Visible to the public Research on Nearest Neighbor Data Association Algorithm Based on Target “Dynamic” Monitoring Model

TitleResearch on Nearest Neighbor Data Association Algorithm Based on Target “Dynamic” Monitoring Model
Publication TypeConference Paper
Year of Publication2020
AuthorsHan, Z., Wang, F., Li, Z.
Conference Name2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
Date Publishedjun
Keywordsgate searching, Measurement, multi-hypothesis tracking, multi-target data association, multiple hypothesis tracking, multitarget data association, nearest neighbor data association, nearest neighbor search, nearest neighbour methods, NNDA, Predictive Metrics, pubcrawl, search problems, sensor fusion, target “dynamic” monitoring model, target updating, Targets Dynamic Monitoring Model, TDMM
AbstractIn order to solve the problem that the Nearest Neighbor Data Association (NNDA) algorithm cannot detect the “dynamic” change of the target, this paper proposes the nearest neighbor data association algorithm based on the Targets “Dynamic” Monitoring Model (TDMM). Firstly, the gate searching and updating of targets are completed based on TDMM, then the NNDA algorithm is utilized to achieve the data association of targets to realize track updating. Finally, the NNDA algorithm based on TDMM is realized by simulation. The experimental results show that the algorithm proposed can achieve “dynamic” monitoring in multi-target data association, and have more obvious advantages than Multiple Hypothesis Tracking (MHT) in timeliness and association performance.
DOI10.1109/ITNEC48623.2020.9085030
Citation Keyhan_research_2020