Bats are famous for their capability of navigating in dense forests for hundreds of kilometers within one night by using their sonar system. Airborne sonar hasn't been heavily used in the industrial world compared to other sensors such as lidar, radar, and cameras. In this study, we applied a biosonar robot to navigate in a dense forest with bat-like FM-CF ultrasonic signals with deep learning. The results presented show that airborne biosonar can classify different areas' plants, in addition to achieving a similar level of navigation granularity compared to GPS, which is about 6 meters of radius resolution. The time- frequency representations of echoes from the forest are used as input data to explore the biosonar navigation ability, and the state-of-the-art CNN deep network (Resnet 152) is used as the brain to do the echolocation in the dense forest. The navigation ability can be improved significantly by combining multiple 10 ms long echoes, however, the data size of the reflected waves is much smaller than the other popularly used sensors, as echo can be collected at a rate of 40 echoes per second. The results can prove that airborne sonar can be used to navigate in GPS-denied environments, and can be an important sensor used in a scenario when other sensors meet constraints, like in the sensor fusion applications. / Doctor of Philosophy / The ability to identify natural landmarks could contribute to the navigation skills of echolo- cating bats and also advance the quest for autonomy in natural environments with man- made systems. The critical sensors used in autonomous robot navigation are camera array, radar, and lidar, airborne sonar hasn't been verified for its navigation efficiency. However, recognizing natural landmarks based on biosonar echoes has to deal with the unpredictable nature of echoes that are typically superpositions of contributions from many different reflec- tors with unknown properties. This dissertation intends to explore the bioinspired airborne sonar navigation ability in dense natural forests. The first part of this project is to use reflected echoes to navigate on a large scale, data was collected from different mountains which are dozens of kilometers away from each other, and we achieved the use of one single navigator in those locations. The second part is to explore the navigation granularity of airborne sonar sensors, data were collected from a small dense forest area, we try to classify which part of the foliage was based on the echo, and in the end, we achieved GPS accuracy for navigation. The finding in this work proves that the sonar sensor can play an important role in the sensing system, with the help of a deep neural network, with a 10 ms long echo, it can have a similar navigation ability to GPS.