Research On Indoor Positioning Technology Based On UWB/IMU Combination

Compared with other traditional wireless communication technologies,UltraWide Band(UWB)technology has become the current indoor positioning due to its low power consumption,low cost,high data transmission rate,strong security and strong multipath resistance.The preferred wireless location solution.However,in a complex indoor environment,the UWB signal introduces a positive deviation during ranging due to the influence of indoor obstacles.Not only that,UWB signals may cause reflection,scattering,and diffraction in indoor walls,floors,and desktops,resulting in increased positioning errors.The indoor positioning method of a single sensor cannot fundamentally solve this problem.The Strapdown Inertial Navigation System based on Inertial Measurement Unit(IMU)can operate independently through acceleration and gyroscope data without relying on the external environment,and has high precision in the short term.However,as time increases,errors accumulate.This paper proposes a method of combining ultra-wideband and inertial positioning to improve indoor positioning accuracy.The UWB signal is blocked by the obstacle,so that the measured value between the node to be tested and the base station will have abnormal values such as zero value,continuous constant value,and sudden value.These outliers will cause a large error in the subsequent UWB positioning solution.Aiming at this problem,this paper proposes an improved TWR ranging algorithm.On the basis of the original TWR algorithm,the processing of zero value,continuous invariant value and abrupt value is added.Compared with the original algorithm,the improved TWR algorithm has less positioning error,and the effectiveness of the modified algorithm is verified in experiments.It is found that the trajectory of the moving object obtained by UWB positioning has a very "burr" in the z-axis direction.Aiming at this problem,this paper proposes a UWB positioning result optimization algorithm based on Kalman filtering.The algorithm takes the estimated value of the UWB positioning result at the previous moment as the predicted value,and takes the observation result of the current time UWB positioning as the observation value,and brings it into the Kalman algorithm for optimization.The experimental results show that this optimization method can smooth the UWB positioning results to a large extent and improve the UWB positioning accuracy.This paper designs the hardware platform and software platform of the indoor positioning system.Based on the extended Kalman filter and the unscented Kalman filter framework,the UWB ranging value and the IMU observation value are merged together.The UWB/IMU combined indoor positioning system based on the above two information fusion frameworks is verified by matlab simulation.The positioning accuracy is better than that of a single UWB-dependent positioning system.