卡尔曼滤波器
跟踪(教育)
计算机科学
人工智能
运动学
人工神经网络
自回归模型
计算机视觉
变压器
动态贝叶斯网络
跟踪系统
贝叶斯概率
工程类
数学
心理学
教育学
电压
电气工程
物理
经典力学
计量经济学
作者
Hui Chen,Binchao Bian,Feng Lian,Wenxu Zhang
出处
期刊:Measurement
[Elsevier]
日期:2025-01-01
卷期号:240: 115474-115474
标识
DOI:10.1016/j.measurement.2024.115474
摘要
This research introduces a new approach to address the challenges related to inaccuracies in shape estimation and motion state tracking during the tracking of maneuvering extended targets. The method combines a novel neural network model with a cubature Kalman filter. Initially, the target's shape variation is represented using a second-order autoregressive model, and Bayesian filtering is employed for target contour estimation in static environments. Subsequently, the kinematic state tracking of maneuvering targets is enhanced by modifying a neural network model based on Transformer architecture. Through the effective integration of these techniques, precise tracking of maneuvering extended targets is achieved, facilitating accurate estimation of irregular contour information while tracking the targets' motion state in real-time. The efficacy of the proposed approach is further confirmed through various simulation scenarios, underscoring its suitability for tracking maneuvering extended targets.
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