Efficient Localization Algorithm With UWB Ranging Error Correction Model Based on Genetic Algorithm-Ant Colony Optimization-Backpropagation Neural Network

The development of wireless sensor network technology has extended the diverse range of tools available in location-based services (LBS). Indoor high-precision positioning is among the most popular topics in location tracking and positioning. Ultrawideband (UWB) double-sided two-way ranging (DS-TWR) is used widely because it provides a reliable ranging performance. In this study, the effect of the UWB DS-TWR ranging error was suppressed using a ranging error model to improve the reliability and accuracy of indoor positioning services. Based on the conditions described above, an optimized backpropagation neural network (BPNN) correction model that integrates both a genetic algorithm (GA) and the ant colony optimization (ACO) algorithm, forming the GA-ACO-BPNN model, is established and verified experimentally. In addition, under static and kinematic actual positioning conditions, improvements in the BPNN, GA-BPNN, ACO-BPNN, and GA-ACO-BPNN ranging error correction models in terms of their positioning performances are calculated and compared. The experimental results show that the proposed GA-ACO-BPNN model can reduce the impact of the ranging error on ranging and positioning effectively. The positioning accuracy and reliability of the UWB DS-TWR solutions are improved significantly after application of this model, which provides a reference point for solutions to subsequent fusion positioning problems, e.g., UWB and inertial measurement unit integration.