An adaptive Gaussian sum algorithm for radar tracking
Signal Processing
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
PDF target detection and tracking
Signal Processing
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
Bayesian Filtering With Random Finite Set Observations
IEEE Transactions on Signal Processing
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
Derivation and evaluation of improved tracking filter for use in dense multitarget environments
IEEE Transactions on Information Theory
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This paper presents a Bayesian algorithm for single target tracking using state mixture model theory. Compared with the existing approaches, the proposed algorithm aims at deriving the likelihood function of all measurements. Given this, an analytic Bayesian algorithm is further proposed. Moreover, under linear Gaussian assumptions on the dynamics and measurement model, a closed-form solution is proposed. Our study demonstrates the effectiveness of the proposed method in single target detection and tracking.