Research on Accurate Location Algorithm of Optimized Multi-source Data Fusion Based on Improved GRU Network

The uncertainty of positioning trajectory and the existence of abnormal trajectory points are major challenges in the field of navigation and positioning. The method that only relies on global positioning system (GPS) has a large error, and the traditional location algorithms still have shortcomings in location efficiency and accuracy. Due to the successful exploration of deep learning in the field of navigation and positioning, an accurate positioning algorithm based on improved gated recurrent unit (GRU) and multi-source data fusion was proposed in this paper. Firstly, interpolation was used to fuse GPS data and inertial measurement unit (IMU) data to overcome the defect of single data. Then, an improved gated recurrent unit was introduced to process the data fusion. On this basis, the attention mechanism (AM) was added to reduce the loss of historical information and strengthen the influence of important information, so as to achieve accurate positioning. Compared with other advanced algorithms with regard to L2 norm and time of training iteration, the training time of this algorithm is reduced by more than 27%, and the highest optimization rate is 12.08%. Therefore, this algorithm can obtain higher positioning accuracy in a shorter time.