.png){kind=icon}
返回
Context and intent enhanced target tracking
.png){kind=icon}
OA
AI
DOI：10.1016/j.dsp.2025.105836.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Exploitation of contextual knowledge has recently emerged as a promising approach to increase the performance of Information Fusion systems. Despite pioneering efforts in context assisted target tracking, the realm is still in its infancy, as the frameworks combining context reasoning with target tracking are not abundant. We here postulate that, in addition to physical constraints, such as the road network, knowledge of common patterns that targets pursue can significantly improve tracking accuracy and continuity. In the presented approach, we address the problem of tracking ground targets in complex urban environments, which generally poses a challenge to modern airborne surveillance systems. A target's actions are modeled as a Markov chain with relevant context defining transition and emission probabilities. Target's kinematics are estimated by the Interacting Multiple Models (IMM) filter that estimates the mode transition probability matrix (TPM) at each recursion step. The TPM posterior is computed by a Quasi-Bayesian estimator conditioned on the prior and the likelihood originating from target's measurements and the context. Through extensive simulations, we demonstrate that incorporating contextual information into TPM estimation significantly improves the filtering performance compared to both the IMM filter with a fixed TPM and adaptive TPM estimation without considering contextual information.
Keyword:
Tracking
Context
Machine learning
Decision support
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
D
IF:
3
论文数:
771
被引数:
0
机构
引用论文
A variational marginalized particle filter for jump Markov nonlinear systems with unknown transition probabilities具有未知转移概率的跳跃马尔可夫非线性系统的变分边际粒子滤波器.png)
SIGNAL PROCESSINGOAAI
SIGNAL PROCESSINGIF3.6
OAAI分享收藏An Interacting Multiple Model for Trajectory Prediction of Intelligent Vehicles in Typical Road Traffic Scenario典型道路交通场景下智能车辆轨迹预测的交互多模型
PREAI
分享收藏Deep Learning Aided State Estimation for Guarded Semi-Markov Switching Systems With Soft Constraints
PREAI
分享收藏
查看更多内容