Research on UWB Localization Algorithm Based on Improved BP Neural Network and Kalman Filter

To solve the problem that the traditional BP(Backpropagation) neural optimization UWB(Ultra-Wideband) localization algorithm is prone to fall into the problem of poor localization effect caused by local optimization, this paper proposes a UWB localization algorithm based on adaptive particle swarm improved neural network algorithm combined with Kalman filtering to improve the status quo of low indoor target positioning accuracy and unreliable positioning data.Nonlinear decay inertial weights are introduced in the particle swarm algorithm to improve parameter learning capabilities. The improved particle swarm algorithm optimizes the neural network for training and calculates the label coordinates to further improve the accuracy of system positioning.