A BA-RRT-Based Indoor Geomagnetic Positioning Algorithm

With the development of the navigation technology, the geomagnetic positioning method is widely used due to its superior characteristics, such as the non-accumulating error, high positioning accuracy, and all-weather applications. Currently, most of the geomagnetic positioning methods need to be combined with external sensors to obtain positioning results, which leads to the limitation of the application environment of traditional geomagnetic positioning methods according to their combined sensors, so it is necessary to implement independent geomagnetic positioning. However, without external sensors providing path information, the process of geomagnetic matching will be more complex, making it more difficult to locate. To solve this problem, a geomagnetic independent positioning method based on the Bat Algorithm combined with the improved Rapidly-exploring Random Tree (BA-RRT) algorithm is proposed in this paper, which can locate with geomagnetic measurement sequence and a priori geomagnetic map in the absence of path information. Each bat position in the Bat Algorithm represents the path starting point, the improved Rapidly-exploring Random Tree is used to match the geomagnetic sequences. The motion path with the best adaptation is obtained by iterative meritocracy, and the localization results are obtained. Positioning experiments were conducted by indoor measurement of geomagnetic data, and the localization accuracy exceeds 90% with accurate geomagnetic map and no obvious interference, verifying the effectiveness of BA-RRT. The method proposed in this paper can provide a new approach for future research on geomagnetic independent positioning.