FPGA Implementation of a Pipelined Kalman Filter for Object Tracking in Two Dimensions

Jonghuy Choi (1), Yonghyeok Yoon (2), Taekyeong Song (3), Sunhee Kim (4)
(1) Department of System Semiconductor Engineering, Sangmyung University, 31, Sangmyeongdae-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do,31066, Republic of Korea
(2) Department of System Semiconductor Engineering, Sangmyung University, 31, Sangmyeongdae-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do,31066, Republic of Korea
(3) Department of System Semiconductor Engineering, Sangmyung University, 31, Sangmyeongdae-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do,31066, Republic of Korea
(4) Department of System Semiconductor Engineering, Sangmyung University, 31, Sangmyeongdae-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do,31066, Republic of Korea
Fulltext View | Download
How to cite (IJASEIT) :
Choi, Jonghuy, et al. “FPGA Implementation of a Pipelined Kalman Filter for Object Tracking in Two Dimensions”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 540-7, doi:10.18517/ijaseit.14.2.18504.
In a location tracking system for a moving object, not only accuracy but also real-time processing are important factors. The Kalman filter, as a recursive function, stands out as one of the prominent algorithms for object tracking. It continuously compares measured data with predicted values based on the system characteristics in real time, and then corrects the error of the predicted values while considering the noise of both system and measured data. This paper focuses on designing a hardware-based Kalman filter for object tracking in two dimensions. Following an analysis of the Kalman filter algorithm, the blocks capable of parallel processing are identified and configured to be processed in parallel, effectively reducing data processing time. The clock speed is enhanced by using the pipeline technique. In addition, the time-sharing technique is applied to increase the utilization of hardware resources and reduce the area. Data was processed at 32-bit floating points to uphold accuracy comparable to software-implemented Kalman filters. The proposed Kalman filter architecture is designed using verilog HDL and then simulated in Synopsys VCS/Verdi. And the accuracy is verified by comparing results with a software-based Kalman filter designed using MATLAB. It was implemented using Zynq ZYNQ-7 ZC702 Programmable logic via Xilnix Vivado, and can operate at 33MHz. It takes a total of 44 clocks, or 1.32 us, to process one data. Therefore, it was confirmed that the designed Kalman filter hardware is suitable for real-time processing.

Y. Fang, A. Panah, J. Masoudi, B. Barzegar and S. Fatehi, “Adaptive Unscented Kalman Filter for Robot Navigation Problem (Adaptive Unscented Kalman Filter Using Incorporating Intuitionistic Fuzzy Logic for Concurrent Localization and Mapping),” IEEE Access, vol. 10, pp. 101869-101879, 2022, doi:10.1109/access.2022.3207925.

Y. Li, C. Bian and H. Chen, “Object Tracking in Satellite Videos: Correlation Particle Filter Tracking Method with Motion Estimation by Kalman Filter,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-12, Sept. 2022, Art no. 5630112, doi:10.1109/TGRS.2022.3204105.

Q. Yu, B. Wang and Y. Su, “Object Detection-Tracking Algorithm for Unmanned Surface Vehicles Based on a Radar-Photoelectric System,” IEEE Access, vol. 9, pp. 57529-57541, 2021, doi:10.1109/access.2021.3072897.

A. -S. T. Hussain, M. Fadhil, T. A. Taha, O. K. Ahmed, S. A. Ahmed and H. Desa, “GPS and GSM Based Vehicle Tracking System,” in 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), Istanbul, Turkiye, 2023, pp. 1-5, doi:10.1109/ISAS60782.2023.10391720.

K. Feng, J. Li, D. Zhang, X. Wei and J. Yin, "Robust Cubature Kalman Filter for SINS/GPS Integrated Navigation Systems With Unknown Noise Statistics," IEEE Access, vol. 9, pp. 9101-9116, 2021, doi:10.1109/access.2020.3036423.

G. Ciaparrone, F. L. Sánchez, S. Tabik, L. Troiano, R. Tagliaferri, and F. Herrera, “Deep learning in video multi-object tracking: A survey,” Neurocomputing, vol. 381, 2020, pp. 61-88, doi:10.1016/j.neucom.2019.11.023.

Y. Nie, C. Bian, and L. Li, "Object Tracking in Satellite Videos Based on Siamese Network with Multidimensional Information-Aware and Temporal Motion Compensation," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 6517005, doi:10.1109/LGRS.2022.3211695.

Q. Li, R. Li, K. Ji, and W. Dai, “Kalman Filter and Its Application,” in 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, China, 2015, pp. 74-77, doi:10.1109/ICINIS.2015.35.

M. S. Grewal, and A. P. Andrews, “Linear Optimal Filters and Predictors” in Kalman Filtering: Theory and Practice with MATLAB, 4th ed., New Jersey, USA: Wiley, 2015, pp. 169-238.

P. Kim, “Kalman Filter,” in Essential Kalman filter, Korea: A-Jin, 2011.

J. Mochnac, S. Marchevsky and P. Kocan, “Bayesian filtering techniques: Kalman and extended Kalman filter basics,” in 2009 19th International Conference Radioelektronika, Bratislava, Slovakia, 2009, pp. 119-122, doi:10.1109/radioelek.2009.5158765.

Y. Fang, L. Yu, S. Fei, “An improved moving tracking algorithm with multiple information fusion based on 3D sensors,” IEEE Access, vol. 8, pp. 142295–142302, 2020, doi:10.1109/access.2020.3008435.

P. S. Madhukar, and L. B. Prasad, “State Estimation using Extended Kalman Filter and Unscented Kalman Filter,” in 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), Lakshmangarh, India, 2020, pp. 1-4, doi:10.1109/iconc345789.2020.9117536.

S. Yang, and M. Baum, “Extended Kalman filter for extended object tracking,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 4386-4390, doi:10.1109/icassp.2017.7952985.

J. Khodaparast, “A Review of Dynamic Phasor Estimation by Non-Linear Kalman Filters,” IEEE Access, vol. 10, pp. 11090-11109, 2022, doi:10.1109/access.2022.3146732.

S. Feng, X. Li, S. Zhang, Z. Jian, H. Duan, and Z. Wang, “A review: state estimation based on hybrid models of Kalman filter and neural network,” Systems Science & Control Engineering, vol. 11, no. 1, 2173682, 2023, doi:10.1080/21642583.2023.2173682.

Y. Wang and X. Mu, “Dynamic Siamese Network With Adaptive Kalman Filter for Object Tracking in Complex Scenes,” IEEE Access, vol. 8, pp. 222918-222930, 2020, doi:10.1109/access.2020.3043878.

T. Kim, and T. H. Park, “Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar,” Sensors, vol. 20, no. 15, 2020, doi:10.3390/s20154126.

Y. Liu, L. Zhang, Z. Chen, Y. Yan and H. Wang, “Multi-Stream Siamese and Faster Region-Based Neural Network for Real-Time Object Tracking,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 7279-7292, Nov. 2021, doi:10.1109/TITS.2020.3006927.

H. Wang, W. Ma, S. Zhang, and W. Hao, "Hierarchical Feature Pooling Transformer for Efficient UAV Object Tracking," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 6010405, doi:10.1109/LGRS.2023.3314435.

S. Kim, I. Petrunin and H. -S. Shin, “A Review of Kalman Filter with Artificial Intelligence Techniques,” in 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS), Dulles, VA, USA, 2022, pp. 1-12, doi:10.1109/ICNS54818.2022.9771520.

Y. -S. Zhang, T. -H. Chen, Y. -S. Chiou, S. -L. Chen, W. -T. Chen, Y. -K. Lin, F. -J. Wen, and T. -L. Lin, “Design and Implementation of Real-Time Localization Algorithms Based on FPGA for Positioning and Tracking,” in 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, 2019, pp. 446-448

A. K. Madsen, M. S. Trimboli and D. G. Perera, “An Optimized FPGA-Based Hardware Accelerator for Physics-Based EKF for Battery Cell Management,” in 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 2020, pp. 1-5, doi:10.1109/ISCAS45731.2020.9181073.

W. -T. Chen, S. -L. Chen, Y. -S. Chiou, T. -L. Lin, F. -J. Wen and Y. -K. Lin, “FPGA-Based Implementation of Reduced-Complexity Filtering Algorithm for Real-Time Location Tracking,” in 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/ CBDCom/CyberSciTech), Fukuoka, Japan, 2019, pp. 721-726, doi:10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00136.

J. Soh and X. Wu, “An FPGA-Based Unscented Kalman Filter for System-On-Chip Applications,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 4, pp. 447-451, April 2017, doi:10.1109/TCSII.2016.2565730.

F. Tian, X. Guo, and W. Fu, “Target Tracking Algorithm Based on Adaptive Strong Tracking Extended Kalman Filter,” Electronics, vol. 13, no. 3:652, 2024, doi:10.3390/electronics13030652.

B. Praveenkumar, and P. Eswaran, “FPGA implementation of multi-dimensional Kalman filter for object tracking and motion detection,” Engineering Science and Technology, an International Journal, vol. 33, 2022, doi:10.1016/j.jestch.2021.101084.

A. Mills, P.H. Jones, and J. Zambreno, “Parameterizable FPGA-based Kalman Filter Coprocessor using Piecewise Affine Modeling,” in IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, IL, USA, 2016, pp. 139–147, doi:10.1109/IPDPSW.2016.101.

P. Zhang, W. Li, and X. Yang, “Efficient Implementation of Recursive Multi-Frame Track-Before-Detect Algorithm Based on FPGA,” in: Proc. 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Chengdu, China, 2019, pp. 1–6

M. Pavlović, Z. Banjac and B. Kovačević, “Object Tracking in SWIR Imaging Based on Both Correlation and Robust Kalman Filters,” IEEE Access, vol. 11, pp. 63834-63851, 2023, doi:10.1109/access.2023.3288694.

C. T. Ginalih, A. S. Jatmiko, and R. Darmakusuma, “Simple Application of Kalman Filter On a Moving Object in Unity3D,” in 2020 6th International Conference on Interactive Digital Media (ICIDM), Bandung, Indonesia, 2020, pp. 1-3, doi:10.1109/ICIDM51048.2020.9339662.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).