Nonlinear multi-object state estimation algorithms for target tracking, communications and biomedical engineering in the presence of model uncertainties

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The problem of multisensor-multitarget tracking involves the utilization of sensor detections to estimate the state (e.g., position and velocity) of an unknown number of mobile or stationary objects (the targets) with possibly different dynamics. Multi-object tracking has applications in defence (e.g., air defence), communications (e.g., mobile user tracking), signal processing (e.g., signal source separation) and biomedical engineering (e.g., EEG signal analysis). Standard techniques for state estimation assume linear state evolution and state-to-measurement equations. They also assume Gaussian noise in the measurement process and state dynamics. In the presence of nonlinearities and non-Gaussian noise, the performance of classical methods degrades, often to the point of complete breakdown. Nonlinear algorithms are the alternative to the classical Kalman filter and its linearized variants for handling nonlinear state/measurement models and/or non-Gaussian noise. In particular, Monte Carlo (random) sampling based methods like particle filtering, finite set statistics tracking and joint multitarget probability density tracking have become some of the possible ways to approximate nonlinear filtering, the optimal form of which is computationally infeasible.