{"title":"A bat biomimetic model for scenario recognition using echo Doppler information.","authors":"Wang Feng, Pang Chunyang, Lu Yuqing, Wang Hao","doi":"10.1088/1748-3190/ad262d","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55377,"journal":{"name":"Bioinspiration & Biomimetics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinspiration & Biomimetics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1088/1748-3190/ad262d","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Bioinspiration & Biomimetics publishes research involving the study and distillation of principles and functions found in biological systems that have been developed through evolution, and application of this knowledge to produce novel and exciting basic technologies and new approaches to solving scientific problems. It provides a forum for interdisciplinary research which acts as a pipeline, facilitating the two-way flow of ideas and understanding between the extensive bodies of knowledge of the different disciplines. It has two principal aims: to draw on biology to enrich engineering and to draw from engineering to enrich biology.
The journal aims to include input from across all intersecting areas of both fields. In biology, this would include work in all fields from physiology to ecology, with either zoological or botanical focus. In engineering, this would include both design and practical application of biomimetic or bioinspired devices and systems. Typical areas of interest include:
Systems, designs and structure
Communication and navigation
Cooperative behaviour
Self-organizing biological systems
Self-healing and self-assembly
Aerial locomotion and aerospace applications of biomimetics
Biomorphic surface and subsurface systems
Marine dynamics: swimming and underwater dynamics
Applications of novel materials
Biomechanics; including movement, locomotion, fluidics
Cellular behaviour
Sensors and senses
Biomimetic or bioinformed approaches to geological exploration.