scholarly journals Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 569
Author(s):  
Peng Wang ◽  
Ge Li ◽  
Yong Peng ◽  
Rusheng Ju
Keyword(s):  
Parameter Estimation ◽  
Sequential Monte Carlo ◽  
Stochastic Systems ◽  
Measurement Model ◽  
Unknown Parameters ◽  
Bayesian Parameter Estimation ◽  
Probability Hypothesis Density ◽  
Estimation Algorithms ◽  
Random Finite Set ◽  
Finite Set

Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation algorithms. It can estimate the unknown parameters of the stochastic system which consists of a varying number of constituent elements by using the measurements disturbed by false detections, missed detections and noises. The models used for parameter estimation are constructed by using random finite set. Based on the proposed system model and measurement model, the key principles and formula derivation of the proposed algorithm are detailed. Then, the implementation of the algorithm is presented by using sequential Monte Carlo based Probability Hypothesis Density (PHD) filter and simulated tempering based importance sampling. Finally, the experiments of systematic errors estimation of multiple sensors are provided to prove the main advantages of the proposed algorithm. The sensitivity analysis is carried out to further study the mechanism of the algorithm. The experimental results verify the superiority of the proposed algorithm.

scholarly journals Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4416 ◽  
Author(s):  
Defu Jiang ◽  
Ming Liu ◽  
Yiyue Gao ◽  
Yang Gao ◽  
Wei Fu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Bayesian Filtering ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Bayesian Formulation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Bernoulli Filter

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

scholarly journals Photovoltaic models parameter estimation via an enhanced Rao-1 algorithm

2021 ◽  
Vol 19 (2) ◽  
pp. 1128-1153
Author(s):  
Junhua Ku ◽  
◽  
Shuijia Li ◽  
Wenyin Gong ◽  
Keyword(s):  
Parameter Estimation ◽  
Population Size ◽  
Evolution Operator ◽  
Evolutionary Process ◽  
Simulated Data ◽  
Local Optimum ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Arduous Task ◽  
Important Position

<abstract><p>The accuracy of unknown parameters determines the accuracy of photovoltaic (PV) models that occupy an important position in the PV power generation system. Due to the complexity of the equation equivalent of PV models, estimating the parameters of the PV model is still an arduous task. In order to accurately and reliably estimate the unknown parameters in PV models, in this paper, an enhanced Rao-1 algorithm is proposed. The main point of enhancement lies in i) a repaired evolution operator is presented; ii) to prevent the Rao-1 algorithm from falling into a local optimum, a new evolution operator is developed; iii) in order to enable population size to change adaptively with the evolutionary process, the population size linear reduction strategy is employed. To verify the validity of ERao-1 algorithm, we embark a study on parameter estimation of three different PV models. Experimental results show that the proposed ERao-1 algorithm performs better than existing parameter estimation algorithms in terms of the accuracy and reliability, especially for the double diode model with RMSE 9.8248E-04, three diode model with RMSE 9.8257E-04 for the R.T.C France silicon cell, and 2.4251E-03 for the three diode model of Photowatt- PWP201 cell. In addition, the fitting curve of the simulated data and the measured data also shows the accuracy of the estimated parameters.</p></abstract>

scholarly journals Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

2017 ◽  
Vol 63 (3) ◽  
pp. 247-254 ◽  
Author(s):  
Huanqing Zhang ◽  
Hongwei Ge ◽  
Jinlong Yang
Keyword(s):  
State Estimation ◽  
Analytic Solution ◽  
Gaussian Mixture ◽  
Multiple Target ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Estimation Precision ◽  
Simulation Results

AbstractProbability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

scholarly journals Random Finite Set Based Data Assimilation for Dynamic Data Driven Simulation of Maritime Pirate Activity

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Peng Wang ◽  
Ge Li ◽  
Rusheng Ju ◽  
Yong Peng
Keyword(s):  
Data Assimilation ◽  
Rapid Development ◽  
Measurement Model ◽  
Data Driven ◽  
Agent Based Simulation ◽  
Dynamic Data ◽  
Agent Based ◽  
Random Finite Set ◽  
Finite Set ◽  
Online Measurements

Maritime piracy is posing a genuine threat to maritime transport. The main purpose of simulation is to predict the behaviors of many actual systems, and it has been successfully applied in many fields. But the application of simulation in the maritime domain is still scarce. The rapid development of network and measurement technologies brings about higher accuracy and better availability of online measurements. This makes the simulation paradigm named as dynamic data driven simulation increasingly popular. It can assimilate the online measurements into the running simulation models and ensure much more accurate prediction of the complex systems under study. In this paper, we study how to utilize the online measurements in the agent based simulation of the maritime pirate activity. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the conventional vectors based data assimilation algorithms. The random finite set based general data model, measurement model, and simulation model are introduced to support the proposed algorithm. The details of the proposed algorithm are presented in the context of agent based simulation of maritime pirate activity. Two groups of experiments are used to practically prove the effectiveness and superiority of the proposed algorithm.

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