Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

ZHANG Hui; LIU Yongxin; JI Yonggang; WANG Linglin

doi:10.1007/s13131-018-1250-0

Volume 37 Issue 7

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Article Navigation > Acta Oceanologica Sinica > 2018 > 37(7): 131-140

ZHANG Hui, LIU Yongxin, JI Yonggang, WANG Linglin. Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system[J]. Acta Oceanologica Sinica, 2018, 37(7): 131-140. doi: 10.1007/s13131-018-1250-0

Citation:

ZHANG Hui, LIU Yongxin, JI Yonggang, WANG Linglin. Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system[J]. Acta Oceanologica Sinica, 2018, 37(7): 131-140. doi: 10.1007/s13131-018-1250-0

Citation:

ZHANG Hui, LIU Yongxin, JI Yonggang, WANG Linglin. Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system[J]. Acta Oceanologica Sinica, 2018, 37(7): 131-140. doi: 10.1007/s13131-018-1250-0

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Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

doi: 10.1007/s13131-018-1250-0

1.
College of Computer Science, Inner Mongolia University, Hohhot 010021, China;College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China
2.
College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China
3.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
4.
College of Computer Science, Inner Mongolia University, Hohhot 010021, China

Received Date: 2017-08-26

Abstract

Abstract

High-frequency surface wave radar (HFSWR) and automatic identification system (AIS) are the two most important sensors used for vessel tracking. The HFSWR can be applied to tracking all vessels in a detection area, while the AIS is usually used to verify the information of cooperative vessels. Because of interference from sea clutter, employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks. Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency. A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS. Since different systematic biases exist between HFSWR frequency measurements and AIS measurements, AIS information is used to estimate and correct the HFSWR systematic biases at each frequency. First, AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm. From the association results of the cooperative vessels, the systematic biases in the dual-frequency HFSWR data are estimated and corrected. Then, based on the corrected dual-frequency HFSWR data, the vessels are tracked using a dual-frequency fusion joint probabilistic data association (JPDA)-unscented Kalman filter (UKF) algorithm. Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.
- vessel tracking,
- high-frequency surface wave radar,
- automatic identification system,
- joint probabilistic data association,
- unscented Kalman filter

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References

Bar-Shalom Y, Li Xiaorong. 1995. Multitarget-Multisensor Tracking:Principles and Techniques. Storrs, CT:YBS Publishing

Braca P, Grasso R, Vespe M, et al. 2012. Application of the JPDA-UKF to HFSW radars for maritime situational awareness. In:Proceedings of 15th International Conference on Information Fusion (FUSION). Singapore:IEEE, 2585-2592

Braca P, Maresca S, Grasso R, et al. 2015. Maritime surveillance with multiple over-the-horizon HFSW radars:an overview of recent experimentation. IEEE Aerospace and Electronic Systems Magazine, 30(12):4-18

Bruno L, Braca P, Horstmann J, et al. 2013. Experimental evaluation of the range-doppler coupling on HF surface wave radars. IEEE Geoscience and Remote Sensing Letters, 10(4):850-854

Chaturvedi S K, Yang C, Ouchi K, et al. 2012. Ship recognition by integration of SAR and AIS. Journal of Navigation, 65(02):323-337

Dzvonkovskaya A, Gurgel K W, Rohling H, et al. 2008. Low power high frequency surface wave radar application for ship detection and tracking. In:Proceedings of 2008 International Conference on Radar. Adelaide, SA:IEEE, 627-632

Dzvonkovskaya A L, Rohling H. 2007. Ship detection with adaptive power regression thresholding for HF radar. Radar Science and Technology, 5(2):81-85

Dzvonkovskaya A L, Rohling H. 2010. HF radar performance analysis based on AIS ship information. In:Proceedings of 2010 IEEE Radar Conference. Washington, DC:IEEE, 1239-1244

Grosdidier S, Baussard A, Khenchaf A. 2010. HFSW radar model:simulation and measurement. IEEE Transactions on Geoscience and Remote Sensing, 48(9):3539-3549

Gurgel K W, Schlick T, Horstmann J, et al. 2010. Evaluation of an HF-radar ship detection and tracking algorithm by comparison to AIS and SAR data. In:Proceedings of 2010 International Waterside Security Conference (WSS). Carrara:IEEE, 1-6

Habtemariam B, Tharmarasa R, McDonald M, et al. 2015. Measurement level AIS/radar fusion. Signal Processing, 106:348-357

Ince A N, Topuz E, Panayirci E, et al. 1998. Principles of Integrated Maritime Surveillance Systems. New York:Springer

Ji Yonggang, Zhang Jie, Meng Junmin, et al. 2014a. Point association analysis of vessel target detection with SAR, HFSWR and AIS. Acta Oceanologica Sinica, 33(9):73-81

Ji Yonggang, Zhang Jie, Wang Yiming, et al. 2014b. Ship detection point association and fusion with dual-frequency HF surface wave radar. Systems Engineering and Electronics (in Chinese), 36(2):266-271

Ji Yonggang, Zhang Jie, Wang Yiming, et al. 2016. Vessel target detection based on fusion range-Doppler image for dual-frequency high-frequency surface wave radar. IET Radar, Sonar & Navigation, 10(2):333-340

Malkoff D B. 1997. Evaluation of the Jonker-Volgenant-Castanon (JVC) assignment algorithm for track association. In:Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI. Orlando, FL, United States:SPIE, 228-239

Maresca S, Braca P, Horstmann J, et al. 2014. Maritime surveillance using multiple high-frequency surface-wave radars. IEEE Transactions on Geoscience and Remote Sensing, 52(8):5056-5071

Ponsford T, Wang Jian. 2010. A review of high frequency surface wave radar for detection and tracking of ships. Turkish Journal of Electrical Engineering & Computer Sciences, 18(3):409-428

Sheng Weidong, Lin Liangkui, An Wei, et al. 2010. A passive multisensor multitarget track association algorithm based on global optimization. Journal of Electronics & Information Technology (in Chinese), 32(7):1621-1625

Vivone G, Braca P, Horstmann J. 2015. Knowledge-based multitarget ship tracking for HF surface wave radar systems. IEEE Transactions on Geoscience and Remote Sensing, 53(7):3931-3949

Xiao F, Ligteringen H, Van Gulijk C, et al. 2015. Comparison study on AIS data of ship traffic behavior. Ocean Engineering, 95(0):84-93

Xiong Kai, Zhang Hongyue, Chan C W. 2006. Performance evaluation of UKF-based nonlinear filtering. Automatica, 42(2):261-270

Zhang Hui, Liu Yongxin, Zhang Jie, et al. 2015. Target point tracks optimal association algorithm with surface wave radar and automatic identification system. Journal of Electronics & Information Technology (in Chinese), 37(3):619-624

Zhou Yifeng, Leung H, Blanchette M. 1999. Sensor alignment with earth-centered earth-fixed (ECEF) coordinate system. IEEE Transactions on Aerospace and Electronic Systems, 35(2):410-418

Zhou Yifeng, Leung H, Yip P C. 1997. An exact maximum likelihood registration algorithm for data fusion. IEEE Transactions on Signal Processing, 45(6):1560-1573

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