A Hybrid AOA and TDOA-Based Localization Method Using Only Two Stations

Target localization plays an important role in the application of radar, sonar, and wireless sensor networks. In order to improve the localization performance using only two stations, a hybrid localization method based on angle of arrival (AOA) and time difference of arrival (TDOA) measurements is proposed in this paper. Firstly, the optimization model for localization based on AOA and TDOA are built, respectively, in the sensor network. Secondly,the majorization-minimization (MM) method is employed to create surrogate functions for solving the multiple objective optimization problem. Next, the hybrid localization problem is solved by the projected gradient decent (PGD) method. Finally, the Cramer–Rao lower bound (CRLB) for the joint AOA and TDOA method is derived for the comparison. Simulations proved that the proposed method has improved localization performance using AOA and TDOA measurements from only two base stations.

Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene

Aiming at the localization problem of mobile robot in construction scenes, a hybrid localization algorithm with the adaptive weights is proposed, which can effectively improve the robust localization of mobile robot. Firstly, two indicators of localization accuracy and calculation efficiency are set to reflect the robustness of localization. Secondly, the construction scene is defined as an ongoing scene, and the robust localization of mobile robot is achieved by using the measurement of artificial landmarks and matching based on generated features. Finally, the experimental results show that the accuracy of localization is up to 8.22 mm and the most matching efficiency is controlled within 0.027 s. The hybrid localization algorithm that based on adaptive weights can realize a good robustness for tasks such as autonomous navigation and path planning in construction scenes.