Heterogeneous Fusion of an IMM Track with Measurements from Different Sources

The problem addressed in this paper is to track a maneuvering target from multiple sources, namely the source
$\mathbf{S}_{\mathrm{A}}$
and source
$\mathrm{S}_{\mathrm{B}}$
. Source
$\mathbf{S}_{\mathrm{A}}$
sends a target track generated by its local Interacting Multiple Model (IMM) estimator to the Fusion Center (FC), whereas
$\mathrm{S}_{\mathrm{B}}$
provides its measurements to the FC. The objective of the FC is to perform heterogeneous fusion of the
$\mathbf{S}_{\mathrm{A}}$
IMM track and
$\mathrm{S}_{\mathrm{B}}$
measurements. This problem cannot be solved by the existing information decorrelation algorithm [6], which works only when a (single model) Kalman filter is used in the
$\mathrm{S}_{\mathrm{A}}$
local tracker (suitable for tracking a non-maneuvering target). To cope with the commonly used IMM estimator (assumed deployed in
$\mathrm{S}_{\mathrm{A}}$
), this paper will introduce the cumulated measurement information concept, and develop the IMM Cumulated information Fusion (IMM-CFusion) algorithm. It evaluates the cumulated
$\mathbf{S}_{\mathrm{B}}$
measurement in the information space with multiple models, and performs fusion with the
$\mathbf{S}_{\mathrm{A}}$
IMM track state (without knowing
$\mathbf{S}_{\mathrm{A}}$
IMM “inside information” on model matched state estimates). Simulation tests are conducted to demonstrate the performance of the IMM-CFusion.