Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016600
Muhammad Hanif Tunio, Liao Jianping, Muhammad Hassaan Farooq Butt, Imran Memon, Yumna Magsi
Farmers may make better crop management decisions with accurate yield estimation. The key challenge for accurate fruit yield estimation is to distinguish and pinpoint the fruit from the tree in the field. In this model, we have used the U-Net architecture to cope and investigate this challenge. U-Net is based on sementic segmentation used for object detection and localisation. U-Net's contraction path encodes and extract the features from the source object (mango pictures), while the expansion path decodes the image by recovering the resolution for better localisation. This study focus on mango fruit an we employed the ACFR Mango Dataset. The all dataset images was divided into three classes: train, validation, and test images. The constructed model evaluated with test iamges that were not part of the training. Our model predicted accuracy and test image loss both were 98.66% and 0.0268%, respectively.
{"title":"Fruit Detection and Segmentation Using Customized Deep Learning Techniques","authors":"Muhammad Hanif Tunio, Liao Jianping, Muhammad Hassaan Farooq Butt, Imran Memon, Yumna Magsi","doi":"10.1109/ICCWAMTIP56608.2022.10016600","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016600","url":null,"abstract":"Farmers may make better crop management decisions with accurate yield estimation. The key challenge for accurate fruit yield estimation is to distinguish and pinpoint the fruit from the tree in the field. In this model, we have used the U-Net architecture to cope and investigate this challenge. U-Net is based on sementic segmentation used for object detection and localisation. U-Net's contraction path encodes and extract the features from the source object (mango pictures), while the expansion path decodes the image by recovering the resolution for better localisation. This study focus on mango fruit an we employed the ACFR Mango Dataset. The all dataset images was divided into three classes: train, validation, and test images. The constructed model evaluated with test iamges that were not part of the training. Our model predicted accuracy and test image loss both were 98.66% and 0.0268%, respectively.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115391960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016549
Enoch Frimpong, Qin Zhiguang, Tenagyei Edwin Kwadwo, Patamia Agbeshi Rutherford, E. Baagyere, Regina Esi Turkson
Listening to the heart sound with digital or manual stethoscopes has become one of the practical ways to identify heart diseases in recent years. It's still difficult because of its manual approach and the fact that only experienced healthcare practitioners can use it to diagnose anomalies. The automatic extraction of heart sound features to aid in classification has been explored, however there is still potential for improvement. This paper proposes a residual neural network integrated with a convolutional block attention module (CBAM) for heart sound analysis, using generated Mel-spectrograms as input for our network. We tested our model using the Pascal Heart Sound Challenge dataset, and it performed favorably to other cutting-edge models.
{"title":"Heart Sound Classification using Residual Neural Network and Convolution Block Attention Module","authors":"Enoch Frimpong, Qin Zhiguang, Tenagyei Edwin Kwadwo, Patamia Agbeshi Rutherford, E. Baagyere, Regina Esi Turkson","doi":"10.1109/ICCWAMTIP56608.2022.10016549","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016549","url":null,"abstract":"Listening to the heart sound with digital or manual stethoscopes has become one of the practical ways to identify heart diseases in recent years. It's still difficult because of its manual approach and the fact that only experienced healthcare practitioners can use it to diagnose anomalies. The automatic extraction of heart sound features to aid in classification has been explored, however there is still potential for improvement. This paper proposes a residual neural network integrated with a convolutional block attention module (CBAM) for heart sound analysis, using generated Mel-spectrograms as input for our network. We tested our model using the Pascal Heart Sound Challenge dataset, and it performed favorably to other cutting-edge models.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124901205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016617
Xiao Fei, Liao Jianping, Tian Jie, Wang Guangshuo
Image recognition is one of the basic tasks of computer vision, and it is also one of the important research directions in the field of machine learning. The artificial neural network algorithm has achieved very remarkable results in image recognition, convolutional neural network is one of the most popular artificial neural network, it’s also the main solution to image recognition currently. The spiking neural network is called the third-generation neural network, which is different from the previous generation of neural networks. Inspiring by neuroscience, spiking neural network try to build neural networks in a way closer to the human brain mechanism. Referring the application of artificial neural network in image recognition, we decide to use the convolutional neural network in image recognition, moreover, we combine the spiking neural network to construct a new neural network and try to apply it in the field of image classification.
{"title":"Image Recognition Algorithm Based on Spiking Neural Network","authors":"Xiao Fei, Liao Jianping, Tian Jie, Wang Guangshuo","doi":"10.1109/ICCWAMTIP56608.2022.10016617","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016617","url":null,"abstract":"Image recognition is one of the basic tasks of computer vision, and it is also one of the important research directions in the field of machine learning. The artificial neural network algorithm has achieved very remarkable results in image recognition, convolutional neural network is one of the most popular artificial neural network, it’s also the main solution to image recognition currently. The spiking neural network is called the third-generation neural network, which is different from the previous generation of neural networks. Inspiring by neuroscience, spiking neural network try to build neural networks in a way closer to the human brain mechanism. Referring the application of artificial neural network in image recognition, we decide to use the convolutional neural network in image recognition, moreover, we combine the spiking neural network to construct a new neural network and try to apply it in the field of image classification.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126024745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016595
Lin Juchuang, Zhu Anmin
Solar power is one of the abundant renewable energy sources. But the power generation capacity of photovoltaic power plants fluctuates significantly due to changes in weather conditions. In this paper, a new model is proposed, which consists of convolutional neural network, wavelet transform and support vector machine (CWS). Firstly, the features of the original data are expanded through the convolutional neural network (CNN). And then the wavelet transform is introduced to suppress the noise in the expanded data. Finally, the output power of the photovoltaic power station is predicted by the support vector regression (SVR) method. The experimental results show that the prediction accuracy and training time of the new model show obvious advantages compared with the previous BI-LSTM (Bidirectional Long Short Term Memory), LS-SPP (LSTM-Based Solar Power Prediction) and LSTM under different prediction time ranges.
{"title":"CNN-Wavelet-Transform-Based Model for Solar Photovoltaic Power Prediction","authors":"Lin Juchuang, Zhu Anmin","doi":"10.1109/ICCWAMTIP56608.2022.10016595","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016595","url":null,"abstract":"Solar power is one of the abundant renewable energy sources. But the power generation capacity of photovoltaic power plants fluctuates significantly due to changes in weather conditions. In this paper, a new model is proposed, which consists of convolutional neural network, wavelet transform and support vector machine (CWS). Firstly, the features of the original data are expanded through the convolutional neural network (CNN). And then the wavelet transform is introduced to suppress the noise in the expanded data. Finally, the output power of the photovoltaic power station is predicted by the support vector regression (SVR) method. The experimental results show that the prediction accuracy and training time of the new model show obvious advantages compared with the previous BI-LSTM (Bidirectional Long Short Term Memory), LS-SPP (LSTM-Based Solar Power Prediction) and LSTM under different prediction time ranges.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125336961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016488
Yu Fan, Qingwen Liu
Machine reading comprehension (MRC) is one of the main research of question answering system tasks. In real-world scenarios, due to the large scale of document data, and many questions that need to be answered urgently, traditional extractive MRC method faces low efficiency, low accuracy, and has poor reasoning ability. In order to efficiently and accurately extract knowledge from massive unstructured text data, we propose a multi-document MRC model based on a two-stage approach. Through document retrieval and answer reasoning, the model can hierarchically match the relevant passages from coarse-to-fine. Experimental results show that our method outperforms baseline and improves Rouge-L and BLEU-4 by 2.5% and 1.9%, respectively. In addition, the reasoning process is interpretable and can provide support for information understanding.
{"title":"A Retriever-Reasoner Method for Multi-Document Reading Comprehension","authors":"Yu Fan, Qingwen Liu","doi":"10.1109/ICCWAMTIP56608.2022.10016488","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016488","url":null,"abstract":"Machine reading comprehension (MRC) is one of the main research of question answering system tasks. In real-world scenarios, due to the large scale of document data, and many questions that need to be answered urgently, traditional extractive MRC method faces low efficiency, low accuracy, and has poor reasoning ability. In order to efficiently and accurately extract knowledge from massive unstructured text data, we propose a multi-document MRC model based on a two-stage approach. Through document retrieval and answer reasoning, the model can hierarchically match the relevant passages from coarse-to-fine. Experimental results show that our method outperforms baseline and improves Rouge-L and BLEU-4 by 2.5% and 1.9%, respectively. In addition, the reasoning process is interpretable and can provide support for information understanding.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"221 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122930785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016565
Xie Tao
With the rapid development of computer and microelectronics technology, intelligent vehicle technology has become the latest development frontier of the automobile industry. In order to deeply explore and study the control problems of intelligent vehicles, we have carried out research in the following aspects: the construction of electric four-wheel steering and four-wheel drive mobile platform, the configuration of multi-layer sensing sensors on the mobile platform, and the Development and experimental validation of vehicle motion control algorithms. Based on the preliminary design of the mobile platform construction and sensor configuration scheme, and the completion of the power part construction, we study the motion model of the mobile platform, and add a PID controller to realize the stable control of the longitudinal and steering of the mobile platform. We have finished the structure construction of the mobile platform and the configuration of the sensors and have completed the preliminary debugging of the platform. And eventually we realize the basic performance of the platform, and verify the developed algorithm on the mobile platform.
{"title":"Research on Multilayer Perception and Control of Intelligent Vehicles","authors":"Xie Tao","doi":"10.1109/ICCWAMTIP56608.2022.10016565","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016565","url":null,"abstract":"With the rapid development of computer and microelectronics technology, intelligent vehicle technology has become the latest development frontier of the automobile industry. In order to deeply explore and study the control problems of intelligent vehicles, we have carried out research in the following aspects: the construction of electric four-wheel steering and four-wheel drive mobile platform, the configuration of multi-layer sensing sensors on the mobile platform, and the Development and experimental validation of vehicle motion control algorithms. Based on the preliminary design of the mobile platform construction and sensor configuration scheme, and the completion of the power part construction, we study the motion model of the mobile platform, and add a PID controller to realize the stable control of the longitudinal and steering of the mobile platform. We have finished the structure construction of the mobile platform and the configuration of the sensors and have completed the preliminary debugging of the platform. And eventually we realize the basic performance of the platform, and verify the developed algorithm on the mobile platform.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128233739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016481
Tan Zhiguo, Shi Longfei, Yang Xiaofan, Teng Shuhua
To solve the transceiver separation system’s synchronization, the requirement of additional communication links, and the radio pulse chasing problems of the bistatic radar, this paper proposes a method of locating stealth targets by distributed communicational radar. Through the information-embedded waveform design, the system uses the wide-beam detection method without additional communication links, which effectively solves the beam chasing problem of the traditional bistatic radar, greatly shortens the target detection and location time and increases the probability of target acquisition. In the paper, the problems of the existing multi-static radar are analyzed, the distributed communication radar technology is introduced, and the target detection by wide radar beams is discussed. We give the basic detecting unit for target detection and location in wide beam mode. On this basis, the detection area and location performance of the basic detection unit are analyzed. The simulation experiment verifies the feasibility and effectiveness of the scheme.
{"title":"Research on Location Method of Distributed Communicational Radar","authors":"Tan Zhiguo, Shi Longfei, Yang Xiaofan, Teng Shuhua","doi":"10.1109/ICCWAMTIP56608.2022.10016481","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016481","url":null,"abstract":"To solve the transceiver separation system’s synchronization, the requirement of additional communication links, and the radio pulse chasing problems of the bistatic radar, this paper proposes a method of locating stealth targets by distributed communicational radar. Through the information-embedded waveform design, the system uses the wide-beam detection method without additional communication links, which effectively solves the beam chasing problem of the traditional bistatic radar, greatly shortens the target detection and location time and increases the probability of target acquisition. In the paper, the problems of the existing multi-static radar are analyzed, the distributed communication radar technology is introduced, and the target detection by wide radar beams is discussed. We give the basic detecting unit for target detection and location in wide beam mode. On this basis, the detection area and location performance of the basic detection unit are analyzed. The simulation experiment verifies the feasibility and effectiveness of the scheme.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128369168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016607
Wang Qianchun, Jonathan M. Caballero
According to the characteristics of high-speed, low delay, large-capacity data transmission and various scenarios in 5G mobile communication, an improved decoding algorithm based on LDPC code for 5G is proposed. The algorithm adopts dynamic weighting decoding method to dynamically weight the information. In this way, the iteration can be accelerated when the information is transmitted, to reduce the number of iterations and improve the gain at the same time. From the point of view that 5G channel coding needs to support more bit rates, the simulation results show that compared with the traditional decoding algorithm, the performance of the improved MS algorithm is improved by about 0.4dB, and the dynamic weighting method improves the decoding performance and reduces the number of decoding iterations.
{"title":"Simulation of Dynamic Weighted MS Decoding Algorithm on LDPC for 5G","authors":"Wang Qianchun, Jonathan M. Caballero","doi":"10.1109/ICCWAMTIP56608.2022.10016607","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016607","url":null,"abstract":"According to the characteristics of high-speed, low delay, large-capacity data transmission and various scenarios in 5G mobile communication, an improved decoding algorithm based on LDPC code for 5G is proposed. The algorithm adopts dynamic weighting decoding method to dynamically weight the information. In this way, the iteration can be accelerated when the information is transmitted, to reduce the number of iterations and improve the gain at the same time. From the point of view that 5G channel coding needs to support more bit rates, the simulation results show that compared with the traditional decoding algorithm, the performance of the improved MS algorithm is improved by about 0.4dB, and the dynamic weighting method improves the decoding performance and reduces the number of decoding iterations.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128802969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABS TV bracket is the main load-bearing part to support TV. The elastic modulus, yield strength, etc. of the ABS TV bracket are crucial and are the key parameters to support TV for a long time. During transportation, the TV support will suffer from alternating fatigue damage under the action of alternating vibration. By combining the polymer fatigue hysteresis theory with the cyclic failure constitutive, the UMAT subroutine was compiled, the material subroutine of the Abaqus finite element software was developed, and the fatigue loss finite element model suitable for ABS was established. Taking the TV bracket as the research object, the failure mechanism and failure process of the TV bracket under vibration fatigue are studied. The research results of this paper provide optimization directions for product design, which help to reduce product development costs and shorten development cycles.
{"title":"Failure Analysis of ABS Tv Bracket Based on Abaqus Fatigue Damage Redevelopment","authors":"Bian Tinghui, Fang Xiaoyong, Duan Ran, Chen Qimao, Zhang Xin","doi":"10.1109/ICCWAMTIP56608.2022.10016613","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016613","url":null,"abstract":"ABS TV bracket is the main load-bearing part to support TV. The elastic modulus, yield strength, etc. of the ABS TV bracket are crucial and are the key parameters to support TV for a long time. During transportation, the TV support will suffer from alternating fatigue damage under the action of alternating vibration. By combining the polymer fatigue hysteresis theory with the cyclic failure constitutive, the UMAT subroutine was compiled, the material subroutine of the Abaqus finite element software was developed, and the fatigue loss finite element model suitable for ABS was established. Taking the TV bracket as the research object, the failure mechanism and failure process of the TV bracket under vibration fatigue are studied. The research results of this paper provide optimization directions for product design, which help to reduce product development costs and shorten development cycles.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123973647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.1109/ICCWAMTIP56608.2022.10016533
Zha Qinglin, Ouyang Guohua, Hu Jianli
Millimeter-wave radar high-resolved one-dimensional distance image (HRRP) provides information about the target longitudinal length (with significant back scattering parts) and the location of strong scattering points, such as the armored target barrel, tracks, and other strong scattering centers. Reflecting the target size, shape, and electrical performance feature information, which forms the basis of HRRP target identification. After obtaining HRRP features, how to maximize the distinction of different targets and improve the target recognition rate is the problem to be solved by the identifier design. Therefore, how to design the appropriate target classification according to the target features determines the target recognition performance of the whole system. According to the characteristic analysis of HRRP, solving the orientation, translation and intensity sensitivity of high resolution distance image sample is the basis of radar high resolution distance image target recognition, which runs through every link in the target recognition process, and is also a problem that must be considered in the identifier design of the radar. Therefore, this paper first introduces the general idea of HRRP template matching target identification, and then expounds the specific classifier principle and classification method. On this basis, the target electromagnetic simulation data is used to verify various classification methods.
{"title":"Target Identification Technology of Millimeter-Wave Radar Based on Feature Template","authors":"Zha Qinglin, Ouyang Guohua, Hu Jianli","doi":"10.1109/ICCWAMTIP56608.2022.10016533","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016533","url":null,"abstract":"Millimeter-wave radar high-resolved one-dimensional distance image (HRRP) provides information about the target longitudinal length (with significant back scattering parts) and the location of strong scattering points, such as the armored target barrel, tracks, and other strong scattering centers. Reflecting the target size, shape, and electrical performance feature information, which forms the basis of HRRP target identification. After obtaining HRRP features, how to maximize the distinction of different targets and improve the target recognition rate is the problem to be solved by the identifier design. Therefore, how to design the appropriate target classification according to the target features determines the target recognition performance of the whole system. According to the characteristic analysis of HRRP, solving the orientation, translation and intensity sensitivity of high resolution distance image sample is the basis of radar high resolution distance image target recognition, which runs through every link in the target recognition process, and is also a problem that must be considered in the identifier design of the radar. Therefore, this paper first introduces the general idea of HRRP template matching target identification, and then expounds the specific classifier principle and classification method. On this basis, the target electromagnetic simulation data is used to verify various classification methods.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126208361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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