Track Detection Algorithm Based on Trace Correlation Using Hough Transform

Introduction . Track detection is one of the main tasks to be solved in trajectory processing. This task can efficiently using Hough Transform. A track considered detected if number position measurements received a consecutive radar scans and falling into same cell parameter space (accumulator) has exceeded threshold. However, effective practical application transform requires sufficiently long time measurement. Under small given for detection, are also accumulated those accumulator cells where their traces intersect. Therefore, order detect true tracks, additional processing required distinguish measurement clusters from different targets based on geometric proximity. In addition, large amount memory computational operations maintenance significantly increase computation load processor. Aim To design simple false-detection resilient algorithm detecting tracks without processor memory. Materials methods proposed algorithm, construction followed by selection with largest passed through them replaced cross correlations clustering maximum similarity traces. Results Mathematical simulation scenario parameters selected paper confirmed accuracy all existing field view its efficiency conducting error free association target measurements. Conclusion was created transform. The does not require