Biomimetic detection of dynamic signatures in foliage echoes

A bat biomimetic model for scenario recognition using echo Doppler information

The flying bat can detect the difference in Doppler frequency between its echolocation transmission signal and the echoes in its surroundings, enabling it to distinguish between various scenarios effectively. By examining the bio-sonar biomimetic model of a flying bat that uses echo Doppler information for environmental recognition, it may enhance the scene recognition capability of human ultrasound sonar during movement. The paper establishes a three-dimensional clutter model of the flying state of bat bio-sonar for bats emitting constant frequency signals. It proposes a scene recognition method that combines multi-scale time-frequency feature analysis with a convolutional neural network (CNN). The short-time Fourier transform of different scales extract the Doppler and range dimensions, which are then fused to create a multi-scale feature plane containing both Doppler and range information. Combined with CNN's powerful image classification and recognition capabilities, extract features from multi-scale feature planes of different clutter scenes to achieve environment recognition based on the differences in Doppler and range dimensions of echoes in various directions. Through computer simulations, this study provides a numerical interpretation of the environmental classification and perception capabilities of bats in flight. The algorithm significantly improves scenario classification and recognition performance according to simulation results, with accuracy exceeding 98% in varied clutter scenarios at 30 dB signal noise ratio. Based on computer simulations, an experimental scene was constructed and actual echo signals were collected and analyzed. The experiments demonstrate that utilizing Doppler information enables the classification and recognition of cluttered environments. The effectiveness of the proposed algorithm was also verified. Ultrasonic sonar systems, such as navigation robots and helicopter obstacle avoidance, can apply this biomimetic model and algorithm for environmental recognition during motion.

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

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 Global Positioning System (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.