In order to enhance the positioning accuracy, a UWB positioning system based on genetic algorithm is proposed. Firstly, it uses the DW1000 module to measure the distance, and preprocesses the measured data to remove n...In order to enhance the positioning accuracy, a UWB positioning system based on genetic algorithm is proposed. Firstly, it uses the DW1000 module to measure the distance, and preprocesses the measured data to remove noise. Then, a positioning equation is established according to the processed distance information, and the genetic algorithm is used to solve the equation to obtain the coordinates of the unknown node. The experimental results show that the measured positioning accuracy is within 30 cm, which means that the system can obtain a good positioning effect and meet the need for precise positioning.展开更多
To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical ...To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical manifold<span>’</span><span>s accurate positioning ability fully. The simulation results show that the hybrid location algorithm has higher accuracy and reduces the influence of initial value selection on location accuracy.</span>展开更多
The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information b...The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.展开更多
文摘In order to enhance the positioning accuracy, a UWB positioning system based on genetic algorithm is proposed. Firstly, it uses the DW1000 module to measure the distance, and preprocesses the measured data to remove noise. Then, a positioning equation is established according to the processed distance information, and the genetic algorithm is used to solve the equation to obtain the coordinates of the unknown node. The experimental results show that the measured positioning accuracy is within 30 cm, which means that the system can obtain a good positioning effect and meet the need for precise positioning.
文摘To improve the location accuracy, a hybrid location algorithm based on cuckoo and statistical manifold method is proposed. It combines the cuckoo algorithm's strong global optimization ability and the statistical manifold<span>’</span><span>s accurate positioning ability fully. The simulation results show that the hybrid location algorithm has higher accuracy and reduces the influence of initial value selection on location accuracy.</span>
文摘The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.
