The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existi...The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.展开更多
Trophic status of some freshwater lakes all over the world, including Russia, Byelorussia, Japan, Sweden and China, has been assessed. The research submitted is based on the approach developed by OECD （Organization E...Trophic status of some freshwater lakes all over the world, including Russia, Byelorussia, Japan, Sweden and China, has been assessed. The research submitted is based on the approach developed by OECD （Organization Economic Cooperation and Development）. Annual total phosphorus averages formed a classification system. A probability assumption for each water body to reach some given trophic status was taken into account. Probability distribution curves for the average lake phosphorus have been analytically approximated.展开更多
基金Supported by the National Key Fundamental Research & Development Program of China (2007CB11006)the Zhejiang Natural Science Foundation (R106745, Y1080422)
文摘The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.
文摘Trophic status of some freshwater lakes all over the world, including Russia, Byelorussia, Japan, Sweden and China, has been assessed. The research submitted is based on the approach developed by OECD （Organization Economic Cooperation and Development）. Annual total phosphorus averages formed a classification system. A probability assumption for each water body to reach some given trophic status was taken into account. Probability distribution curves for the average lake phosphorus have been analytically approximated.
