no code implementations • 31 Jan 2024 • Tiancheng Li, Haozhe Liang, Guchong Li, Jesús García Herrero, Quan Pan
This paper, the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking, addresses the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion.
no code implementations • 12 Mar 2023 • Tiancheng Li, Ruibo Yan, Kai Da, Hongqi Fan
This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need.
no code implementations • 21 Sep 2022 • Tiancheng Li
As a fundamental information fusion approach, the arithmetic average (AA) fusion has recently been investigated for various random finite set (RFS) filter fusion in the context of multi-sensor multi-target tracking.
no code implementations • 23 Apr 2022 • Tiancheng Li, Zheng Hu, ZhunGa Liu, Xiaoxu Wang
A multi-sensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises.
no code implementations • 20 Apr 2021 • Tiancheng Li, Yan Song, Hongqi Fan
This paper addresses the problem of real-time detection and tracking of a non-cooperative target in the challenging scenario with almost no a-priori information about target birth, death, dynamics and detection probability.
no code implementations • 12 Aug 2013 • Tiancheng Li, Shudong Sun, Tariq P. Sattar, Juan M. Corchado
We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices.