With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved ...With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.展开更多
The paper deals with the state estimation of the widely used scaled unscented Kalman filter（UKF）. In particular, the stress is laid on the scaling parameters selection principle for the scaled UKF. Several problems ...The paper deals with the state estimation of the widely used scaled unscented Kalman filter（UKF）. In particular, the stress is laid on the scaling parameters selection principle for the scaled UKF. Several problems caused by recommended constant scaling parameters are highlighted. On the basis of the analyses, an effective scaled UKF is proposed with self-adaptive scaling parameters,which is easy to understand and implement in engineering. Two typical strong nonlinear examples are given and their simulation results show the effectiveness of the proposed principle and algorithm.展开更多
A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the re...A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.展开更多
基金supported by the National Natural Science Foundation of China(61703228)
文摘With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.
基金supported by the National Natural Science Foundation of China(61703228)
文摘The paper deals with the state estimation of the widely used scaled unscented Kalman filter（UKF）. In particular, the stress is laid on the scaling parameters selection principle for the scaled UKF. Several problems caused by recommended constant scaling parameters are highlighted. On the basis of the analyses, an effective scaled UKF is proposed with self-adaptive scaling parameters,which is easy to understand and implement in engineering. Two typical strong nonlinear examples are given and their simulation results show the effectiveness of the proposed principle and algorithm.
基金Supported by Henan Institute of Science and Technology (055031)
文摘A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.
