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2018 International Conference on Radar (RADAR)最新文献

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Robust Absolute Calibration for SAR SAR的鲁棒绝对校准
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557317
D. Crisp
{"title":"Robust Absolute Calibration for SAR","authors":"D. Crisp","doi":"10.1109/RADAR.2018.8557317","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557317","url":null,"abstract":"We describe two radiometric calibration issues using in-scene corner reflectors acquired with the DST Ingara experimental synthetic aperture radar (SAR) system. The two problems were: 1) the radar beam illumination of the scene had an unknown offset in the azimuth direction and 2) the radar receivers saturated during calibration collections corrupting some of the measured data. Non-standard techniques for overcoming these problems have been developed and their utility proven on real data. These techniques are robust in that they use as many valid data samples as possible and a model fitting process to estimate the relevant calibration parameters.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124392442","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}
引用次数: 2
Human Activities Detection and Classification Based on Micro-Doppler Signatures Near the Baseline of Forward Scattering Radar 基于前向散射雷达基线附近微多普勒特征的人类活动检测与分类
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557303
A. Alnaeb, Raja Syamsul, Azmir Raja Abdullah, A. Salah, A. Sali, N. E. Rashid, I. Pasya
{"title":"Human Activities Detection and Classification Based on Micro-Doppler Signatures Near the Baseline of Forward Scattering Radar","authors":"A. Alnaeb, Raja Syamsul, Azmir Raja Abdullah, A. Salah, A. Sali, N. E. Rashid, I. Pasya","doi":"10.1109/RADAR.2018.8557303","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557303","url":null,"abstract":"Fall poses a major problem, which raises the concern of elderly populations aged 65 and above in all over the world. In this paper, we propose Forward Scattering Radar system as a Doppler sensor in distinguishing features of fall events from non-fall activities. The signal features of joint time-frequency representations are used for detection, while the support vector machine, which is based on the short-time Fourier transform feature, has been used in the classification process. An indoor experiment was conducted to emulate the elderly people's daily activities and the falling down event, where 50 trials were carried out by five adults for each of the activity. The detection results indicated that the forward scattering radar has a high ability in detecting the micro-Doppler signatures generated from the low speed motion of a human body segments during daily activities. The preliminary classification results are 100% for the corresponding free fall-sitting on a chair, free fall-sitting on the floor, and for all three activities.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121775168","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}
引用次数: 3
Human Activity Recognition Based on Deep Learning Method 基于深度学习方法的人类活动识别
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557335
Xiaoran Shi, Yaxin Li, Feng Zhou, Lei Liu
{"title":"Human Activity Recognition Based on Deep Learning Method","authors":"Xiaoran Shi, Yaxin Li, Feng Zhou, Lei Liu","doi":"10.1109/RADAR.2018.8557335","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557335","url":null,"abstract":"With the increasing demand of security defense., anti-terrorism investigation and disaster rescue., human activity classification and recognition have become a hot research topic. When a human is illuminated by electromagnetic waves., a Doppler signal is generated from his or her moving parts. Indeed., bodily movements are what make humans micro-Doppler signatures unique., offering a chance to classify human activities. Classification needs a lot of samples for training., however., in the real application., there is a certain gap between the simulated data and the real data., and the measured data is often difficult to obtain. Due to the nonstationary characteristic for human radar echoes., the spectrograms for the human activities show different micro-Doppler signatures. Therefore., we proposed a method of human activity classification based on spectrograms using deep learning techniques., including deep convolutional generative adversarial network for expanding and enriching training set and a transfer-learned deep convolutional network (DCNN) for feature extraction and classification., which is based on a DCNN pre-trained by a large-scale RGB image data set-that is., ImageNet. Finally., the simulation results verified the effectiveness of the proposed method.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123453243","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}
引用次数: 25
ISAR Multi-Band Fusion Based on Attributed Scattering Center 基于属性散射中心的ISAR多波段融合
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557279
Yu Ning, Feng Zhou, Lei Liu, Xueru Bai
{"title":"ISAR Multi-Band Fusion Based on Attributed Scattering Center","authors":"Yu Ning, Feng Zhou, Lei Liu, Xueru Bai","doi":"10.1109/RADAR.2018.8557279","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557279","url":null,"abstract":"High resolution Inverse synthetic aperture radar imaging is an important technique for aerospace targets recognition. Multi-band fusion method greatly improves the radar bandwidth without the increasing of hardware cost. Traditional Multi-band fusion methods usually adopt damped exponential model or geometrical theory of diffraction model, but these models are not precise enough, especially when the target is complex. This paper proposes a novel multi-band fusion method based on attributed scattering center model. Firstly, The ASCs of different subbands are extracted using zoom dictionary; Secondly, the vacant band is recovered; Finally, the coherence phase is compensated, and the full-band signal is obtained. Compared with traditional methods, the proposed method has higher range resolution.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131042923","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}
引用次数: 0
Electronic Countermeasure for OFDM-Based Imaging Passive Radars 基于ofdm的无源成像雷达电子对抗
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557316
E. Giusti, A. Capria, M. Martorella, C. Moscardini, F. Berizzi
{"title":"Electronic Countermeasure for OFDM-Based Imaging Passive Radars","authors":"E. Giusti, A. Capria, M. Martorella, C. Moscardini, F. Berizzi","doi":"10.1109/RADAR.2018.8557316","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557316","url":null,"abstract":"Passive radars are notoriously robust to jammer because the receiver location cannot be detected. Furthermore the use of OFDM based signal as reference signal further improve the robustness against jammers. Nevertheless this paper aims at proposing an electronic countermeasure for OFDM-based passive radars. In this paper a target-on-board deception jammer is proposed that aims at protecting the target itself from detection, localization and identification. The proposed system is required to operate in real time, which could be seen as a quite challenging objective. However the recent technological advances make the realization of a system able to sample a broadband signal, modulate it in amplitude and phase and retransmit it with a very short time delay, likely and possible.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128052625","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}
引用次数: 10
Maneuvering Target Tracking with Recurrent Neural Networks for Radar Application 基于递归神经网络的机动目标跟踪在雷达中的应用
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557284
Chang Gao, Hongwei Liu, Shenghua Zhou, Hongtao Su, Bo Chen, Junkun Yan, Kuiying Yin
{"title":"Maneuvering Target Tracking with Recurrent Neural Networks for Radar Application","authors":"Chang Gao, Hongwei Liu, Shenghua Zhou, Hongtao Su, Bo Chen, Junkun Yan, Kuiying Yin","doi":"10.1109/RADAR.2018.8557284","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557284","url":null,"abstract":"Maneuvering target tracking is a problem of state estimation where the system undergoes abrupt changes. Traditional model-based approaches to this problem are threatened by the performance degradation caused by the model mismatch and the estimator usually has limited statistical precision in practice. Developed from what the machine sees as mathematically optimal in the data, deep neural network-based methods are not sensitive to variant models as long as the interested motions are fully contained in the training data. Besides, having access to the true state while training a network can make it possible to break the limit of traditional estimation precision. To coincide with the sequential manner of target tracking, the recurrent neural network is proposed to estimate the true state. Simulation results show that the proposed network handles the target motion uncertainty problem well, meanwhile, the states are estimated more accurately.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126277209","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}
引用次数: 17
Radar Cross Section of Modified Target Using Gaussian Beam Methods: Experimental Validation 高斯波束法修正目标雷达截面的实验验证
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557258
Ghanmi Helmi, Ali Khenchaf, P. Pouliguen
{"title":"Radar Cross Section of Modified Target Using Gaussian Beam Methods: Experimental Validation","authors":"Ghanmi Helmi, Ali Khenchaf, P. Pouliguen","doi":"10.1109/RADAR.2018.8557258","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557258","url":null,"abstract":"The aim of this paper is to study the Radar Cross Section (RCS) of modified radar targets (plate with notch) using Gaussian Beam techniques. The Gaussian methods used in this work are Gaussian Beam Summation (GBS) and Gaussian Beam Launching (GBL). We establish the theoretical formulation of the GBS and GBL techniques and analyze the influence of the main Gaussian beam parameters on the variation of the scattered field. Then, we present the simulations of RCS. The numerical results are compared with PO, MoM methods, and also with experimental measurements performed in the anechoic chamber at Lab-STICC (ENSTA Bretagne).","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127061545","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}
引用次数: 4
Tracker Operating Characteristic for Integrated Probabilistic Data Association 集成概率数据关联的跟踪器工作特性
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557232
I. Vaughan, L. Clarkson, Jason L. Williams
{"title":"Tracker Operating Characteristic for Integrated Probabilistic Data Association","authors":"I. Vaughan, L. Clarkson, Jason L. Williams","doi":"10.1109/RADAR.2018.8557232","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557232","url":null,"abstract":"Integrated Probabilistic Data Association (IPDA) is a popular approach to target tracking and to track initiation and termination. In regards to track initiation, it may be said to belong to the “detect-then-track” class in which processing begins with sensor-level detections. This is in contrast to “track-before-detect” (TkBD) approaches in which un-thresholded sensor-level measurements are directly incorporated into a track-initiation statistic. In IPDA, the track-initiation statistic is the probability of existence (PoE). How should the confirmation threshold be set? We perform a statistical analysis that yields an accurate approximation of the false-track and track detection probabilities as a function of the threshold on the PoE. From these probabilities, IPDA's tracker operating characteristic (TOC) can be derived. We further propose a tracker using similar ideas but which belongs to the TkBD class. Its TOC is likewise derived and compared with IPDA. The results of numerical simulations are presented which support the theoretical derivations.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114462795","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}
引用次数: 3
Prior Knowledge Based Transmit Nulling Method for MIMO Radar 基于先验知识的MIMO雷达发射零化方法
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557262
Hao Zheng, B. Jiu, Hongwei Liu, Yuan Liu, Xu Wang
{"title":"Prior Knowledge Based Transmit Nulling Method for MIMO Radar","authors":"Hao Zheng, B. Jiu, Hongwei Liu, Yuan Liu, Xu Wang","doi":"10.1109/RADAR.2018.8557262","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557262","url":null,"abstract":"In this paper, a multiple-input-multiple-output (MIMO) radar transmit nulling method is proposed in the presence of fast moving interference. The MIMO radar transmit waveform design is divided into two steps: transmit waveform covariance matrix optimization and transmit waveform synthesis to approximate the optimized covariance matrix. In the first step, the prior knowledge of interference is utilized to optimize covariance matrix and the derivative constraint is introduced to widen nulls. In the second step, most existing methods, e.g. cyclic algorithm (CA) and sequential iterative algorithm (SIA), synthesize the waveform with constant modulus under leastsquares criterion. As the value at the null even can be neglected in the calculation of least-squares, these methods cannot form deep enough nulls at the interference direction. Aiming at this problem, a block coordinate descent (BCD) method is proposed to optimize the initial waveform synthesized by CA. Besides, the optimized waveform is still constant modulus. Numerical results show the efficiency of the proposed method.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115352081","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}
引用次数: 2
Low-THz Wave Snow Attenuation 低太赫兹波雪衰减
2018 International Conference on Radar (RADAR) Pub Date : 2018-08-01 DOI: 10.1109/RADAR.2018.8557275
Fatemeh Norouziari, E. Marchetti, E. Hoare, M. Gashinova, C. Constantinou, P. Gardner, M. Cherniakov
{"title":"Low-THz Wave Snow Attenuation","authors":"Fatemeh Norouziari, E. Marchetti, E. Hoare, M. Gashinova, C. Constantinou, P. Gardner, M. Cherniakov","doi":"10.1109/RADAR.2018.8557275","DOIUrl":"https://doi.org/10.1109/RADAR.2018.8557275","url":null,"abstract":"This paper assesses the attenuation through various intensities of snowfall experimentally at 300 GHz, and is characterized by measuring the ratio of the received power from the target through the snow precipitation and through the same path with no precipitation. Higher attenuation is measured at higher snowfall rate. Snow events are categorized as “dry snow” and “wet snow”. The comparison between the measured attenuation through wet and dry snowfall shows larger attenuation is expected through wet snowfall compared to the same snowfall rate of dry snow. This study is fundamentally important to investigate the effect of adverse weather condition on Low- THz waves and to assess their feasibility for outdoor applications.","PeriodicalId":187176,"journal":{"name":"2018 International Conference on Radar (RADAR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126647224","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}
引用次数: 10
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