Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion. Issue 5 (3rd February 2021)
- Record Type:
- Journal Article
- Title:
- Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion. Issue 5 (3rd February 2021)
- Main Title:
- Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion
- Authors:
- Mai, Zhenzhen
Xiong, Hailiang
Yang, Gangqiang
Zhu, Weihong
He, Fen
Bian, Ruochen
Li, Yujun - Abstract:
- Abstract: Multi‐source cooperative positioning systems relying on federated filtering have become attractive development directions of navigation strategy in real‐time localization and tracking under challenging urban environment. However, local Kalman filters of traditional federated filtering may result in divergence when the system model or the measurements is inaccurate, and the fixed information distribution coefficient in federated filter cannot adaptively reflect the performance of each local filter. To improve the precision and robustness of the integrated navigation system, a novel adaptive federated strong tracking Kalman filter with dynamic fading factor mechanism for multi‐sensor information fusion is proposed. Through iterative computation of the fading factor and updating the adaptive weight coefficients, strong tracking filter becomes robust to the uncertainty of system model. Meanwhile, an effective adaptive information distribution estimation algorithm based on the predicted residuals is constructed to balance the contributions of the kinematic model information and measurements on the state estimates. To ensure the stability of filtering, a simplified fusion strategy is established to solve the singular problem of the global covariance matrix of estimation error. Theoretical analysis and simulation results demonstrate the validity of the proposed approach in improving the accuracy and robustness of the integrated navigation and positioning systems. TheAbstract: Multi‐source cooperative positioning systems relying on federated filtering have become attractive development directions of navigation strategy in real‐time localization and tracking under challenging urban environment. However, local Kalman filters of traditional federated filtering may result in divergence when the system model or the measurements is inaccurate, and the fixed information distribution coefficient in federated filter cannot adaptively reflect the performance of each local filter. To improve the precision and robustness of the integrated navigation system, a novel adaptive federated strong tracking Kalman filter with dynamic fading factor mechanism for multi‐sensor information fusion is proposed. Through iterative computation of the fading factor and updating the adaptive weight coefficients, strong tracking filter becomes robust to the uncertainty of system model. Meanwhile, an effective adaptive information distribution estimation algorithm based on the predicted residuals is constructed to balance the contributions of the kinematic model information and measurements on the state estimates. To ensure the stability of filtering, a simplified fusion strategy is established to solve the singular problem of the global covariance matrix of estimation error. Theoretical analysis and simulation results demonstrate the validity of the proposed approach in improving the accuracy and robustness of the integrated navigation and positioning systems. The proposed integrated multi‐modal cooperative navigation and positioning algorithm will be of great significance to the implementation of real‐time mobile target localization and tracking in harsh environment or dead zone. … (more)
- Is Part Of:
- IET communications. Volume 15:Issue 5(2021)
- Journal:
- IET communications
- Issue:
- Volume 15:Issue 5(2021)
- Issue Display:
- Volume 15, Issue 5 (2021)
- Year:
- 2021
- Volume:
- 15
- Issue:
- 5
- Issue Sort Value:
- 2021-0015-0005-0000
- Page Start:
- 736
- Page End:
- 747
- Publication Date:
- 2021-02-03
- Subjects:
- Telecommunication systems -- Periodicals
Speech processing systems -- Periodicals
621.38205 - Journal URLs:
- http://digital-library.theiet.org/content/journals/iet-com ↗
http://ieeexplore.ieee.org/servlet/opac?punumber=4105970 ↗
http://www.ietdl.org/IET-COM ↗
https://ietresearch.onlinelibrary.wiley.com/journal/17518636 ↗
http://www.theiet.org/ ↗
http://ojps.aip.org/dbt/dbt.jsp?KEY=ICEOCW ↗ - DOI:
- 10.1049/cmu2.12116 ↗
- Languages:
- English
- ISSNs:
- 1751-8628
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4363.252200
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23468.xml