Danaee, M. R., Behnia, F.. (1396). Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters. نشریه مهندسی عمران امیرکبیر, 49(1), 95-106. doi: 10.22060/eej.2017.12053.5029
M. R. Danaee; F. Behnia. "Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters". نشریه مهندسی عمران امیرکبیر, 49, 1, 1396, 95-106. doi: 10.22060/eej.2017.12053.5029
Danaee, M. R., Behnia, F.. (1396). 'Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters', نشریه مهندسی عمران امیرکبیر, 49(1), pp. 95-106. doi: 10.22060/eej.2017.12053.5029
Danaee, M. R., Behnia, F.. Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters. نشریه مهندسی عمران امیرکبیر, 1396; 49(1): 95-106. doi: 10.22060/eej.2017.12053.5029

Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters

AUT Journal of Electrical Engineering
مقاله 11، دوره 49، شماره 1، 2017، صفحه 95-106    اصل مقاله (4.19 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2017.12053.5029
نویسندگان
M. R. Danaee* 1؛ F. Behnia2
1Electrical Engineering Department, Imam Hossein Comprehensive University (IHCU), Tehran, Iran
2Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
چکیده
The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new
approaches to enhance the Sequential Monte Carlo (SMC) implementation of the PHD filter, current SMC implementation for the CPHD filter is limited to choose only state transition density as a proposal distribution. In this paper, we propose an auxiliary particle implementation of the CPHD filter by estimating the linear functionals in the elementary symmetric functions based on the unscented transform (UT). Numerical simulation results indicate that our proposed algorithm out performs both the SMC-CPHD filter and the auxiliary particle implementation of the PHD filter in difficult situations with high clutter. We also compare our proposed algorithm with its counterparts in terms of other metrics, such as run times and sensitivity to new target appearance.
کلیدواژه‌ها
Multi-target tracking؛ Cardinalized probability hypothesis density filter؛ unscented auxiliary particle filter؛ linear functional؛ potential functions
مراجع
آمار
تعداد مشاهده مقاله: 1,558
تعداد دریافت فایل اصل مقاله: 931
پیوندهای مفید
آمار
تعداد نشریات9
تعداد شماره‌ها455
تعداد مقالات5,771
تعداد مشاهده مقاله8,375,284
تعداد دریافت فایل اصل مقاله6,933,834