Many bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that "clutter echoes" originating from vegetation can convey information on the environment they were recorded in -- despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The GPS location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.

Many bat species are able to find their way through densely vegetated habitats and select their microhabitats to suit their respective needs. This raises the question as to how granular the natural habitats of bats are when sensed through the echoes triggered by the animals' biosonar pulses. To investigate this question, a portable biomimetic sonar head has been used to collect about 40 000 foliage echoes across a natural forest site on the Virginia Tech campus that was approximately 250 × 140 m2 in size. Each recorded echo was assigned a spatial location that was estimated from the outputs of a concurrently operated GPS receiver. These spatial locations were then used to cluster the echoes into a varying number of compact patches using a k-means clustering algorithm. To determine whether these spatial patches could be determined from the echoes, a convolutional neural network based on the ResNet50 architecture was trained to classify the echoes with respect to their spatial labels. Even based on just a single echo, up to 100 different spatial patches could be distinguished in this way with accuracies greater than 95%. These results demonstrate that bat biosonar can capture sensory information for small-scale navigation in natural forest habitats.