Research On Indoor Location Algorithm Of AOA Base On UWB

Accurate location service has become an important foundation in the era of the Internet of Things.Currently,Outdoor positioning technology based on satellite navigation has been widely used in people’s lives.However,with the advent of the era of Internet of Everything,people’s demand for accurate location services in the indoor environment has become increasingly strong.Indoor location technology has established a bridge between the real world and virtual space,providing many conveniences for people to live in the indoor environment.At present,Ultra Wide Band(UWB)based positioning technology is the mainstream of current indoor positioning technology,among which Angle of Arrival(AOA)based positioning algorithm is one of the most widely studied UWB positioning algorithms.At present,the AOA positioning technology based on UWB is mainly realized by multichannel antenna array,which has complex system and high cost.At the same time,in the indoor environment,it is easy to be affected by the complex indoor layout,the random movement of pedestrians and other factors,which leads to the serious multi-path effect,resulting in low positioning accuracy and poor stability of the system.In this thesis,the current mainstream multi-channel UWB array antenna to achieve AOA positioning algorithm has the shortcomings of complex algorithm deployment and high cost.The dual channel UWB base station is designed.Combining the direction finding algorithm and ranging algorithm,the AOA positioning algorithm is studied to realize simple algorithm deployment,low implementation cost and high accuracy.At the same time,This thesis analyzes the influence of indoor multi-path environment on the basic AOA localization algorithm from the perspectives of ranging and direction finding.It provides the foundation for algorithm optimization in the following indoor environment.This thesis aiming at the problems of low positioning accuracy and poor system stability of AOA algorithm based on dual channel UWB in indoor environment,an optimization algorithm of distance measurement error prediction convolutional neural network is proposed.By optimizing the ranging algorithm,the optimization of AOA localization algorithm is realized.When the proposed optimization algorithm is deployed on the server,its ranging accuracy is significantly improved compared with that of the common bilateral bidirectional ranging algorithm,and its overall positioning accuracy and robustness are also improved compared with the basic algorithm.The optimization algorithm based on convolutional neural network can achieve good optimization effect in indoor environment,but it needs to be deployed on a platform with high computing power,which requires high system resources consumption and low realtime system performance.In order to achieve high precision positioning on embedded platforms with low computational power,this thesis proposes an optimization algorithm based on signal decomposition by referring to the design idea of convolutional neural network.The ultra-wideband signal received in the multi-path environment is decomposed,and an ultra-wideband signal that is not affected by the multi-path response or has little influence is fitted.The basic AOA positioning algorithm is optimized through this fitting signal.Experiments show that this algorithm can effectively reduce the influence of multipath effect on UWB AOA localization algorithm and achieve high precision indoor localization.At the same time,the algorithm needs less system resources and is more real-time.