering; Kalman Filters | ||
| 690 | |a Target tracking, Sensor, Estimation, Noise, Nonlinear, EKF, UKF, CKF, AKF, ANKF | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 15, No 1: July 2019; 190-198 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v15.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16928/12552 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16928/12552 |z Get fulltext |