Unscented Kalman Filter Based IMU and UWB Fusion Position Algorithm

The emerging application of the Internet of things puts forward a huge demand for providing a low-cost and high-precision indoor positioning scheme. However, in the complex indoor environment, it is difficult to use an independent system to achieve higher requirements of positioning accuracy. In order to solve the problem of low accuracy and poor stability of the traditional three base station observation distance least square positioning algorithm which only relies on UWB (Ultra-wideband) sensors, an IMU (Inertial measurement unit) and UWB multi-sensor fusion positioning algorithm based on Unscented Kalman filter (Unscented Kalman filter) is proposed. In addition, the DPA (Direct position algorithm) based on UKF is also proposed. When the positioning accuracy is not high, the DPA positioning algorithm can greatly reduce the complexity and cost of base station deployment. The results show that the positioning accuracy of the proposed three base station observation distance WLS positioning algorithm is relatively improved. The fusion algorithm based on UKF can not only improve the positioning accuracy, but also effectively suppress the jitter of the positioning data, thus further improving the positioning stability. It is suitable for the positioning scene with low cost, high precision and high reliability in complex indoor environment.