A review of efficient techniques and applications of kernel density in particle filtering framework
บทความในวารสาร
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Kham S.L.; Chamnongthai K.; Aunsri N.
ผู้เผยแพร่: Elsevier
ปีที่เผยแพร่ (ค.ศ.): 2025
Volume number: 122
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
Particle filter (PF), a sequential Monte Carlo (SMC) paradigm, is a powerful technique for managing non-linear dynamics and non-Gaussian noise, which is complex or irregular noise in measurements as well as for predicting the unobserved real state of systems. The standard PF algorithm comprises three primary stages: particle prediction, probabilities or weights adjustment, and resampling to regenerate particles. Particle degeneracy, in which most particles gain insignificant weights over multiple iterations, and particle impoverishment, in which the algorithm's efficacy is reduced due to a lack of particle diversity, are two major issues in PF. To enhance the performance of posterior probability density function (PDF) estimation in dynamic systems, this work explores the integration of kernel density estimation (KDE) into PF. KDE, a non-parametric statistical smoothing method, is included into PF to smooth the resampled particles following each time step to overcome these difficulties. Thus, this paper investigates KDE-integration methods to improve estimation performance of the PF. This results in a more resilient PF algorithm by maintaining the diversity of particles and avoiding the loss of important information. In this process, choosing the right kernel bandwidth is essential because it controls the smoothness of the distribution. The KDE-PF approach demonstrates higher accuracy and dependability in challenging real-world tracking problems. This method offers useful insights for both research and industrial innovation, with potential applications across various sectors. © 2025 Elsevier B.V.
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