Obtaining accurate angle parameters using direction-of-arrival (DOA) estimation algorithms is crucial for acquiring channel state information (CSI) in massive multiple-input multiple-output (MIMO) systems. However, the performance of the existing algorithms deteriorates severely due to mutual coupling between antenna elements in practical engineering. Therefore, for solving the array mutual coupling, the array output signal vector is modeled by mutual coupling coefficients and the DOA estimation problem is transformed into block sparse signal reconstruction and parameter optimization in this paper. Then, a novel sparse Bayesian learning (SBL)-based algorithm is proposed, in which the expectation-maximum (EM) algorithm is used to estimate the unknown parameters iteratively, and the convergence speed of the algorithm is enhanced by utilizing the approximate approximation. Moreover, considering the off-grid error caused by discretization processes, the grid refinement is carried out using the polynomial roots to realize the dynamic update of the grid points, so as to improve the DOA estimation accuracy. Simulation results show that compared with the existing algorithms, the proposed algorithm is more robust to mutual coupling and off-grid error and can obtain better estimation performance.

Simultaneous localization and mapping (SLAM) enables robots to localize in uncertain environments and has been widely used in the field of robotics. However, traditional vision SLAM systems usually assume that the observation environment is stationary, which results in unsatisfactory performance in dynamic scenarios. To overcome this challenge, we propose a DYMRO-SLAM system which incorporates a Mask R-CNN instance segmentation network and an optical flow tracking algorithm based on ORB-SLAM3 to enhance the localization performance in dynamic environments. Specifically, the system separates dynamic targets from the stationary background during image processing stage to improve the accuracy of its localization in dynamic scenes; When processing key and non-keyframes, the system adopts feature point tracking and optical flow tracking, respectively, which greatly reduces computation time of descriptors and thus improves the real-time property. Finally, we validate the performance on datasets from TUM, EuRoC and our own datasets, and the experiment results verifies that our DYMRO-SLAM system has significant performance gains in terms of localization accuracy and runtime efficiency.

Aiming at the cultivation concept of high-level innovative talents in the new era, according to the training objectives and positioning of new engineering students, the innovation ability and practical ability still need to be further strengthened and improved. In order to make up for the practical defects that the conventional graduate training model does not provide students with opportunities and platforms to organically integrate and apply specific engineering problems and theoretical knowledge, this paper will explore and study a new teaching and training mode based on the background of "AI+(Artificial Intelligence +)", which focuses on improving and strengthening the innovation and practical ability of new engineering students through the mechanism of project-led and multi-dimensional collaboration. The authors put forward a set of teaching mode to stimulate students to dig and solve practical engineering problems. By adopting the "AI+ industry-specific application" strategy in teaching, the model guides students to make full use of AI models and algorithms, and closely integrate course content with specific problems in specific industries. By applying this teaching mode to students of grade 2019, good results have been achieved.