Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Particle filters for maneuvering target tracking problem
Signal Processing
Functional sampling density design for particle filters
Signal Processing
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
A new evolutionary particle filter for the prevention of sample impoverishment
IEEE Transactions on Evolutionary Computation
Adaptive sensor fault detection and identification using particle filter algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Interacting MCMC particle filter for tracking maneuvering target
Digital Signal Processing
Particle filter enhancement of speech spectral amplitudes
IEEE Transactions on Audio, Speech, and Language Processing
Risk-Sensitive Particle Filters for Mitigating Sample Impoverishment
IEEE Transactions on Signal Processing - Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
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Particle filters provide a general numerical tool to deal with the nonlinear/non-Gaussian filtering problems. However, it is still a challenging problem to design a good proposal distribution to generate high-quality particles. In this paper, we present the concept of hybrid proposal distribution (HPD) defined by the weighted sum of multiple basic proposal distributions (BPDs), transform the adaptive particle filtering into the online weight optimization, and, as a result, propose the framework of particle filter with multimode sampling strategy. Compared with traditional sampling strategies, multimode sampling strategy is more flexible to accommodate the time-varying system characteristics. To demonstrate the efficiency of the proposed framework, a particle filter with HPD consisting of two BPDs is designed, where one BPD is the transition density and the other, first proposed in this paper, is defined by an updated system equation. The numerical simulation with two examples shows that the proposed filter outperforms the extended Kalman filter, the unscented Kalman filter, the standard particle filter and the unscented Kalman particle filter.