This paper presents the principles of data association algorithms and describes them using mathematical language. It then provides a detailed analysis of the application of the Joint Compatible Branch and Bound (JCBB) algorithm in the data association stage, proposing the DOJCBB data association algorithm. Firstly, to avoid excessive computational resource waste caused by numerous environmental features in complex environments, the data association is restricted to a localized association region. Secondly, to address the error accumulation resulting from uncertainties during robot operation, a threshold constant dynamic adaptation is introduced. Lastly, considering the issues of multiple hypotheses with the maximum number of associations and false associations in the data association process of the JCBB algorithm, corresponding optimization criteria are designed for improvement. The effectiveness of the proposed improved DOJCBB data association algorithm is validated through comparative experiments on a simulation platform. 

Huiheng Suo, Bosi Wei, Qiang Hu, Jiingjia Pei, Xie Ma, Yujie Song, Bibo Yu, Xiushui Ma. Dynamic Optimization Data Association based on JCBB Algorithm. International Journal of Big Data Intelligent Technology (2023), Vol. 4, Issue 1: 53-67. https://doi.org/10.38007/IJBDIT.2023.040106.

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