Due to the complexity of strong metal interference and multiple occlusions in aircraft assembly workshop,the random "drift"phenomenon often happens in the ultra wide band (UWB)based positioning system.To sol...Due to the complexity of strong metal interference and multiple occlusions in aircraft assembly workshop,the random "drift"phenomenon often happens in the ultra wide band (UWB)based positioning system.To solve this,a fusion positioning optimization algorithm is proposed based on median filtering,hidden Markov model (HMM)a nd Kalman filtering.Firstly,based on the three-dimensional (3D) median filtering,aqueue optimization method with weights is introduced to smooth the measurement data and eliminate the abnormal value.Secondly,taking Singer model as a reference,a single-dimension acceleration distribution model is designed.In order to further consider the spatial motion characteristics of objects in workshop,the distribution is extended from 1D to 3D,and discretized into the state quantity of HMM.Subsequently,the data obtained by the two methods are fused by taking Kalman filter as an iterator,and then the optimized location solution is obtained based on dynamic weights.Finally,an experiment is conducted in an aircraft assembly workshop.Results show that 99.3% of dynamic positioning errors are less than 15 cm after using the proposed algorithm.Even in the situation with large signal-fluctuation,there are 71.6% of positioning data whose errors are reduced.The random "drift"is greatly decreased.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51575274)the National Defense Basic Scientific Research of China(No.JCKY2016605B006)the Jiangxi Provincial Key R&D Program Project(No.20161ACE50004)
文摘Due to the complexity of strong metal interference and multiple occlusions in aircraft assembly workshop,the random "drift"phenomenon often happens in the ultra wide band (UWB)based positioning system.To solve this,a fusion positioning optimization algorithm is proposed based on median filtering,hidden Markov model (HMM)a nd Kalman filtering.Firstly,based on the three-dimensional (3D) median filtering,aqueue optimization method with weights is introduced to smooth the measurement data and eliminate the abnormal value.Secondly,taking Singer model as a reference,a single-dimension acceleration distribution model is designed.In order to further consider the spatial motion characteristics of objects in workshop,the distribution is extended from 1D to 3D,and discretized into the state quantity of HMM.Subsequently,the data obtained by the two methods are fused by taking Kalman filter as an iterator,and then the optimized location solution is obtained based on dynamic weights.Finally,an experiment is conducted in an aircraft assembly workshop.Results show that 99.3% of dynamic positioning errors are less than 15 cm after using the proposed algorithm.Even in the situation with large signal-fluctuation,there are 71.6% of positioning data whose errors are reduced.The random "drift"is greatly decreased.
