A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-...Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].展开更多
In this paper,we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data.Each animal carried sensors generating time series accelerometer data placed on a collar on the...In this paper,we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data.Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head,on a halter positioned at the side of the head behind the mouth,or on the ear using a tag.The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours.Data were collected and animal behaviours(grazing,standing or ruminating)were observed over a common time frame.Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour.Classification accuracies were computed on separate independent test sets.The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space(representing motion patterns)between the training and test animal is similar.The paper will discuss these analyses in detail and can act as a guide for future studies.展开更多
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
基金supported by the National Nature Science Foundation of China(62272078)
文摘Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].
文摘In this paper,we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data.Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head,on a halter positioned at the side of the head behind the mouth,or on the ear using a tag.The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours.Data were collected and animal behaviours(grazing,standing or ruminating)were observed over a common time frame.Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour.Classification accuracies were computed on separate independent test sets.The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space(representing motion patterns)between the training and test animal is similar.The paper will discuss these analyses in detail and can act as a guide for future studies.
