Multiobject fusion with minimum information loss
For researchers in distributed multitarget tracking, this work provides a principled fusion rule that reduces information loss compared to GCI, though the improvement is incremental.
The paper proposes a minimum weighted information loss (MWIL) fusion rule for multiobject densities, which is consistent with the linear opinion pool (LOP) but adapted to preserve the same distribution family for recursive Bayesian filtering. Simulations show improved tracking performance over generalized covariance intersection (GCI) in distributed multitarget tracking.
Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, the interest is in the fusion rule that dually minimizes the weighted information loss (WIL) and it turns out that such a fusion rule is consistent with the so-called linear opinion pool (LOP). However, the LOP cannot be directly applied to multiobject fusion since the resulting fused multiobject density (FMD), in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in the context of Bayesian multiobject filtering. In order to overcome such a difficulty, the principle of minimizing WIL is further exploited in that the optimal FMD in the same family of the local ones is looked for. Implementation issues relative to the proposed minimum WIL (MWIL) fusion rule are discussed. Finally, the performance of the MWIL rule is assessed via simulation experiments concerning distributed multitarget tracking over a wireless sensor network.