基于TBM双层融合架构的航路属性异常检测

doi:10.3969/j.issn.0372-2112.2017.03.011

电子学报 ›› 2017, Vol. 45 ›› Issue (3): 577-583.DOI: 10.3969/j.issn.0372-2112.2017.03.011

基于TBM双层融合架构的航路属性异常检测

王晓华^1,2, 邹杰², 李立^1,2, 梁彦^1,2

1. 西北工业大学自动化学院, 陕西西安 710072;
2. 光电控制技术重点实验室, 河南洛阳 471009

收稿日期:2015-07-09 修回日期:2015-11-20 出版日期:2017-03-25
- 通讯作者:
- 梁彦
- 作者简介:
- 王晓华 女,1986年3月出生.博士研究生.模式识别与智能系统专业,主要研究方向航迹规划、信息融合.E-mail:xiaohuawang311@sina.com;邹杰男,1977年1月出生.系统工程专业,高级工程师,光电控制技术重点实验室.主要研究方向为火控系统工程、智能控制、信息融合等技术研究.E-mail:zoujie@163.com;李立女,1991年1月出生于河南.硕士研究生.信息融合,证据推理.E-mail:lilynwpu@mail.nwpu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61135001，No.61374023，No.61374159）; 航空科学基金 (No.20125153）; 光电控制技术重点实验室和航空科学基金联合资助 (No.20125153027）

The Anomaly Detection Based on TBM Two-Level Fusion Architecture

WANG Xiao-hua^1,2, ZOU Jie², LI Li^1,2, LIANG Yan^1,2

1. School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China;
2. Key Laboratory of Electrooptical Control Technology, Luoyang, Henan 471009, China

Received:2015-07-09 Revised:2015-11-20 Online:2017-03-25 Published:2017-03-25

摘要/Abstract

摘要：

航路飞行目标的属性异常检测是确保及时发现飞行异常的关键问题.常用的概率框架需要受到先验信息的局限.可传递置信模型（Transferable Belief Model，TBM）不需要先验信息，能高效处理异质信息，但是传统的TBM无法处理时间上的不连续与不确定性，因此针对异常航路目标检测问题，将马尔可夫模型与TBM框架结合，建立了基于TBM的双层融合架构，实现了多特征融合航路属性异常检测.第一层是通过对多属性冲突信息的分析，实现对多特征的检测，并通过特征贡献度分析，对多特征信息进行打折后再融合；第二层是通过在时间序列上的指派融合，对比预测值和观测值差异，检测航路目标异常变化.仿真试验验证，在切换航路场景与偏离回归场景中，相较动态证据推理方法，本文方法具有更好的决策准确性与时间精确度.

关键词: 信息融合, 航迹关联, 异常信源, 决策理论

Abstract:

The track anomaly detection is the key issue to make sure flying anomaly detected in time for the route flight.Traditional probabilistic frameworks are always based on prior probabilities.Transferable belief model (TBM) theory can generalizes the Bayesian approach without prior probabilities and efficiently deal with heterogeneous data.However,the traditional TBM cannot deal with the discontinuity and uncertainty about the time.Considering the existence of unreliable evidence sources,an alternative anomaly detection method is proposed in the framework of transferable belief model (TBM) theory.A two-level architecture fusion system based on TBM is developed.The novelty of this work is that it can detect both unreliable evidence source and abnormal behavior of the targets within our architecture by using a temporal analysis and a new discounting coefficient through introducing the concept of contribution degrees of features.Detection of abnormal behavior is based on a prediction/observation process and the influence of the faulty sources is weakened through discounting coefficients.The simulations show the better accuracy of decision and precision of time compared with the dynamic evidence reasoning method.

Key words: information fusion, track association, anomaly source data, decision theory

中图分类号:

TN958

王晓华, 邹杰, 李立, 梁彦. 基于TBM双层融合架构的航路属性异常检测[J]. 电子学报, 2017, 45(3): 577-583.

WANG Xiao-hua, ZOU Jie, LI Li, LIANG Yan. The Anomaly Detection Based on TBM Two-Level Fusion Architecture[J]. Acta Electronica Sinica, 2017, 45(3): 577-583.

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基于TBM双层融合架构的航路属性异常检测

The Anomaly Detection Based on TBM Two-Level Fusion Architecture

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