This article addresses the issue of parameter estimation in linear system in the presence of Gaussian noises, under which the random number searching algorithm (LJ (Luus and Jaakola) algorithm) is combined with the Ra...
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This article addresses the issue of parameter estimation in linear system in the presence of Gaussian noises, under which the random number searching algorithm (LJ (Luus and Jaakola) algorithm) is combined with the Rao-Blackwellised particlefilter (RBPF) algorithm. This yields the so-called RBPF algorithm based on LJ (RBPF-LJ). Unlike the mature alternatives of generic particlefilter, the parameter particles of RBPF-LJ are set as random numbers that search in the parameter value scope, which is regulated based on the estimation result to track the changes of the unknown parameter. The contrasting simulations show that the proposed RBPF-LJ outperform the RBPF as well as the particlefilter based on kernel smoothing contraction algorithm on the estimation of the dynamically linear or nonlinear parameter and it can obtain the similar estimation results on the static parameter if some coefficients are regulated.
Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor base...
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Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particlefilter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particlefilter localisation algorithm.
作者：
Kager, JulianHerwig, ChristophStelzer, Ines ViktoriaTU Wien
Inst Chem Environm & Biol Engn Gumpendorfer Str 1a A-1060 Vienna Austria TU Wien
Christian Doppler Lab Mech & Physiol Methods Impr Gumpendorfer Str 1a A-1060 Vienna Austria Med Univ Vienna
Ctr Publ Hlth Dept Hlth Econ Kinderspitalgasse 15 A-1090 Vienna Austria Gen Hosp Vienna
Ludwig Boltzmann Inst Appl Diagnost Wahringer Gurtel 18-20 A-1090 Vienna Austria
Real time monitoring of physiological characteristics during a cultivation process is of great importance in the pharmaceutical industry. Measuring biomass, product, substrate and precursor concentrations continuously...
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Real time monitoring of physiological characteristics during a cultivation process is of great importance in the pharmaceutical industry. Measuring biomass, product, substrate and precursor concentrations continuously however is limited due to time-consuming laboratory analysis or expensive and hard-to-handle devices. In this work, a particle filter algorithm for estimating these difficult-to-measure process states in a Penicillium chrysogenum fed-batch cultivation is presented. The implemented particlefilter represents a new algorithmic framework, combining several already existing methods and techniques for state estimation. It is based on nonlinear process and measurement models and takes into account both online measurements for state estimation and time delayed offline measurements, ensuring the observability of the considered system and being essential for the adaptation of dynamic model parameters. The application on real experimental data showed the convincing performance of the algorithm, estimating biomass, precursor and product concentration, as well as the specific growth rate, requiring standard measurements only. Furthermore, the positive effect of parameter estimation with respect to estimation quality was analyzed and the effect of the time delay was highlighted exemplarily. Despite of being computationally expensive due to time delayed data, the algorithm can be considered as an alternative monitoring strategy for industrial applications. Thus, it can be used further for process understanding and control. (C) 2017 Elsevier Ltd. All rights reserved.
In smart city development, the prediction of bus arrival time is a popular research issue, which often uses GPS data and other related bus data to conduct collaborative data analysis. It is of great importance for imp...
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ISBN:
(纸本)9781728103501
In smart city development, the prediction of bus arrival time is a popular research issue, which often uses GPS data and other related bus data to conduct collaborative data analysis. It is of great importance for improving the public transportation services. But the accuracy and the efficiency of bus arrival time prediction is still the major obstacles. In this paper, an optimized particle-filtering algorithm is used to establish a bus arrival time prediction model. To better solve the problem of prediction error and particle optimization in the process of using particle filter algorithms, the prediction model is improved by introducing the latest bus speed for collaborative data analysis, which improves the accuracy of the bus arrival time prediction based on the actual road conditions and can simultaneously predict the arrival time of multiple buses. Based on the above model and the Spark streaming platform, a real-time bus arrival time prediction software system is implemented. The experimental results show that our proposed model and system can accurately predict the bus arrival time and then well promote the bus travel experience for citizens.
The absolute positioning accuracy of the industrial robot is one of its important performance indexes,which is impacted by the key factor of robotic kinematic ***,based on the MDH model a calibration method of robot k...
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The absolute positioning accuracy of the industrial robot is one of its important performance indexes,which is impacted by the key factor of robotic kinematic ***,based on the MDH model a calibration method of robot kinematic parameters,which combines the Levenberg-Marquardt algorithm with the particle filter algorithm is ***,the MDH model of an industrial robot is established,and the parameters in the tool coordinate transformation are also regarded as the parameters to be *** the end error model is ***,the initial optimization is carried out using the Levenberg-Marquardt（LM） ***,the particle filter algorithm is used to further optimize the parameters considering the measurement ***,compared with other methods,such as spatial circle fitting method,least square method and extended Kalman filter *** results show that the kinematic parameters of the robot are accurately calibrated and the absolute positioning accuracy of the industrial robot is significantly improved by this *** with other methods,the parameters calibrated by this method have stronger generalization ability.
The movement of the robot adds great difficulty to dynamic human tracking. The traditional method of image stabilization can not remove the error caused by the movement of the human body to the stable image, resulting...
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ISBN:
(纸本)9789881563958
The movement of the robot adds great difficulty to dynamic human tracking. The traditional method of image stabilization can not remove the error caused by the movement of the human body to the stable image, resulting in low tracking accuracy and low real-time performance. This paper improves on the basis of traditional image stabilization techniques. In this paper, the least squares method is used to fit the position of the human body in the first N frames to infer the position of the human body in the (N + 1)th frame. Subsequently, this paper adopts the method of sub-regional gray projection to separate the positions of the human body in two adjacent frames of images and stabilize the images, which greatly reduces the error caused by the movement of the human body on the stable image distance. In addition, the traditional mobile body tracking method cannot solve the occlusion tracking situation of the target human body while satisfying the real-time performance. In this paper, various strategies such as camshift algorithm, particlefiltering method, image stabilization, and cross-frame difference are integrated, and a dynamic evaluation strategy of tracking quality is designed. The strategy can realize the normal tracking of the moving human body in the state of dynamic robot and tracking of the target human body in the occlusion situation.
Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particlefilter-based assimilation model and a one-dimensi...
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Reliable real-time flood forecasting is a challenging prerequisite for successful flood protection. This study developed a flood routing model combined with a particlefilter-based assimilation model and a one-dimensional hydrodynamic model. This model was applied to an indoor micro-model, using the Lower Yellow River (LYR) as prototype. Real-time observations of the water level from the micro-model were used for data assimilation. The results show that, compared to the traditional hydrodynamic model, the assimilation model could effectively update water level, flow discharge, and roughness coefficient in real time, thus yielding improved results. The mean water levels of the particle posterior distribution are closer to the observed values than before assimilation, even when water levels change greatly. In addition, the calculation results for different lead times indicate that the root mean square error of the forecasting water level gradually increases with increasing lead time. This is because the roughness value changes greatly in response to unsteady water flow, and the incurring error accumulates with the predicted period. The results show that the assimilation model can simulate water level changes in the micro-model and provide both research method and technical support for real flood forecasting in the LYR.
Data assimilation can help to ensure that model results remain close to observations despite potential errors in the model, parameters, and inputs. In this study, we test whether assimilation of snow depth observation...
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Data assimilation can help to ensure that model results remain close to observations despite potential errors in the model, parameters, and inputs. In this study, we test whether assimilation of snow depth observations using the particlefilter, a generic data assimilation method, improves the results of a multilayer energy-balance snow model, and compare the results against a direct insertion method. At the field site Col de Porte in France, the particlefilter reduces errors in SWE, snowpack runoff, and soil temperature when forcing the model with coarse resolution reanalysis data, which is a typical input scenario for operational simulations. For those variables, the model performance after assimilation of snow depths is similar to model performance when forcing with high-quality, locally observed input data. Using the particlefilter, we could also estimate a snowfall correction factor accurately at Col de Porte. The assimilation of snow depths also improves forecasts with lead-times of, at least, 7 days. At further 40 sites in Switzerland, the assimilation of snow depths in a model forced with numerical weather prediction data reduces the root-mean-squared-error for SWE by 64% compared to the model without assimilation. The direct insertion method shows similar performance as the particlefilter, but is likely to produce inconsistencies between modeled variables. The particlefilter, on the other hand, avoids such limitations without loss of performance. The methods proposed in this study efficiently reduces errors in snow simulations, seems applicable for different climatic and geographic regions, and are easy to deploy.
The movement of the robot adds great difficulty to dynamic human tracking. The traditional method of image stabilization can not remove the error caused by the movement of the human body to the stable image, resulting...
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The movement of the robot adds great difficulty to dynamic human tracking. The traditional method of image stabilization can not remove the error caused by the movement of the human body to the stable image, resulting in low tracking accuracy and low real-time performance. This paper improves on the basis of traditional image stabilization techniques. In this paper, the least squares method is used to fit the position of the human body in the first N frames to infer the position of the human body in the(N + 1)th frame. Subsequently, this paper adopts the method of sub-regional gray projection to separate the positions of the human body in two adjacent frames of images and stabilize the images, which greatly reduces the error caused by the movement of the human body on the stable image distance. In addition, the traditional mobile body tracking method cannot solve the occlusion tracking situation of the target human body while satisfying the real-time performance. In this paper, various strategies such as camshift algorithm, particlefiltering method, image stabilization, and cross-frame difference are integrated, and a dynamic evaluation strategy of tracking quality is designed. The strategy can realize the normal tracking of the moving human body in the state of dynamic robot and tracking of the target human body in the occlusion situation.
Power battery packs are the energy source of battery electric vehicles (BEVs). A precise state-of-health (SOH) estimation for batteries is crucial to ensure the operational security and stability of BEVs. This paper e...
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Power battery packs are the energy source of battery electric vehicles (BEVs). A precise state-of-health (SOH) estimation for batteries is crucial to ensure the operational security and stability of BEVs. This paper employs an equivalent circuit model of battery pack in SOH estimation. Since a battery pack is a complex and nonlinear system, the equivalent circuit model of battery pack is always complicated. To balance estimation accuracy and computational complexity, the equivalent circuit model of battery pack should be simplified. However, much noise is produced in the simplified model. In addition, the errors during SOH estimation are from various sources so that SOH estimation is a non-Gaussian problem. Given the genetic resampling particlefilter (GPF) performs efficiently in solving non-Gaussian problems, this paper proposes a new GPF-based method for battery SOH dynamic estimation when accuracy of the equivalent circuit model is not high. First, a second-order equivalent circuit model of Resistance Capacitance (RC) circuit for the battery pack is developed. The unknown parameters are identified using the recursive least-squares method with forgetting factor. Second, a state-space model of the GPF is developed based on the equivalent circuit model. Finally, a case study is conducted using real data collected from operating electric taxis in Beijing to investigate the estimation performance of the proposed model. Estimation results show that the proposed GPF model outperforms the particlefilter method in the SOH estimation problem. (C) 2016 Elsevier Ltd. All rights reserved.
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