Fast near-duplicate keyframe detection is the basis of similar video content and video topic analysis. Traditional solutions based on local features are time-consuming and unsuitable for real-time applications. Considering there are little changes on the angles between similar video keyframes, a fast near-duplicate keyframe detection method based on local features is proposed. Firstly, the feature points are detected in various scales of image pyramids by adopting FAST (Features from Accelerated Segment Test) detection method. Secondly, each feature point is described by BRIEF algorithm. Thirdly, the similar keyframes are recognized based on the pattern entropy. Experiments prove that the proposed method is accurate and efficient, and is suitable for real-time applications of similar video keyframes detection.
快速近重复关键帧检测是相似视频内容和视频主题分析的基础。传统的基于局部特征的解决方案耗时长，不适合实时应用。考虑到相似视频关键帧之间的角度变化不大，提出了一种基于局部特征的快速近重复关键帧检测方法。首先，采用FAST (Features from Accelerated Segment Test)检测方法在不同尺度的图像金字塔中检测特征点;其次，利用BRIEF算法对每个特征点进行描述;第三，基于模式熵对相似关键帧进行识别。实验证明，该方法准确、高效，适用于相似视频关键帧检测的实时应用。
{"title":"A fast near-duplicate keyframe detection method based on local features","authors":"Xidao Luan, Yuxiang Xie, Yanming Guo, Jingmeng He, Lili Zhang, Xin Zhang","doi":"10.1109/ICCT.2017.8359890","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359890","url":null,"abstract":"Fast near-duplicate keyframe detection is the basis of similar video content and video topic analysis. Traditional solutions based on local features are time-consuming and unsuitable for real-time applications. Considering there are little changes on the angles between similar video keyframes, a fast near-duplicate keyframe detection method based on local features is proposed. Firstly, the feature points are detected in various scales of image pyramids by adopting FAST (Features from Accelerated Segment Test) detection method. Secondly, each feature point is described by BRIEF algorithm. Thirdly, the similar keyframes are recognized based on the pattern entropy. Experiments prove that the proposed method is accurate and efficient, and is suitable for real-time applications of similar video keyframes detection.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366879","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359971
Zhiquan Wang, Zhiyi Qu
Web text classification is one of the research focuses and core technologies in Web information retrieval and data mining, and it has been widely concerned and developed rapidly in recent years. The convolutional neural network (CNN), as a kind of deep learning model, can extract the features of the text data accurately and reduce the complexity of models at the same time. The support vector machine (SVM) has always had the advantages of being effective and stable in traditional machine learning algorithms. According to the characteristics of CNN and SVM, this paper proposes a new method of Web text classification based on the improved CNN and SVM, using the CNN model with the five-layer network structure to extract text feature and then classify and predict by using SVM. Finally, it will obtain an excellent effect on mixed text data set.
{"title":"Research on Web text classification algorithm based on improved CNN and SVM","authors":"Zhiquan Wang, Zhiyi Qu","doi":"10.1109/ICCT.2017.8359971","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359971","url":null,"abstract":"Web text classification is one of the research focuses and core technologies in Web information retrieval and data mining, and it has been widely concerned and developed rapidly in recent years. The convolutional neural network (CNN), as a kind of deep learning model, can extract the features of the text data accurately and reduce the complexity of models at the same time. The support vector machine (SVM) has always had the advantages of being effective and stable in traditional machine learning algorithms. According to the characteristics of CNN and SVM, this paper proposes a new method of Web text classification based on the improved CNN and SVM, using the CNN model with the five-layer network structure to extract text feature and then classify and predict by using SVM. Finally, it will obtain an excellent effect on mixed text data set.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115119814","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359781
Xianglin Wei, Qin Sun
As a promising technology for newly emerging computing paradigms, like edge computing and Internet of Things (IoT), Software Defined Networking (SDN) has attracted much attention since 2008. SDN enables centralized network management, mobility supporting, security enhancement and quality of service promotion through separating control and data flows. Data centers (DCs) are treated as an ideal deploying scenarios for SDN since they are usually owned or maintained by single entities. Therefore, many newly constructed DC Networks (DCNs) adopt SDN paradigm to enable flexible and reliable network service. However, applying SDN to already-running DCs is not straightforward since it is very hard for us to deploy SDN without disrupting existing network service or introducing complex wiring. In this paper, a wireless control plane for DCN is put forward based on introducing 60GHz wireless links into DCs to enable incremental deployment of SDN in the DCs. A spanning tree algorithm for constructing the control plane is presented which can efficiently connect racks without incurring high cost. Moreover, to reduce the transmission delay in the control plane, a betweenness centrality-based controller placement method is presented. Compared with traditional wire-only methods, our wireless solution can achieve higher performance with low cost. To evaluate the performance of our control plane, a series of simulation experiments have been conducted on NS3. Experimental results have shown that the proposed control plane could efficiently reduce the one-way delay as well as the completion time of the control flows.
{"title":"A wireless control plane for deploying SDN in data center networks","authors":"Xianglin Wei, Qin Sun","doi":"10.1109/ICCT.2017.8359781","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359781","url":null,"abstract":"As a promising technology for newly emerging computing paradigms, like edge computing and Internet of Things (IoT), Software Defined Networking (SDN) has attracted much attention since 2008. SDN enables centralized network management, mobility supporting, security enhancement and quality of service promotion through separating control and data flows. Data centers (DCs) are treated as an ideal deploying scenarios for SDN since they are usually owned or maintained by single entities. Therefore, many newly constructed DC Networks (DCNs) adopt SDN paradigm to enable flexible and reliable network service. However, applying SDN to already-running DCs is not straightforward since it is very hard for us to deploy SDN without disrupting existing network service or introducing complex wiring. In this paper, a wireless control plane for DCN is put forward based on introducing 60GHz wireless links into DCs to enable incremental deployment of SDN in the DCs. A spanning tree algorithm for constructing the control plane is presented which can efficiently connect racks without incurring high cost. Moreover, to reduce the transmission delay in the control plane, a betweenness centrality-based controller placement method is presented. Compared with traditional wire-only methods, our wireless solution can achieve higher performance with low cost. To evaluate the performance of our control plane, a series of simulation experiments have been conducted on NS3. Experimental results have shown that the proposed control plane could efficiently reduce the one-way delay as well as the completion time of the control flows.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116078306","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 : 2017-10-01DOI: 10.1109/icct.2017.8359910
Peng Zhang, Ye Li, Xiaoming Wu, Xiangzhi Liu, Qiuyun Hao, Yan Liang
Equalizers (EQs) have been widely used in audio and acoustic processing to adjust the magnitude of certain frequency bands. This paper proposes a parametric EQ based on short-time Fourier transform (STFT). The audio signal is equalized in the frequency domain by modifying its short-time spectrum with the interpolated magnitude frequency response of the EQ. Design examples show that the proposed method can realize the same function as filter-based equalization, while providing more adjustable frequency bands and wider range of gains. This brings in more flexible control and usage of the EQ for practical applications.
{"title":"Parametric audio equalizer based on short-time fourier transform","authors":"Peng Zhang, Ye Li, Xiaoming Wu, Xiangzhi Liu, Qiuyun Hao, Yan Liang","doi":"10.1109/icct.2017.8359910","DOIUrl":"https://doi.org/10.1109/icct.2017.8359910","url":null,"abstract":"Equalizers (EQs) have been widely used in audio and acoustic processing to adjust the magnitude of certain frequency bands. This paper proposes a parametric EQ based on short-time Fourier transform (STFT). The audio signal is equalized in the frequency domain by modifying its short-time spectrum with the interpolated magnitude frequency response of the EQ. Design examples show that the proposed method can realize the same function as filter-based equalization, while providing more adjustable frequency bands and wider range of gains. This brings in more flexible control and usage of the EQ for practical applications.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116050605","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359775
Peijun Hong, Hao Liu, Z. Yan, Zhiqin Qian, Kai Wu, Z. Bi
This Nowadays, people are paying more and more attention to the quality of home environment with the rapid popularity of smart home system and Internet of things technology. In this paper, a new method based on the technology of WSN is adopted to monitor some indicators of the environment indoors, so that the damaging caused by these imperceptible and even dangerous indications, such as PM 2.5, temperature, humidity and the concentration of carbon monoxide, can be reduced or eliminated. The proposed system of WSN and a PM 2.5 detector is used in acquiring real-time data for surveillance and control of smart homes. The final results show that this cost-effective home monitoring system based on Wireless Sensor Network can reflect the indoor air quality to a great extent, thus it is of great progress in making people changing their living mode and sustaining a better life.
{"title":"Research of home environment surveillance system based on wireless sensor network","authors":"Peijun Hong, Hao Liu, Z. Yan, Zhiqin Qian, Kai Wu, Z. Bi","doi":"10.1109/ICCT.2017.8359775","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359775","url":null,"abstract":"This Nowadays, people are paying more and more attention to the quality of home environment with the rapid popularity of smart home system and Internet of things technology. In this paper, a new method based on the technology of WSN is adopted to monitor some indicators of the environment indoors, so that the damaging caused by these imperceptible and even dangerous indications, such as PM 2.5, temperature, humidity and the concentration of carbon monoxide, can be reduced or eliminated. The proposed system of WSN and a PM 2.5 detector is used in acquiring real-time data for surveillance and control of smart homes. The final results show that this cost-effective home monitoring system based on Wireless Sensor Network can reflect the indoor air quality to a great extent, thus it is of great progress in making people changing their living mode and sustaining a better life.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116682105","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359740
Wei Chen, Huawei Chen
As an important part of future wireless communication technology, D2D communication can reduce the pressure of increasing demand for limited radio resources and improve the performance of wireless communication. To further promote the transmission efficiency of D2D communication, researchers combine social networks with it to establish a two-tier model that typically includes social domain and physical domain. This paper presents a personal trajectory based social aware content share solution to D2D communication. Personal trajectory data contains both physical and social information which contacts two domains of the model. The solution of this paper also considers the classification of share contents. Simulation results show that proposed solution improve the performance of D2D transmission.
{"title":"Personal trajectory based social-aware D2D communication networks","authors":"Wei Chen, Huawei Chen","doi":"10.1109/ICCT.2017.8359740","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359740","url":null,"abstract":"As an important part of future wireless communication technology, D2D communication can reduce the pressure of increasing demand for limited radio resources and improve the performance of wireless communication. To further promote the transmission efficiency of D2D communication, researchers combine social networks with it to establish a two-tier model that typically includes social domain and physical domain. This paper presents a personal trajectory based social aware content share solution to D2D communication. Personal trajectory data contains both physical and social information which contacts two domains of the model. The solution of this paper also considers the classification of share contents. Simulation results show that proposed solution improve the performance of D2D transmission.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116866415","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359478
Mohamed Bouchou, Hua Wang, Mohammed El Hadi Lakhdari
In this paper, a modulation recognition algorithm based on Stacked sparse Auto-Encoder (SSAE) is proposed for the classification of common digitally modulated signals. To this end, a set of eight features including, two instantaneous features and six higher order cumulants features are extracted from the intercepted signal; these features are then fed to the SSAE for classification. Unlike the majority of classifiers used in AMR algorithms, which relies only on the supervised learning scenario, the stacked sparse autoencoder benefits from both, unsupervised and supervised learning approaches. In fact, the main advantage of the SSAE is that it can automatically learn new features to separate the input data during the unsupervised pre-training phase. These new features are used as initialization parameters in the supervised training phase to enhance the convergence of the SSAE to optimal results, as well as improve the noise resistance of the eight features extracted before. Results show that the overall success rate reach 100 % at 5dB SNR. The performance of the proposed algorithm is compared to an SVM-based method, and it is found that the probability of correct classification in our method is considerably improved.
{"title":"Automatic digital modulation recognition based on stacked sparse autoencoder","authors":"Mohamed Bouchou, Hua Wang, Mohammed El Hadi Lakhdari","doi":"10.1109/ICCT.2017.8359478","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359478","url":null,"abstract":"In this paper, a modulation recognition algorithm based on Stacked sparse Auto-Encoder (SSAE) is proposed for the classification of common digitally modulated signals. To this end, a set of eight features including, two instantaneous features and six higher order cumulants features are extracted from the intercepted signal; these features are then fed to the SSAE for classification. Unlike the majority of classifiers used in AMR algorithms, which relies only on the supervised learning scenario, the stacked sparse autoencoder benefits from both, unsupervised and supervised learning approaches. In fact, the main advantage of the SSAE is that it can automatically learn new features to separate the input data during the unsupervised pre-training phase. These new features are used as initialization parameters in the supervised training phase to enhance the convergence of the SSAE to optimal results, as well as improve the noise resistance of the eight features extracted before. Results show that the overall success rate reach 100 % at 5dB SNR. The performance of the proposed algorithm is compared to an SVM-based method, and it is found that the probability of correct classification in our method is considerably improved.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115009410","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359812
Jin Ren, Yunan Wang, Wenle Bai, Changliu Niu, Shan Meng
For the positioning range of the indoor localization algorithm is limited, location accuracy requirements are more precise. In the study of the measurement distance based on the signal receiving strength (RSSI), it is not reliable to calculate the receiving signal strength to affect the final positioning accuracy. By sampling and analyzing the signal strength of the node, filter out too big error and further reduce the measurement error to improve the positioning accuracy. The feasibility and effectiveness of the improved algorithm are verified by simulation results. The location accuracy of positioning algorithm is improved.
{"title":"An improved indoor positioning algorithm based on RSSI filtering","authors":"Jin Ren, Yunan Wang, Wenle Bai, Changliu Niu, Shan Meng","doi":"10.1109/ICCT.2017.8359812","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359812","url":null,"abstract":"For the positioning range of the indoor localization algorithm is limited, location accuracy requirements are more precise. In the study of the measurement distance based on the signal receiving strength (RSSI), it is not reliable to calculate the receiving signal strength to affect the final positioning accuracy. By sampling and analyzing the signal strength of the node, filter out too big error and further reduce the measurement error to improve the positioning accuracy. The feasibility and effectiveness of the improved algorithm are verified by simulation results. The location accuracy of positioning algorithm is improved.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115432141","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 : 2017-10-01DOI: 10.1109/ICCT.2017.8359848
Hua Qu, Jing-Feng Xue, Ji-hong Zhao
Currently, the mobile data traffic is tremendously increasing and the solution is needed to handle this increasing demands. There are several solutions to solve this issue. Among them, the Content-Centric Networking (CCN) is one of the most promising solutions, where CCN reduces the network traffic by caching the contents in-network nodes temporarily. So, the in-network nodes can provide these cached contents to the user, instead of retrieving from the original server. Hence the caching scheme is important for the efficient use of cache and content delivery. Even tough, there are several caching decision algorithms already existed, it is still a great challenge to better utilize in-network caching of CCN. In this paper, we propose an effective popularity-based caching scheme in response to the special characteristics of CCN with the idea to reduce content redundancy within network and make popular contents near the network edge. We evaluate our approach using k-ary tree network topology with many factors that may have impacts on the performance. Results show that our approach can significantly reduce content redundancy, and outperforms existing schemes in access hops and server hit proportion.
{"title":"A popularity-based cooperative caching in content-centric networking","authors":"Hua Qu, Jing-Feng Xue, Ji-hong Zhao","doi":"10.1109/ICCT.2017.8359848","DOIUrl":"https://doi.org/10.1109/ICCT.2017.8359848","url":null,"abstract":"Currently, the mobile data traffic is tremendously increasing and the solution is needed to handle this increasing demands. There are several solutions to solve this issue. Among them, the Content-Centric Networking (CCN) is one of the most promising solutions, where CCN reduces the network traffic by caching the contents in-network nodes temporarily. So, the in-network nodes can provide these cached contents to the user, instead of retrieving from the original server. Hence the caching scheme is important for the efficient use of cache and content delivery. Even tough, there are several caching decision algorithms already existed, it is still a great challenge to better utilize in-network caching of CCN. In this paper, we propose an effective popularity-based caching scheme in response to the special characteristics of CCN with the idea to reduce content redundancy within network and make popular contents near the network edge. We evaluate our approach using k-ary tree network topology with many factors that may have impacts on the performance. Results show that our approach can significantly reduce content redundancy, and outperforms existing schemes in access hops and server hit proportion.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125309085","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 : 2017-10-01DOI: 10.1109/icct.2017.8359815
Yanqing Ren, Bin Ba, Zhiyu Lu, Daming Wang
The traditional multiple-station direct position determination method suffers location accuracy loss and source resolution degradation for the lack of position information fusion of raw data. And an information fusion direct position determination method based on Wishart random matrix asymptotic distribution theory is proposed to overcome the above-mentioned shortcomings. Firstly, the information fusion direct position determination model is established via fusing raw data of each station. Then the new cost function containing eigenspace is constructed with theory of Wishart random matrix asymptotic distribution. Finally, the target location estimation is obtained by two-dimensional geographic grid search. Furthermore, the Cramer-Rao bound of the new model is derived. Compared with the original method, the proposed method performs much better in location accuracy and source resolution by simulations. And it frequently outperforms the information fusion direct position determination method with the cost function only containing noise subspace, under scenarios of low SNR and snapshot deficiency. Its performance has been greatly improved at the expense of lower complexity.
{"title":"An information fusion direct position determination method based on Wishart random matrix asymptotic distribution theory","authors":"Yanqing Ren, Bin Ba, Zhiyu Lu, Daming Wang","doi":"10.1109/icct.2017.8359815","DOIUrl":"https://doi.org/10.1109/icct.2017.8359815","url":null,"abstract":"The traditional multiple-station direct position determination method suffers location accuracy loss and source resolution degradation for the lack of position information fusion of raw data. And an information fusion direct position determination method based on Wishart random matrix asymptotic distribution theory is proposed to overcome the above-mentioned shortcomings. Firstly, the information fusion direct position determination model is established via fusing raw data of each station. Then the new cost function containing eigenspace is constructed with theory of Wishart random matrix asymptotic distribution. Finally, the target location estimation is obtained by two-dimensional geographic grid search. Furthermore, the Cramer-Rao bound of the new model is derived. Compared with the original method, the proposed method performs much better in location accuracy and source resolution by simulations. And it frequently outperforms the information fusion direct position determination method with the cost function only containing noise subspace, under scenarios of low SNR and snapshot deficiency. Its performance has been greatly improved at the expense of lower complexity.","PeriodicalId":199874,"journal":{"name":"2017 IEEE 17th International Conference on Communication Technology (ICCT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806567","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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