Harmonic mean density fusion in distributed tracking: Performance and comparison

Abstract A distributed sensor fusion architecture is preferred in a real target‐tracking scenario as compared to a centralised scheme since it provides many practical advantages in terms of computation load, communication bandwidth, fault‐tolerance, and scalability. In multi‐sensor target‐tracking literature, such systems are better known by the pseudonym—track fusion, since processed tracks are fused instead of raw measurements. A fundamental problem, however, in such systems is the presence of unknown correlations between the tracks, which renders a standard Kalman filter (naive fusion) useless. A widely accepted solution is covariance intersection (CI) which provides near‐optimal estimates but at the cost of a conservative covariance. Thus, the estimates are pessimistic, which might result in a delayed error convergence. Also, fusion of Gaussian mixture densities is an active area of research where standard methods of track fusion cannot be used. In this article, the harmonic mean density (HMD)‐based fusion is discussed, which seems to handle both of these issues. The authors present insights on HMD fusion and prove that the method is a result of minimising average Pearson divergence. This article also provides an alternative and easy implementation based on an importance‐sampling‐like method without the requirement of a proposal density. Similarity of HMD with inverse covariance intersection is an interesting find, and has been discussed in detail.Results: based on a real‐world multi‐target multi‐sensor scenario show that the proposed approach converges quickly than existing track fusion algorithms while also being consistent, as evident from the normalised estimation‐error squared (NEES) plots.