Dönüştürülmüş ölçümler Kalman filtresi tabanlı skalerle ağırlıklandırılmış etkileşimli çoklu model

Yıl 2020, , 71 - 80, 25.10.2019

Kübra Turgut Ali Köksal Hocaoğlu

https://doi.org/10.17341/gazimmfd.439384

Öz

Bu çalışmada hedef takibi için, füzyon kriterlerine dayalı Skalerle ağırlıklandırılmış Etkileşimli Çoklu Model (SIMM) algoritması ile koordinat dönüşümlerinden kaynaklı sapmanın azaltılmasına yönelik öne sürülen Dönüştürülmüş Ölçümler Kalman Filtresi (CMKF) algoritması özelliklerinden yararlanılarak birden fazla hedef hareket modelinin kullanımına olanak sağlayan yeni bir Etkileşimli Çoklu Model (IMM) algoritması önerilmiştir. Önerilen algoritma yoğun manevralı ve yoğun gürültülü senaryolarda test edilmiştir. Önerilen algoritmanın, ölçümlerin polar/küresel koordinat olması durumunda literatürdeki SIMM-KF ve IMM-CMKF algoritmalarından daha az mesafe hatasına sahip olduğu gösterilmiştir.

Anahtar Kelimeler

Hedef takibi, etkileşimli çoklu model (IMM), skalerle ağırlıklandırılmış ektileşimli çoklu model (SIMM), dönüştürülmüş ölçümler Kalman filtresi (CMKF)

Scalar-weight interacting multiple model based on converted measurements Kalman filter

Öz

In this study, we take advantage of the fusion criteria based- Scalar-weight Interacting Multiple Model (SIMM) algorithm and the Converted Measurements Kalman Filter (CMKF), which reduces the bias caused by coordinate transformations to propose a novel Interacting Multiple Model (IMM) tracking algorithm which uses multiple motion models for target tracking, The proposed algorithm has been tested on scenarios with highly maneuvering targets under heavy measurement noise. It is shown that the proposed algorithm has smaller estimation error compared to SIMM-KF and IMM-CMKF algorithms.

Anahtar Kelimeler

Target tracking, interacting multiple model (IMM), scalar-weight interacting multiple model (SIMM), converted measurements Kalman filter (CMKF)

Kaynakça

• 1. Özden K., Özer A., Yücedağ O. M., Koçer H., Reduction of radar cross section using metamaterial based broadband absorbes , Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (4), 2016.
• 2. Tüysüz B., Development of semi-real time multi-frequency band supported passive radar system for aerial target detection, Journal of the Faculty of Engineering and Architecture of Gazi University, 2018 (2018), 2018.
• 3. Lerro D., Bar-Shalom Y., Tracking with debiased consistent converted measurements versus EKF, IEEE Transactions on Aerospace and Electronic Systems, 29 (3), 1015–1022, 1993.
• 4. Longbin M., Xiaoquan S., Yiyu Z., Kang S. Z., Bar-Shalom Y., Unbiased converted measurements for tracking, IEEE Transactions on Aerospace and Electronic Systems ,34 (3), 1023–1027, 1998.
• 5. Duan Z., Han C., Li X. R., Comments on ‘Unbiased converted measurements for tracking, IEEE Transactions on Aerospace and Electronic Systems, 40 (4), 1374-, 2004.
• 6. Bordonaro S. V., Willett P., Bar-Shalom Y., Unbiased tracking with converted measurements, 2012 IEEE Radar Conference, 0741–0745, 2012.
• 7. Bordonaro S., Willett P., Bar-Shalom Y., Decorrelated unbiased converted measurement Kalman filter, IEEE Transactions on Aerospace and Electronic Systems, 50 (2), 1431–1444, 2014.
• 8. Guo Z., Zhou G., Xu R., A Gaussian mixture Converted Doppler Measurement Kalman Filter, IET International Radar Conference 2015, 1–6, 2015.
• 9. Zhou G., Pelletier M., Kirubarajan T., Quan T., Statically Fused Converted Position and Doppler Measurement Kalman Filters, IEEE Transactions on Aerospace and Electronic Systems, 50 (1), 300–318, 2014.
• 10. Zhou G., Guo Z., Chen X., Xu R., Kirubarajan T., Statically Fused Converted Measurement Kalman Filters for Phased-Array Radars, IEEE Transactions on Aerospace and Electronic Systems, 54 (2), 554-568, 2018.
• 11. Spitzmiller J. N., Adhami R. R., A novel data-fusion-based improvement to debiased CMKF tracking, 17th European Signal Processing Conference, 1052–1056, 2009.
• 12. Blom H. A. P., An efficient filter for abruptly changing systems, The 23rd IEEE Conference on Decision and Control, 656–658, 1984.
• 13. Blair W. D., Watson G. A., Rice T. R., Interacting multiple model filter for tracking maneuvering targets in spherical coordinates, IEEE Proceedings of Southeastcon ’91, 2, 1055–1059 , 1991.
• 14. Cho C. H., Chong S. Y., Song T. L., IMM filtering for vehicle tracking in cluttered environments with glint noise, 2017 International Conference on Control, Automation and Information Sciences (ICCAIS), 98–105, 2017.
• 15. Liu Y., Tian X., Xu X., Posture estimation system by IMM-based unscented Kalman filters, 2017 Chinese Automation Congress (CAC), 2363–2368, 2017.
• 16. Yuan T., Krishnan K., Duraisamy B., Maile M., Schwarz T., Extended object tracking using IMM approach for a real-world vehicle sensor fusion system, 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 638–643, 2017.
• 17. Guo J. P., Xu J., Yan L., Xia X. G., Xiao X., Long T., Bian M. M., An improved IMM algorithm based on maneuvering-adaptive model set, 2016 CIE International Conference on Radar (RADAR), 1–5, 2016.
• 18. Sun M., Ma Z., Li Y., Maneuvering Target Tracking Using IMM Kalman Filter Aided by Elman Neural Network, 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, 1, 144–148, 2015.
• 19. Sustika R., Suryana J., Nonlinear-Filtering with Interacting Multiple-Model Algorithm for Coastal Radar Target Tracking System, Telkomnika Telecommunication, Computing, Electronics and Control, 13 (1), 211, 2015.
• 20. Fu X., Jia Y., Du J., Yu F., New interacting multiple model algorithms for the tracking of the manoeuvring target [Brief Paper], IET Control Theory Applications, 4(10), 2184–2194, 2010.