The article discusses the suitability of the multiwall radio wave propagation model for RSSI reference data preparation for fingerprinting-based indoor positioning applications. Localization system employs Bluetooth L...
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With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health（SOH） is of great significance in the safe and reliable operation of battery management system and...
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With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health（SOH） is of great significance in the safe and reliable operation of battery management system and the reduction of maintenance cost. Based on the analysis of the traditional particle filter algorithm, the genetic factor of genetic algorithm（GA）is introduced into the particlefilter and improved by adaptive mutation. In order to predict the SOH of lithium-ion battery, the health index（HI） is extracted from the measurable parameters of lithium-ion battery. The mapping model between HI index and SOH is established and applied to the observation of state space model. In this paper, a battery SOH estimation method based on improved particle filter algorithm is proposed. The experimental results show that the proposed method is superior to the traditional particlefilter（PF） algorithm and has good accuracy in estimating the degradation process of lithium-ion batteries.
For detecting and tracking the infrared dim-small target with the low SNR, the particle filter algorithm is applied to the problem of infrared dim-small target's tracking before detection in this paper. The infrar...
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ISBN:
(纸本)9781479902606
For detecting and tracking the infrared dim-small target with the low SNR, the particle filter algorithm is applied to the problem of infrared dim-small target's tracking before detection in this paper. The infrared dim-small target's tracking before detection based on the full view sampling's particle filter algorithm is proposed. And the hardware system is composed of an infrared imager, the ICETEK-DM642-PCI development board and the monitor. The hardware system is configured by using DSP/BIOS which is a real-time operating system. The experimental results show that the system achieves better detection and tracking's effect than the traditional methods.
Micro milling aims to manufacture miniature structures with high quality and complex features, and the stochastic time-varying tool wear is a crucial factor which has great influence on machining quality and efficienc...
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Micro milling aims to manufacture miniature structures with high quality and complex features, and the stochastic time-varying tool wear is a crucial factor which has great influence on machining quality and efficiency of micro milling process. To improve the precision of machining and sustainability of micro cutting tools, the in-process tool wear conditions should be identified and updated ahead of time. In this work, an improved integrated estimation method is proposed based on the long short-term memory (LSTM) network and particlefilter (PF) algorithm to predict the stochastic tool wear values. The integrated PF-LSTM identification methodology is developed to predict the in-process stochastic tool wear progression on the basis of the historical measurement data. With the estimation of in-process stochastic tool wear, the cutting force model is modified, in which the influence of tool run-out and the trochoidal trajectory of cutting edge are also considered. The proposed integrated estimation method of in-process stochastic tool wear and the modified cutting force model were validated by the micro milling experiments with workpiece material Al6061. It can be seen from the comparison results that the availability and sustainability of micro cutting tool have been improved, and the prediction accuracy also could be increased by 3.4% compared with that without considering the influence of tool wear.
This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the...
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This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.
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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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.
In order to solve the fusion estimation problem of multi-sensor with unknown cross-covariance,an improved suboptimal fusion algorithm weighted by matrices is proposed for nonlinear ***,for significance of linear minim...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
In order to solve the fusion estimation problem of multi-sensor with unknown cross-covariance,an improved suboptimal fusion algorithm weighted by matrices is proposed for nonlinear ***,for significance of linear minimum variance,the simplest constraints based on fusion weighted by matrices are derived by Shure complement *** constraints can ensure the positive definiteness of the fusion estimate error covariance,and the consistency of the proposed suboptimal fusion ***,a suboptimal fusion estimation weighted by matrices is proposed based on linear matrix inequality(LMI).Considering the time-consuming problem in the optimization process of LMI algorithm and the complexity of the nonlinear system,the optimal value is obtained by the nonlinear auto-regressive neural network with exogenous input(NARX).Finally,a nonlinear suboptimal fusion algorithm weighted by matrices based on LMI and NARX is proposed in combination with the particle filter algorithm(PF).
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