کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1712898 | 1013209 | 2008 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modified joint probabilistic data association with classification-aided for multitarget tracking*
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Systems Engineering and Electronics - Volume 19, Issue 3, June 2008, Pages 434-439
Journal: Journal of Systems Engineering and Electronics - Volume 19, Issue 3, June 2008, Pages 434-439
نویسندگان
Ba Hongxin, Cao Lei, He Xinyi, Cheng Qun,