Correlation Evaluation and Fusion Method for Telemetry Data of Near Space Vehicles with Conflicting Evidence

ZHANG Shaojie; RONG Haijun; YANG Zhaoxu; HOU Kaizhen

doi:10.7652/xjtuxb202311010

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Correlation Evaluation and Fusion Method for Telemetry Data of Near Space Vehicles with Conflicting Evidence

更新时间：2025-12-08

- Correlation Evaluation and Fusion Method for Telemetry Data of Near Space Vehicles with Conflicting Evidence
- Vol. 57, Issue 11, Pages: 100-109(2023)
- 作者机构：
  
  1. 西安交通大学机械结构强度与振动国家重点实验室,西安,710049
  2. 西安交通大学陕西省先进飞行器服役环境与控制重点实验室,西安,710049
  3. 西安交通大学航天航空学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202311010
  CLC： TK311.5
- Online First：10 November 2023，
  
  Published：2023
- 稿件说明：
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ZHANG Shaojie, RONG Haijun, YANG Zhaoxu, et al. Correlation Evaluation and Fusion Method for Telemetry Data of Near Space Vehicles with Conflicting Evidence[J]. 2023, 57(11): 100-109.
DOI：

ZHANG Shaojie, RONG Haijun, YANG Zhaoxu, et al. Correlation Evaluation and Fusion Method for Telemetry Data of Near Space Vehicles with Conflicting Evidence[J]. 2023, 57(11): 100-109. DOI： 10.7652/xjtuxb202311010.

摘要

为了解决单一的相关性分析方法在分析临近空间飞行器遥测数据时存在局限性以及证据冲突问题

在相关系数分析评价与优势组合的基础上

提出了基于支持因子的证据理论融合算法。首先分别利用Pearson相关系数、Spearman相关系数与距离相关系数对遥测数据进行相关性评价分析

表明3种相关系数可优势互补。其次

建立了基于支持因子的证据理论融合算法

实现证据的冲突基本概率赋值函数分配

避免Dempster-Shafer证据理论(D-S证据理论)的一票否决和合成规则失效问题。最后

利用3种相关系数构造相关性证据并开展遥测数据相关性分析实验。结果表明:基于支持因子的证据理论融合方法能使Pearson相关系数、Spearman相关系数与距离相关系数的证据融合更加合理; 在证据冲突大的情况下

相比相容系数的证据理论融合方法

相关概率计算结果准确度提高约6.55%

能更有效地处理证据冲突问题。

Abstract

This paper proposes a support factor-based evidence theory fusion algorithm to address the limitations and evidence conflicts of a single correlation analysis method in analyzing telemetry data from nearby space vehicles. Firstly

the Pearson correlation coefficient

Spearman correlation coefficient

and distance correlation coefficient are used to evaluate and analyze the correlation of telemetry data. The results show that the three correlation coefficients complement each other's advantages. Secondly

an evidence theory fusion algorithm based on support factors is established to achieve the assignment of basic probability assignment functions for evidence conflicts

thus avoiding the one-vote veto and composite rule invalidation issues of Dempster-Shafer evidence theory(D-S evidence theory). Finally

the Pearson correlation coefficient

Spearman correlation coefficient

and distance correlation coefficient are used to make evidence and an experiment is conducted to analyze the correlation of telemetry data. The results show that the proposed algorithm can ensure the reasonability of the fusion from the Pearson coefficient

Spearman coefficient

and distance coefficient. In case of high evidence conflicts

the accuracy of correlation probability calculation is improved by about 6.55% compared to the evidence theory fusion method based on compatibility ratio. These findings indicate that the proposed algorithm is more effective in handling evidence conflict problems.

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references

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