On Pooling-Based Track Fusion Strategies : Harmonic Mean Density

TL;DR

This paper introduces a novel pooling-based fusion strategy using harmonic mean density for distributed sensor fusion, improving accuracy and consistency over traditional methods in tracking scenarios.

Contribution

It develops a harmonic mean density fusion method compatible with Gaussian mixtures, offering a simpler alternative to covariance intersection with better performance.

Findings

HMD fusion outperforms conservative strategies in RMSE

Method handles Gaussian and mixture densities easily

Simulations confirm improved accuracy and consistency

Abstract

In a distributed sensor fusion architecture, using standard Kalman filter (naive fusion) can lead to degraded results as track correlations are ignored and conservative fusion strategies are employed as a sub-optimal alternative to the problem. Since, Gaussian mixtures provide a flexible means of modeling any density, therefore fusion strategies suitable for use with Gaussian mixtures are needed. While the generalized covariance intersection (CI) provides a means to fuse Gaussian mixtures, the procedure is cumbersome and requires evaluating a non-integer power of the mixture density. In this paper, we develop a pooling-based fusion strategy using the harmonic mean density (HMD) interpolation of local densities and show that the proposed method can handle both Gaussian and mixture densities without much changes to the framework. Mathematical properties of the proposed fusion strategy are studied and simulated on 2D and 3D maneuvering target tracking scenarios. The simulations suggest that the proposed HMD fusion performs better than other conservative strategies in terms of root-mean-squared error while being consistent.