Tracking and data association
Mathematics of Data Fusion
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
Localization of multiple emitters based on the sequential PHD filter
Signal Processing
Adaptive fault-tolerant tracking control of near-space vehicle using takagi-sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
Multitarget miss distance via optimal assignment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
Robust Output Feedback Tracking Control for Time-Delay Nonlinear Systems Using Neural Network
IEEE Transactions on Neural Networks
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Multi-target Bayesian filter in the framework of finite set statistics (FISST) and its approximations, including probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter, are elegant methods for multi-target tracking by jointly estimating the number of targets and their states from a sequence of noisy and cluttered observation sets. PHD filter and CPHD filter can deal with the tracking scenario involving the surviving targets, the spawned targets, and the spontaneous births. One of the limitations of PHD and CPHD filter is that it is assumed that intensities of spontaneous birth targets are known at the initialization stage. To address the problem, a track initiation technique is proposed to detect the position unknown birth targets and is hybridized with PHD and CPHD filter. Once new targets are detected, the position estimates are employed to form intensities of spontaneous births for starting PHD and CPHD filter. Simulation results demonstrate that the proposed tracker can adaptively and efficiently track multiple targets especially in scenarios with birth targets of unknown position, which the PHD and CPHD filter are unable to do on their own.