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Passive multisensor multitarget feature-aided unconstrained tracking: a geometric perspective

Novel targets-to-sensors' geometry based performance measure, bootstrap estimation algorithm and feature aided association are described for the passive multisensor multitarget data association, position and velocity measurement estimation and coupled unconstrained association/tracking problem. The approach reduces computational complexity and ghost targets and provides dynamically changing geometry-dependent online estimation of both the target's velocity measurements and the computation of the associated correlated position and velocity measurement noise covariance matrix (R-matrix). Sequences of these estimates, along with position measurement estimate sequences, serve as inputs to a Kalman filter tracker, associating/forming/de-ghosting and maintaining tracks in Cartesian coordinates. Based on state estimates of targets, a relative geometric measure-of-merit is used to select sensors for optimum tracking performance. Previous approaches to the passive multisensor-multitarget position state estimation problem did not incorporate feature aided gating and association, and used R-matrix formulations, based on Cramer-Rao lower bound computations, which do not explicitly exploit the effects of the changing geometry. An overall system construct embodying the above features is described. The tracking performance efficacy of the new algorithmic system is demonstrated in a simulated self-organizing network of synchronized acoustic Unattended Ground Sensors (UGS) using sequences of bearing measurement sets from triplets of UGS.