Results 161 to 170 of about 8,181 (194)
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Improved unscented particle filter for nonlinear bayesian estimation
2007 10th International Conference on Information Fusion, 2007The idea of particle filter is to represent probability density function (PDF) of nonlinear/non-Gaussian system by a set of random samples. One of the key issue of particle filter is the proposal distribution. In this paper, the iterated unscented Kalman filter (IUKF) is used to generate the proposal distribution for particle filter.
Wenyan Guo, Chongzhao Han, Ming Lei
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Exactly Rao-Blackwellized unscented particle filters for SLAM
2011 IEEE International Conference on Robotics and Automation, 2011This paper addresses the limitation of the conventional Rao-Blackwellized unscented particle filters. The problem is on the usage of the overconfident optimal proposal distribution caused by perfect map assumption, so that predictive robot poses are sampled from the underestimated error covariance in the particle filtering process.
Chanki Kim +2 more
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Particle-Based Tuning of the Unscented Kalman Filter
Journal of Control, Automation and Electrical Systems, 2015The unscented Kalman filter (UKF) is one of the most used approximate solutions to the problem of nonlinear filtering. It is relatively easy to implement, and it produces better state estimates than the extended Kalman filter, especially when the nonlinearities of the dynamic system are significant.
Leonardo Azevedo Scardua +1 more
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Research on robust unscented regularized particle filtering
2010 IEEE International Conference on Information Theory and Information Security, 2010In nonlinear and non-Gaussian systems, particle filtering is effective but it is difficult to select the importance distribution function and diverges more greatly. Aiming at this problem, the paper represents robust unscented regularized particle filtering to improve the performance of filtering.
null Li Xue +2 more
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Conjugate Unscented Transform based Multiple Model Particle Filter
2021 American Control Conference (ACC), 2021In this paper we develop the Multiple Model Particle Filter (MMPF) for nonlinear systems. The particle filter is used to estimate the conditional probability for the modes while the Conjugate Unscented Transform (CUT) based Kalman filter is used to estimate the dynamical state of the system.
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A New Particle Swarm Optimization Based Unscented Particle Filtering
2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009A new filtering algorithm - PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which means that these particles will move to the region with
Chunhe Song +3 more
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Particle Swarm Optimized Unscented Particle Filter for Target Tracking
2009 2nd International Congress on Image and Signal Processing, 2009In this paper, a novel particle swarm optimized (PSO) unscented particle filter (PSO-UPF) algorithm is proposed for target tracking. Unscented particle filter (UPF) can obtain the better sequential importance sampling than the traditional PF algorithm. Then we use PSO to optimize the state equation of UPF.
Shuying Yang, Qin Ma, Wenjuan Huang
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Square Root Unscented Particle Filtering for Grid Mapping
2009In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [13].
Simone Zandara, Ann E. Nicholson
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Unscented particle filter for delayed car-following models estimation
2006 IEEE Intelligent Transportation Systems Conference, 2006Microscopic simulation models have become widely applied tools in traffic engineering. Nevertheless, parameter identification remains a difficult task. This is for one caused by the fact that parameters are generally not directly observable from common traffic data.
Serge P. Hoogendoorn +3 more
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Conflict Detection Based on Improved Unscented Particle Filter
2011With increasing air traffic flow, the increasingly complex air traffic situation has raised possibility of conflicts, which requires higher timeliness and accuracy for conflict detection. Based on the rapidly growing air traffic control technologies, such as radars with excellent accuracy, an improved unscented particle filter (MUPF) algorithm is ...
Lianzhi Yu, Shilei Zhang, Xiaofei Zhu
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