Track-To-Track Association For Fusion of Dimension-Reduced Estimates

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Earlier work on target tracking in a dimension-reduced configuration has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. This problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.

Decentralized State Estimation In A Dimension-Reduced Linear Regression

Decentralized state estimation in a communication constrained sensor network is considered. To reduce the communication load only dimension-reduced estimates are exchanged between the networking agents. The considered dimension-reduction is restricted to be a linear mapping from a higher-dimensional space to a lower-dimensional space. The optimal, in the mean square error sense, linear mapping depends on the particular estimation method used. Several dimension-reducing algorithms are therefore proposed, where each algorithm corresponds to a commonly applied decentralized estimation method. All except one of the algorithms are shown to be optimal. For the remaining algorithm we provide a convergence analysis where it is theoretically shown that this algorithm converges to a stationary point and numerically shown that the convergence rate is fast. A message encoding solution is proposed that allows for efficient communication when using the proposed dimension-reduction techniques. Applicability of the different algorithms is illustrated by a numerical evaluation.