{"title":"蝙蝠生物声纳中多散点物体距离-方位辨别的仿生研究。","authors":"Feng Wang, Ming Chen","doi":"10.1088/1748-3190/ad2085","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.</p>","PeriodicalId":55377,"journal":{"name":"Bioinspiration & Biomimetics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bionic study of distance-azimuth discrimination of multi-scattered point objects in bat bio-sonar.\",\"authors\":\"Feng Wang, Ming Chen\",\"doi\":\"10.1088/1748-3190/ad2085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.</p>\",\"PeriodicalId\":55377,\"journal\":{\"name\":\"Bioinspiration & Biomimetics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-02-06\",\"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/ad2085\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinspiration & Biomimetics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1088/1748-3190/ad2085","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}
Bionic study of distance-azimuth discrimination of multi-scattered point objects in bat bio-sonar.
This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.
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.