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Multisensor Estimation Fusion on Statistical Manifold.

Xiangbing Chen1, Jie Zhou2

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Summary
This summary is machine-generated.

This study introduces novel sensor data fusion methods, treating estimates as probability densities on a Riemannian manifold. The proposed techniques effectively approximate an informative barycenter, outperforming existing approaches for distributed estimation.

Keywords:
Manhattan distancedistributed estimation fusionelliptical distributioninformation geometrylie algebra

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Area of Science:

  • Information Geometry
  • Distributed Estimation
  • Sensor Fusion

Background:

  • Characterizing local sensor estimates as posterior probability densities.
  • Utilizing an information-geometric viewpoint with Riemannian manifolds and Fisher metric.
  • Formulating fused density as an informative barycenter by minimizing geodesic distances.

Purpose of the Study:

  • Develop novel sensor data fusion methods for distributed systems.
  • Address challenges in fusing estimates from multiple sensors, especially with heavy-tailed noise.
  • Compare proposed methods against existing fusion techniques.

Main Methods:

  • Assuming multivariate elliptical distributions (MED) for posterior densities.
  • Developing two fusion methods using minimal Manhattan distance instead of geodesic distance.
  • Employing a robust fixed-point iterative algorithm and an explicit expression for fused covariance estimation.

Main Results:

  • Both methods achieve identical mean estimation fusion.
  • Demonstrated superior approximation of the informative barycenter compared to existing methods.
  • Validated performance in static target estimation with varying heavy-tailed noise levels.

Conclusions:

  • The proposed fusion methods offer improved performance in distributed estimation.
  • Effective application demonstrated in dynamic systems with heavy-tailed noise.
  • Provides robust and efficient solutions for sensor fusion challenges.