To achieve the integration of radar and jammer, we explore the power allocation problem of multicarrier waveform frequency points to improve the spectral efficiency of the radar and jammer systems coexisting in the same bandwidth. First, the conditional mutual information of random target impulse response and echo signal, and the output signal-to-jamming-noise ratio of the enemy radar under suppressed jamming are established as indicators of the detection and jamming performance of the integrated system, respectively. Then, with the constraint on the total power, the optimization problem, which simultaneously considers the conditional MI for radar and SJNR for jamming, is designed and solved. The designed waveform outperforms the conventional equal power allocation waveform under the limited transmit power. Finally, the effectiveness of the designed waveform is verified by several simulated experiments.
{"title":"Power Allocation Method for Coexistence of Multicarrier Radar and Jamming System","authors":"Zhuochen Chen, Shengqi Zhu, Yongjun Liu, Ximin Li, Yanxing Wang, Feilong Liu","doi":"10.1109/ICICSP55539.2022.10050662","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050662","url":null,"abstract":"To achieve the integration of radar and jammer, we explore the power allocation problem of multicarrier waveform frequency points to improve the spectral efficiency of the radar and jammer systems coexisting in the same bandwidth. First, the conditional mutual information of random target impulse response and echo signal, and the output signal-to-jamming-noise ratio of the enemy radar under suppressed jamming are established as indicators of the detection and jamming performance of the integrated system, respectively. Then, with the constraint on the total power, the optimization problem, which simultaneously considers the conditional MI for radar and SJNR for jamming, is designed and solved. The designed waveform outperforms the conventional equal power allocation waveform under the limited transmit power. Finally, the effectiveness of the designed waveform is verified by several simulated experiments.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"635 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116212212","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 : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050688
Jing Xiang, Wenqiang Fan, Peng Liu, Mengxia Wang
For the traditional Iterative Closest Point (ICP) algorithm, its registration efficiency is low and the initial position of the registered point cloud is high. Accordingly, a point cloud registration method combining the optimized Intrinsic Shape Signatures (ISS) algorithm with the improved ICP is proposed. Specifically, the voxel filter is used to sample the original point cloud, then the key points are extracted by optimizing the search radius of the ISS algorithm, and described by fast point feature histogram (FPFH), and the corresponding relationship is established according to the feature. Subsequently, the normal features and the RANSAC algorithm are fused to eliminate the mismatching point pairs, and the initial transformation matrix is obtained by singular value decomposition(SVD). Finally, the ICP algorithm with median distance constraint is used to complete the precise registration. Experiments suggest that the accuracy and efficiency of the proposed algorithm are significantly improved compared with the traditional ICP algorithm.
{"title":"Point Cloud Alignment Method Based on Improved ISS-ICP Algorithm","authors":"Jing Xiang, Wenqiang Fan, Peng Liu, Mengxia Wang","doi":"10.1109/ICICSP55539.2022.10050688","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050688","url":null,"abstract":"For the traditional Iterative Closest Point (ICP) algorithm, its registration efficiency is low and the initial position of the registered point cloud is high. Accordingly, a point cloud registration method combining the optimized Intrinsic Shape Signatures (ISS) algorithm with the improved ICP is proposed. Specifically, the voxel filter is used to sample the original point cloud, then the key points are extracted by optimizing the search radius of the ISS algorithm, and described by fast point feature histogram (FPFH), and the corresponding relationship is established according to the feature. Subsequently, the normal features and the RANSAC algorithm are fused to eliminate the mismatching point pairs, and the initial transformation matrix is obtained by singular value decomposition(SVD). Finally, the ICP algorithm with median distance constraint is used to complete the precise registration. Experiments suggest that the accuracy and efficiency of the proposed algorithm are significantly improved compared with the traditional ICP algorithm.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121472954","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 : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050611
B. Yu, Zhan Zhang, Ding Zhao, Yuehai Wang
In daily interactions, human speech perception is inherently a multi-modality process. Audio-visual speech enhancement (AV-SE) aims to aid speech enhancement with the help of visual information. However, the fusion strategy of most AV-SE approaches is too simple, resulting in the dominance of audio modality. The visual modality is usually ignored, especially when the signal-to-noise ratio (SNR) is medium or high. This paper proposes an encoder-decoder-based convolutional neural network of AV-SE with deep multi-modality fusion. The deep multi-modality fusion uses temporal attention to align multi-modality features selectively and preserves the temporal correlation by linear interpolation. The novel fusion strategy can take full advantage of video features, leading to a balanced multi-modality representation. To further improve the performance of AV-SE, mixed deep feature loss is introduced. Two neural networks are applied to model the characteristics of speech and noise signals, respectively. The experiment conducted on NTCD-TIMIT demonstrates the effectiveness of our proposed model. Compared to audio-only baseline and simple fusion approaches, our model achieves better performance in objective metrics under all SNR conditions.
{"title":"Audio-Visual Speech Enhancement with Deep Multi-modality Fusion","authors":"B. Yu, Zhan Zhang, Ding Zhao, Yuehai Wang","doi":"10.1109/ICICSP55539.2022.10050611","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050611","url":null,"abstract":"In daily interactions, human speech perception is inherently a multi-modality process. Audio-visual speech enhancement (AV-SE) aims to aid speech enhancement with the help of visual information. However, the fusion strategy of most AV-SE approaches is too simple, resulting in the dominance of audio modality. The visual modality is usually ignored, especially when the signal-to-noise ratio (SNR) is medium or high. This paper proposes an encoder-decoder-based convolutional neural network of AV-SE with deep multi-modality fusion. The deep multi-modality fusion uses temporal attention to align multi-modality features selectively and preserves the temporal correlation by linear interpolation. The novel fusion strategy can take full advantage of video features, leading to a balanced multi-modality representation. To further improve the performance of AV-SE, mixed deep feature loss is introduced. Two neural networks are applied to model the characteristics of speech and noise signals, respectively. The experiment conducted on NTCD-TIMIT demonstrates the effectiveness of our proposed model. Compared to audio-only baseline and simple fusion approaches, our model achieves better performance in objective metrics under all SNR conditions.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121523448","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}
Traditional identification methods often depend on the validity of training data set and the rationality of parameter selection, which leads to the decrease of availability. A comprehensive recognition method based on Bayes reasoning and SVM classifier is proposed in this paper to address the difficulty of radar operating pattern recognition under non-cooperative confrontation and jamming pulse conditions. According to the tactical application characteristics and hierarchical structure of radar operation mode, a feature parameter extraction method based on CPI is constructed. And the pattern recognition rate Bayes inference algorithm is improved base on the SVM algorithm. Simulation results show that the accuracy of this method is improved by 1.37% on average, and is 98.28% and 92.79% respectively under cooperative and non-cooperative confrontation, which proves the effectiveness of the algorithm.
{"title":"Airborne Multi-function Radar Air-to-air Working Pattern Recognition Based on Bayes Inference and SVM","authors":"Jingwei Xiong, Jifei Pan, Yihong Zhuo, Linqing Guo","doi":"10.1109/ICICSP55539.2022.10050681","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050681","url":null,"abstract":"Traditional identification methods often depend on the validity of training data set and the rationality of parameter selection, which leads to the decrease of availability. A comprehensive recognition method based on Bayes reasoning and SVM classifier is proposed in this paper to address the difficulty of radar operating pattern recognition under non-cooperative confrontation and jamming pulse conditions. According to the tactical application characteristics and hierarchical structure of radar operation mode, a feature parameter extraction method based on CPI is constructed. And the pattern recognition rate Bayes inference algorithm is improved base on the SVM algorithm. Simulation results show that the accuracy of this method is improved by 1.37% on average, and is 98.28% and 92.79% respectively under cooperative and non-cooperative confrontation, which proves the effectiveness of the algorithm.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132480151","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 : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050694
Shuyao Lu, Jun Wang, Zihan Wu
Coherent sources often exist due to various factors such as multipath effects and electronic interference. How to estimate the parameters of coherent sources is a significant part of spatial spectrum estimation. The traditional algorithm for coherent signals has the defect of losing the effective aperture of the array, which affects the accuracy and resolution of the estimation. To solve the problem, this paper models coherent DOA estimation as multi-label classification based on neural network. Sparse autoencoder, spatial filter, and multiple parallel DNN classifiers are employed to complete the multi-label classification task. The whole framework can also adapt to close DOA scenario, and simulation results have demonstrated the superiority of the method. Moreover, this paper discussed the reason of DOA estimation failure and a staggered grid method is utilized to improve the classification accuracy.
{"title":"A Novel Neural Network Approach for Coherent Source DOA Estimation","authors":"Shuyao Lu, Jun Wang, Zihan Wu","doi":"10.1109/ICICSP55539.2022.10050694","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050694","url":null,"abstract":"Coherent sources often exist due to various factors such as multipath effects and electronic interference. How to estimate the parameters of coherent sources is a significant part of spatial spectrum estimation. The traditional algorithm for coherent signals has the defect of losing the effective aperture of the array, which affects the accuracy and resolution of the estimation. To solve the problem, this paper models coherent DOA estimation as multi-label classification based on neural network. Sparse autoencoder, spatial filter, and multiple parallel DNN classifiers are employed to complete the multi-label classification task. The whole framework can also adapt to close DOA scenario, and simulation results have demonstrated the superiority of the method. Moreover, this paper discussed the reason of DOA estimation failure and a staggered grid method is utilized to improve the classification accuracy.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129999415","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 : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050621
Kun Shen, W. Xie, Haojin Tang, Yanshan Li
Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. Therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. Therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. The main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. Then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. The experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors.
{"title":"Dense Convolution Siamese Network for Hyperspectral Image Target Detection","authors":"Kun Shen, W. Xie, Haojin Tang, Yanshan Li","doi":"10.1109/ICICSP55539.2022.10050621","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050621","url":null,"abstract":"Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. Therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. Therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. The main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. Then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. The experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134382952","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 : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050654
Xuetao Wan, Jie Wang, Jiamin Li, Xinhong Liu, Xuequn Fu, Xiaotian Wang
With the emergence of air-ground integrated network, compared with the traditional network, the network conditions are more complex, which puts forward higher requirements for satellite packet classification. Although the demand is urgent, there is no research on satellite quality of service classifier algorithm. A challenging problem in this area is the use of an algorithm that can classify packets at high speed and with relatively low memory consumption. In this paper, the performance of the modified recursive flow classification (MRFC) algorithm under different preprocessing schemes is introduced and compared. First of all, we use classbench to generate different number of filter sets. And then carry out experiments to compare the advantages and disadvantages of different preprocessing schemes. Finally, the comparison of packet preprocessing time, storage space and search time is given. The results show that the proposed preprocessing scheme of MRFC algorithm has better performance on satellite QoS classifier. This can greatly save the speed of classifying data packets by satellite QoS classifier and reduce the delay in the case of massive packets.
{"title":"Research on Satellite Quality of Service Classifier Based on Modified Recursive Flow Classification Algorithm","authors":"Xuetao Wan, Jie Wang, Jiamin Li, Xinhong Liu, Xuequn Fu, Xiaotian Wang","doi":"10.1109/ICICSP55539.2022.10050654","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050654","url":null,"abstract":"With the emergence of air-ground integrated network, compared with the traditional network, the network conditions are more complex, which puts forward higher requirements for satellite packet classification. Although the demand is urgent, there is no research on satellite quality of service classifier algorithm. A challenging problem in this area is the use of an algorithm that can classify packets at high speed and with relatively low memory consumption. In this paper, the performance of the modified recursive flow classification (MRFC) algorithm under different preprocessing schemes is introduced and compared. First of all, we use classbench to generate different number of filter sets. And then carry out experiments to compare the advantages and disadvantages of different preprocessing schemes. Finally, the comparison of packet preprocessing time, storage space and search time is given. The results show that the proposed preprocessing scheme of MRFC algorithm has better performance on satellite QoS classifier. This can greatly save the speed of classifying data packets by satellite QoS classifier and reduce the delay in the case of massive packets.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117337251","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 : 2022-11-26DOI: 10.1109/ICICSP55539.2022.10050544
Pengcheng Gong, Junjia Zhang, Yuntao Wu, Liang Yu, Lirong Li
Some characteristics of array in superdirective beamformers contradict each other such as spatially white noise and position errors. The white noise amplification of the array itself is the main problem to be solved in superdirective beamforming, which can be evaluated by a robustness measure, the White Noise Gain (WNG). In this paper, we present an optimization problem for beamformer design with an efficient method which incorporates constraints for the WNG and least-squares for constant beamwidth into beamformer design, and then solve it based on alternating direction penalty method (ADPM) Algorithm. The effectiveness of the proposed method is demonstrated by numerical results.
{"title":"Robust Superdirective Beamforming Based on ADPM","authors":"Pengcheng Gong, Junjia Zhang, Yuntao Wu, Liang Yu, Lirong Li","doi":"10.1109/ICICSP55539.2022.10050544","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050544","url":null,"abstract":"Some characteristics of array in superdirective beamformers contradict each other such as spatially white noise and position errors. The white noise amplification of the array itself is the main problem to be solved in superdirective beamforming, which can be evaluated by a robustness measure, the White Noise Gain (WNG). In this paper, we present an optimization problem for beamformer design with an efficient method which incorporates constraints for the WNG and least-squares for constant beamwidth into beamformer design, and then solve it based on alternating direction penalty method (ADPM) Algorithm. The effectiveness of the proposed method is demonstrated by numerical results.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115232923","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}
A large number of transient ice events exist in underwater ambient noise in the Arctic Ocean, and they are believed to be the main contributor to underwater noise different from contributors in other oceans. This paper studied the underwater ambient noise (CHINARE18 and CHINARE20) recorded during the 9th Chinese National Arctic Research Expedition and the 11th Chinese National Arctic Research Expedition. The time-frequency analysis of two 7-s-long ice noise samples in the two experiments shows that ice transient events have impulsivity and wide spectrum. The statistical result of transient ice events in different frequency bands reveals that these events mainly contribute to the high-frequency component of under-ice ambient noise. It can be seen in the power spectrum that transient energy is apparent in the 90th and 99th percentile curves. Due to the influence of ice transient events, the CHINARE18 has obvious fluctuations in the spectral kurtosis in a certain frequency band (4kHz-8kHz).
{"title":"Transient Ice Events of Underwater Ambient Noise in the Arctic Ocean","authors":"Xueli Sheng, Meng Liu, Mengfei Mu, Chaoran Yang, Jing Xu","doi":"10.1109/ICICSP55539.2022.10050649","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050649","url":null,"abstract":"A large number of transient ice events exist in underwater ambient noise in the Arctic Ocean, and they are believed to be the main contributor to underwater noise different from contributors in other oceans. This paper studied the underwater ambient noise (CHINARE18 and CHINARE20) recorded during the 9th Chinese National Arctic Research Expedition and the 11th Chinese National Arctic Research Expedition. The time-frequency analysis of two 7-s-long ice noise samples in the two experiments shows that ice transient events have impulsivity and wide spectrum. The statistical result of transient ice events in different frequency bands reveals that these events mainly contribute to the high-frequency component of under-ice ambient noise. It can be seen in the power spectrum that transient energy is apparent in the 90th and 99th percentile curves. Due to the influence of ice transient events, the CHINARE18 has obvious fluctuations in the spectral kurtosis in a certain frequency band (4kHz-8kHz).","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124751530","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}
Obtaining the scatterer slant range and scatterer trajectory association are two crucial steps of 3-D target imaging from the inverse synthetic aperture radar (ISAR) image sequence or high resolution range profiles (HRRP) series. However, scatterers are drowned out by noise at low SNR so that the scatterer slant range cannot be obtained. In addition, the minimum Euclidean distance criterion is a common trajectory method, which will lead to wrong association results when the scatterer trajectory has crossings. To tackle above problems, a novel ISAR 3-D imaging method based on Generalized Radon-Fourier Transform (GRFT) is proposed. In this method, the slant range history of the scatterer is reconstructed based on GRFT without trajectory association. In addition, GRFT is a parameter estimation technique with robustness at low SNR. The 3-D image of the target is obtained using the factorization method. Simulation results prove the effectiveness of the proposed method.
{"title":"Three-Dimensional ISAR Imaging under Low SNR","authors":"Shujiang Liu, Yongpeng Gao, Zegang Ding, Tianyi Zhang, Zhi Yang, Guanxing Wang","doi":"10.1109/ICICSP55539.2022.10050646","DOIUrl":"https://doi.org/10.1109/ICICSP55539.2022.10050646","url":null,"abstract":"Obtaining the scatterer slant range and scatterer trajectory association are two crucial steps of 3-D target imaging from the inverse synthetic aperture radar (ISAR) image sequence or high resolution range profiles (HRRP) series. However, scatterers are drowned out by noise at low SNR so that the scatterer slant range cannot be obtained. In addition, the minimum Euclidean distance criterion is a common trajectory method, which will lead to wrong association results when the scatterer trajectory has crossings. To tackle above problems, a novel ISAR 3-D imaging method based on Generalized Radon-Fourier Transform (GRFT) is proposed. In this method, the slant range history of the scatterer is reconstructed based on GRFT without trajectory association. In addition, GRFT is a parameter estimation technique with robustness at low SNR. The 3-D image of the target is obtained using the factorization method. Simulation results prove the effectiveness of the proposed method.","PeriodicalId":281095,"journal":{"name":"2022 5th International Conference on Information Communication and Signal Processing (ICICSP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128374181","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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