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Time Series Data Fusion Based on Evidence Theory and OWA Operator.

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  • 1School of Computer and Information Science, Southwest University, Chongqing 400715, China. lg645187984@email.swu.edu.cn.

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Summary

This study introduces a new time series data fusion method using an improved ordered weighted aggregation operator (OWA) to reduce the impact of long sensor data intervals. The enhanced approach improves target recognition accuracy in real-world applications.

Keywords:
OWAcredibility decay modeldata fusiontarget recognitiontime series

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Area of Science:

  • Computer Science
  • Signal Processing
  • Artificial Intelligence

Background:

  • Time series data fusion is critical for applications like sensor-based target recognition.
  • Existing credibility decay models (CDM) struggle with long time intervals between sensor data points.
  • This limitation impacts the efficiency and accuracy of data fusion in dynamic environments.

Purpose of the Study:

  • To develop a novel data fusion method that mitigates the negative effects of extended time intervals between sensor readings.
  • To enhance the performance of target recognition systems relying on time series data fusion.
  • To improve the robustness of data fusion models in scenarios with intermittent data availability.

Main Methods:

  • A new data fusion method is proposed, building upon the ordered weighted aggregation operator (OWA).
  • The method incorporates a Q function within the OWA framework to better manage temporal data discrepancies.
  • The effectiveness of the proposed method was evaluated using time series data for target recognition.

Main Results:

  • The proposed method significantly reduces the impact of long time intervals on the final fused data.
  • Experimental results demonstrate improved accuracy in target recognition tasks compared to existing methods.
  • The enhanced OWA approach shows greater efficiency and reliability in handling time series data fusion.

Conclusions:

  • The novel data fusion method effectively addresses the limitations of traditional models concerning time intervals.
  • The improved OWA operator offers a promising solution for robust time series data fusion.
  • This research has significant implications for advancing sensor data fusion in target recognition and other critical applications.