Track Detection Algorithm Based on Trace Correlation Using Hough Transform
https://doi.org/10.32603/1993-8985-2023-26-2-65-77
Abstract
Introduction. Track detection is one of the main tasks to be solved in trajectory processing. This task can be efficiently solved using the Hough Transform. A track is considered detected if the number of position measurements received in a number of consecutive radar scans and falling into the same cell of the parameter space (accumulator) has exceeded the detection threshold. However, the effective practical application of the Hough transform requires a sufficiently long time of measurement. Under a small number of scans given for track detection, measurements are also accumulated in those accumulator cells where their traces intersect. Therefore, in order to detect true tracks, additional processing is required to distinguish measurement clusters from different targets based on their geometric proximity. In addition, a large amount of memory and computational operations for the accumulator maintenance significantly increase the computation load of the trajectory processor.
Aim. To design a simple and false-detection resilient algorithm for detecting tracks without the Hough accumulator in the processor memory.
Materials and methods. In the proposed algorithm, the construction of measurement traces in the Hough accumulator followed by selection of cells with the largest number of traces passed through them is replaced by computation of the cross correlations of the traces and clustering of measurements based on the maximum similarity of their traces.
Results. Mathematical simulation with the scenario parameters selected in the paper confirmed the accuracy of the proposed algorithm in detecting all tracks existing in the radar field of view and its efficiency in conducting error free association of target position measurements.
Conclusion. A false-detection resilient algorithm for track detection was created based on the Hough transform. The algorithm does not require the Hough accumulator in the processor memory.
About the Author
A. A. Monakov
Saint-Petersburg State University of Aerospace Instrumentation
Russian Federation
Russian Federation
Andrey A. Monakov, Dr Sci. (Eng.) (2000), Professor (2005) of the Department of Radio Engineering Systems. Honored Mechanical Engineer of the Russian Federation (2005), Honored Worker of Higher Professional Education of the Russian Federation (2006). The author of more than 200 scientific publications. Area of expertise: extended radar targets; digital signal processing; synthetic aperture radar; remote sensing; air traffic control.
190000, St Petersburg, Bolshaya Morskaya St., 67 A
References
1. Konovalov A. A. Osnovy traektornoi obrabotki radiolocatzionnoi informatzii [Foundations of the Trajectory Surveillance of Radar Targets]. Part. 1. St Petersburg, ETU Publishing House, 2013, 164 p. (In Russ.)
2. Kiselev V. Yu., Monakov A. A. Assessment of Trajectory Processing Algorithms in Air Traffic Control Radar Systems: Track Detection. J. Radioengineering. 2016, vol. 80, no. 3, pp. 28–36.
3. Hough P. V. C. Methods and Means for Recognizing Complex Patterns. Pat. US 3069654. Publ. 18.12.1962.
4. Duda R. O., Hart P. E. Use of the Hough Transform to Detect Lines and Curves in Pictures. Communication of the ACM. 1972, vol. 15, no. 1, pp. 11–15. doi:10.1145/361237.361242
5. Illingworth J., Kittler J. A Survey of the Hough Transform. Computer Vision. Graphics and Image Processing. 1988, vol. 44, no. 1, pp. 87–116. doi:10.1016/0734-189x(88)90071-0
6. Mukhopadhyay P., Chaudhuri B. B. A Survey of Hough Transform. Pattern Recognition. 2015, vol. 48, no. 3, pp. 993–1010. doi:10.1016/j.patcog.2014.08.027
7. Carlson B. D., Evans E. D., Wilson S. L. Search Radar Detection and Track with the Hough Transform, Part I: System Concept. IEEE Trans. on Aerospace and Electronic Systems. 1994, vol. 30, no. 1, pp. 102–108. doi:10.1109/7.250410
8. Yankowich S. W., Farooq M. A Hough Transform Based Multisensor, Multitarget Track Initiation Technique. Proc. of the 36th IEEE Conf. on Decision and Control. 1997, vol. 5, pp. 5018–5023. doi:10.1109/cdc.1997.649856
9. Alexiev K. Implementation of Hough Transform as Track Detector. Proc. of the Third Intern. Conf. on Information Fusion, Paris, France, 10-13 July 2000. IEEE, 2000, vol. 2. doi:10.1109/ific.2000.859896
10. Jin S., Liang Y., He P., Pan G., Pan Q., Cheng Y. An Effective Hough Transform Based Track Initiation. Intern. Conf. on Machine Learning and Cybernetics, Dalian, China, 13–16 Aug. 2006. IEEE, 2006. P. 3196–3200. doi:10.1109/icmlc.2006.258425
11. Voroshilina E. P., Voroshilin E. P., Tislenko V. I. Track initiation methods for moving targets. Proc. of TUSUR University. 2009, vol. 20, no. 2, pp. 48–52. (In Russ.)
12. Semenov A. N. Radar Target Detection by Means of the Hough Transform. Science and Education. MGTU Electron J. 2014, no. 12, pp. 619–632. doi:10.7463/1214.0738733 (In Russ.)
13. Semerdjiev E., Alexiev K., Djerassi E., Konstantinova P. Multiple Hypothesis Tracking Using Hough Transform Track Detector. Information & Security: An Intern. J. 1999, vol. 2, pp. 113–121. doi:10.11610/isij.0210
14. Alexiev K. M., Bojilov L. V. A Hough Transform Track Initiation Algorithm for Multiple Passive Sensors. Proc. of the 3rd Intern. Conf. on Information Fusion, Paris, France, 10–13 July 2000. IEEE, 2000, vol. 1. doi:10.1109/ific.2000.862662
15. Lo Т., Litva J., Leung Н., Bridgewater А. W. Multitarget Track Initiation Using a Modified Hough Transform. AGARD, Pointing and Tracking Systems. England, Technivision Services, 1994, pp. 25-1–25-8.
16. Liu H., Liu H. W., Dan X., Zhou S., Liu J. Cooperative Track Initiation for Distributed Radar Network Based on Target Track Information. IET Radar Sonar Navig. 2016, vol. 10, no. 4, pp. 735–741. doi:10.1049/ietrsn.2015.0312
17. Spratling А. А Neural Implementation of the Hough Transform and the Advantages of Explaining Away. Image and Vision Computing. 2016, vol. 52, рр. 15–24. doi:10.1016/j.imavis.2016.05.001
18. Carlson B. D., Evans E. D., Wilson S. L. Search Radar Detection and Track with the Hough Transform, Part II: Detection Statistics. IEEE Trans. on Aerospace and Electronic Systems. 1994, vol. 30, no. 1, p. 109–115. doi:10.1109/7.250411
19. Monakov A. A. Detector of a Moving Optic Target Based on the Hough Transform. Proc. of XIX Intern. Conf. "Radar, Navigation and Communication". 2013, vol. 3. pp. 1434–1443. (In Russ.)
20. Monakov A. A. Detector of a Moving Radar Target Based on the Hough Transform. Proc. of XX Intern. Conf. "Radar, Navigation and Communication". 2014, vol. 3, pp. 1584–1594. (In Russ.)
21. Carlson B. D., Evans E. D., Wilson S. L. Search Radar Detection and Track with the Hough Transform, Part III: Detection Performance with Binary Integration. IEEE Trans. on Aerospace and Electronic Systems. 1994, vol. 30, no. 1, p. 116–125. doi:10.1109/7.250412
22. Wang F., Lu W., Shi C., Zhou J. Prior KnowledgeBased Statistical Estimation of Linear False Tracks in Uniform Distributed Clutter. IET Radar Sonar & Navigation. 2021, vol. 15, no. 10, pp. 1237–1246. doi:10.1049/rsn2.12107
23. Imiya A., Hada T., Tatara K. The Hough Transform without the Accumulators. Structural, Syntactic, and Statistical Pattern Recognition. Lecture Notes in Computer Science. 2002, vol. 2396, pp. 823–832. doi:10.1007/3-540-70659-3_87
24. Yu X., Leong H. W., Xu C., Tian Q. A Robust and Accumulator-Free Ellipse Hough Transform. Proc. of the 12th ACM Intern. Conf. on Multimedia. New York, USA, 10–16 Oct. 2004, pp. 256–259. doi:10.1145/1027527.1027585
25. Kurniawan R., Fuadi Z., Adriman R. Accumulator-free Hough Transform for Sequence Collinear Points. Aceh Intern. J. of Science and Technology. 2021, pp. 74–83. doi:10.13170/aijst.10.2.20894
Review
For citations:
Monakov A.A. Track Detection Algorithm Based on Trace Correlation Using Hough Transform. Journal of the Russian Universities. Radioelectronics. 2023;26(2):65-77. (In Russ.) https://doi.org/10.32603/1993-8985-2023-26-2-65-77
Views:
387
Email this article 