Pub Date : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947311
Haoyue Xiao, Yubai Li
This paper discusses the singular source’s Direction-Of-Arrival (DOA) and Direction-Of-Distance (DOD) estimation method based on a tensor decomposition algorithm in the near-field situation. With the assistance of the uniqueness of tensor decomposition, the proposed method achieves a high-accuracy performance in both DOA and DOD estimations. For Uniform Linear Arrays (ULA), the steering vector of near-field sources is determined by both angle and distance parameters. Two modified models are built for DOA and DOD estimations respectively and each of them contains only one parameter. These two models are furtherly turned to tensor models by cutting to slices. Rank-l tensor approximation Alternating Least Squares (ALS) algorithms are then used to estimate DOA and DOD for its general global convergence property. The results are used for localization and numerical simulations have verified the effectiveness of the proposed method.
{"title":"CANDECOMP&PARAFAC-based Near-Field Source Localization by Passive Sensor Arrays","authors":"Haoyue Xiao, Yubai Li","doi":"10.1109/ICCT46805.2019.8947311","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947311","url":null,"abstract":"This paper discusses the singular source’s Direction-Of-Arrival (DOA) and Direction-Of-Distance (DOD) estimation method based on a tensor decomposition algorithm in the near-field situation. With the assistance of the uniqueness of tensor decomposition, the proposed method achieves a high-accuracy performance in both DOA and DOD estimations. For Uniform Linear Arrays (ULA), the steering vector of near-field sources is determined by both angle and distance parameters. Two modified models are built for DOA and DOD estimations respectively and each of them contains only one parameter. These two models are furtherly turned to tensor models by cutting to slices. Rank-l tensor approximation Alternating Least Squares (ALS) algorithms are then used to estimate DOA and DOD for its general global convergence property. The results are used for localization and numerical simulations have verified the effectiveness of the proposed method.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134603733","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947242
M. Li, Tian Lan, Chuan Peng, Yuxin Qian, Qiao Liu
Speech separation is the front-end of speech processing applications. Its purpose is to separate the speech in a multi-speaker environment. The neural network methods show good performance in speech separation, but most of the existing methods try to separate all the speaker speech. From the theory of auditory selection, we know that people can only focus on one speaker each time in multi-speaker conditions. Inspired by this, we use the attention mechanism to introduce the speaker information and propose a multi-layer structure so that the proposed model can extract a more complete separation speech. The experiments tested on the TSP and THCHS-30 datasets show that our model is superior to the baseline models in Short-Time Objective Intelligibility(STOI) and Perceptual Evaluation of Speech Quality(PESQ).
{"title":"Multi-layer Attention Mechanism Based Speech Separation Model","authors":"M. Li, Tian Lan, Chuan Peng, Yuxin Qian, Qiao Liu","doi":"10.1109/ICCT46805.2019.8947242","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947242","url":null,"abstract":"Speech separation is the front-end of speech processing applications. Its purpose is to separate the speech in a multi-speaker environment. The neural network methods show good performance in speech separation, but most of the existing methods try to separate all the speaker speech. From the theory of auditory selection, we know that people can only focus on one speaker each time in multi-speaker conditions. Inspired by this, we use the attention mechanism to introduce the speaker information and propose a multi-layer structure so that the proposed model can extract a more complete separation speech. The experiments tested on the TSP and THCHS-30 datasets show that our model is superior to the baseline models in Short-Time Objective Intelligibility(STOI) and Perceptual Evaluation of Speech Quality(PESQ).","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117096708","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947070
Xiaoyan Jin, Jun Zhang, Xinghua Sun, Ping Zhang, Shu Cai
Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost.
{"title":"Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach","authors":"Xiaoyan Jin, Jun Zhang, Xinghua Sun, Ping Zhang, Shu Cai","doi":"10.1109/ICCT46805.2019.8947070","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947070","url":null,"abstract":"Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775485","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947053
Wenli Zhu, Min Zhang
An efficient neural network-based approach for broadband direction of arrival (DOA) estimation is presented in this paper. The received data of the uniform circle array (UCA) is transformed into direction image, which is used as the input of the neural network. The phase component of the spatial covariance matrix of the received signal is extracted to form the direction image. We establish a convolutional neural network (CNN) with five hidden layers to learn the inverse mapping from the space of possible antenna element excitations to the space of possible angular directions to the signal source. DOA estimation is formulated as a regression problem, where the each DOA label to the direction image is consisted of the sine and cosine values of the angle of arrival. Simulation results show that the trained CNN network can be successfully used for broadband DOA estimation. The performance of the developed CNN model is comparable to the performance of the conventional algorithms at the lower signal-to-noise ratio. Importantly, the proposed CNN estimator further reduces the computation time which makes it successful to apply to real-time applications.
{"title":"A Deep Learning Architecture for Broadband DOA Estimation","authors":"Wenli Zhu, Min Zhang","doi":"10.1109/ICCT46805.2019.8947053","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947053","url":null,"abstract":"An efficient neural network-based approach for broadband direction of arrival (DOA) estimation is presented in this paper. The received data of the uniform circle array (UCA) is transformed into direction image, which is used as the input of the neural network. The phase component of the spatial covariance matrix of the received signal is extracted to form the direction image. We establish a convolutional neural network (CNN) with five hidden layers to learn the inverse mapping from the space of possible antenna element excitations to the space of possible angular directions to the signal source. DOA estimation is formulated as a regression problem, where the each DOA label to the direction image is consisted of the sine and cosine values of the angle of arrival. Simulation results show that the trained CNN network can be successfully used for broadband DOA estimation. The performance of the developed CNN model is comparable to the performance of the conventional algorithms at the lower signal-to-noise ratio. Importantly, the proposed CNN estimator further reduces the computation time which makes it successful to apply to real-time applications.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124484179","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947205
Can Liu, Chucheng Chen, Jianbin Lu, X. Dai
In order to improve the reliability of channel communication in MIMO-OFDM systems, this paper proposes a channel estimation algorithm which can be adopted in situations with time-varying multi-path effect. In our study, the channel estimation is obtained based on the least squares (LS) method of the optimal pilots, and then further improve the performance of the rough estimate by autoregressive (AR) model prediction and decision-directed (DD). As channel time-varying rapidly, this research use the correction factor to improve the algorithm so that the proposed algorithm can be applied to this scene. The simulation results demonstrate that our proposed channel estimation algorithm outperforms the existing algorithms without increasing the number and the power of pilots.
{"title":"A New Channel Estimation Algorithm for Time-Varying Multi-Path Channel in MIMO-OFDM Systems","authors":"Can Liu, Chucheng Chen, Jianbin Lu, X. Dai","doi":"10.1109/ICCT46805.2019.8947205","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947205","url":null,"abstract":"In order to improve the reliability of channel communication in MIMO-OFDM systems, this paper proposes a channel estimation algorithm which can be adopted in situations with time-varying multi-path effect. In our study, the channel estimation is obtained based on the least squares (LS) method of the optimal pilots, and then further improve the performance of the rough estimate by autoregressive (AR) model prediction and decision-directed (DD). As channel time-varying rapidly, this research use the correction factor to improve the algorithm so that the proposed algorithm can be applied to this scene. The simulation results demonstrate that our proposed channel estimation algorithm outperforms the existing algorithms without increasing the number and the power of pilots.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124774744","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947246
Ziwen Tang, Jie Sheng, Cheng Wu, Yiming Wang
As the speed of high-speed railways continues to accelerate and the carrying capacity is also increasing, the service quality of rail transit wireless communication users has declined due to the impact of railway speed and user density. We propose a user classification method based interference alignment algorithm for high-speed railway wireless communication in this paper. The users on the train are divided into central users and edge users by mobility prediction, and then interference management is performed on different types of users. The simulation results show that the user classification based on mobility prediction is beneficial to accurately grasp the trend of user types, and conducive to interference management, and has significantly improved the performance of high-speed railway communication network
{"title":"Interference Alignment Algorithm for High-Speed Railway Wireless Communication Based on Mobile User Classification","authors":"Ziwen Tang, Jie Sheng, Cheng Wu, Yiming Wang","doi":"10.1109/ICCT46805.2019.8947246","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947246","url":null,"abstract":"As the speed of high-speed railways continues to accelerate and the carrying capacity is also increasing, the service quality of rail transit wireless communication users has declined due to the impact of railway speed and user density. We propose a user classification method based interference alignment algorithm for high-speed railway wireless communication in this paper. The users on the train are divided into central users and edge users by mobility prediction, and then interference management is performed on different types of users. The simulation results show that the user classification based on mobility prediction is beneficial to accurately grasp the trend of user types, and conducive to interference management, and has significantly improved the performance of high-speed railway communication network","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124822597","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947128
Hongqiang Li, Yubing Zhang, Xu Zhao, Xiaoke Tang
The rapid development of Internet of Things (IoT) puts forward much requirements for wireless communication technology. Low Power Wide Area Networks (LPWAN) are designed for low bandwidth, low power, long range and large number of connected IoT applications. As one of the LPWAN, Low Power Long Range Transceiver (LoRa) described as a Frequency Shift Chirp Modulation (FSCM), is widely concerned and studied. In this paper, the signal reception of LoRa modulation in the wireless tree topology is analyzed, and the multi-user interference analysis proves that the multi-user interference has a great impact on the system performance. At the same time, we proposed Delay Coordinated Multiple Points Transmission (DCoMP). Multiple nodes close to each other send the same data to the target node. Due to the inaccuracy of synchronization between nodes, there will be a certain timing offset when sending signals to the same target node. After combining signals of multiple nodes according to different timing offset, the receiver performance of signals can be improved. The coordinated nodes can also actively adjust the signal sending timing according to the path time delay and processing delay, so as to improve the receiving performance of the signal merging algorithm of the receiver node. LoRa modulation improves the signal reception performance by adopting DCoMP transmission, thus improving the overall throughput of the system.
{"title":"Delay CoMP of LoRa Modulation in Wireless Tree Topology Network","authors":"Hongqiang Li, Yubing Zhang, Xu Zhao, Xiaoke Tang","doi":"10.1109/ICCT46805.2019.8947128","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947128","url":null,"abstract":"The rapid development of Internet of Things (IoT) puts forward much requirements for wireless communication technology. Low Power Wide Area Networks (LPWAN) are designed for low bandwidth, low power, long range and large number of connected IoT applications. As one of the LPWAN, Low Power Long Range Transceiver (LoRa) described as a Frequency Shift Chirp Modulation (FSCM), is widely concerned and studied. In this paper, the signal reception of LoRa modulation in the wireless tree topology is analyzed, and the multi-user interference analysis proves that the multi-user interference has a great impact on the system performance. At the same time, we proposed Delay Coordinated Multiple Points Transmission (DCoMP). Multiple nodes close to each other send the same data to the target node. Due to the inaccuracy of synchronization between nodes, there will be a certain timing offset when sending signals to the same target node. After combining signals of multiple nodes according to different timing offset, the receiver performance of signals can be improved. The coordinated nodes can also actively adjust the signal sending timing according to the path time delay and processing delay, so as to improve the receiving performance of the signal merging algorithm of the receiver node. LoRa modulation improves the signal reception performance by adopting DCoMP transmission, thus improving the overall throughput of the system.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"419 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123552931","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947006
Shiwen Fan, Shu Fang, Yan Zhao, Yue Xiao, Xiaotian Zhou
In this paper, a novel minimum mean square error (MMSE) detector is proposed in generalized space-time-frequency index modulation (GSTFIM) system with carrier frequency offset (CFO). Specifically, the influence of CFO is considered as a component-wide product of the CFO matrix and the frequency domain channel matrix. Furthermore, the inter-carrier interference (ICI) cancellation is realized by the proposed MMSE detector. Simulation results show that the proposed MMSE detector can obtain better performance compared to conventional parallel interference cancellation aided maximum likelihood (PIC-ML) detector.
{"title":"MMSE-based Detector for Generalized Space-Time-Frequency Index Modulation Systems with Carrier Frequency Offsets","authors":"Shiwen Fan, Shu Fang, Yan Zhao, Yue Xiao, Xiaotian Zhou","doi":"10.1109/ICCT46805.2019.8947006","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947006","url":null,"abstract":"In this paper, a novel minimum mean square error (MMSE) detector is proposed in generalized space-time-frequency index modulation (GSTFIM) system with carrier frequency offset (CFO). Specifically, the influence of CFO is considered as a component-wide product of the CFO matrix and the frequency domain channel matrix. Furthermore, the inter-carrier interference (ICI) cancellation is realized by the proposed MMSE detector. Simulation results show that the proposed MMSE detector can obtain better performance compared to conventional parallel interference cancellation aided maximum likelihood (PIC-ML) detector.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121891456","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947111
B. Varun, V. AbhishekM., A. Gangadhar, U. Purushotham
Progress of mobile communication and VLSI technology has aided in development of smart devices. These devices process the information of various formats and sizes in a limited amount of time. This information will be either stored in the devices or in cloud, hence there is a need for some kind of methodology to process and secure the data. Implementation of new algorithms to secure the information is always of immense interest. These algorithms will improve the performance of smart devices and helps for better human-machine interaction. Generally, symmetric and asymmetric approaches are used to secure the data from unauthorized users or attacks. Considering the amount of delay and complexity involved in processing the data, various forms of algorithms are used. In this paper, we propose a novel algorithm to secure the data from vulnerable attacks. These algorithms can be implemented on various platforms. The experimental results demonstrate an improvement of 10% for contacts and 15% for the encryption of images as compared to other conventional approaches.
{"title":"Implementation of Encryption and Decryption Algorithms for Security of Mobile Devices","authors":"B. Varun, V. AbhishekM., A. Gangadhar, U. Purushotham","doi":"10.1109/ICCT46805.2019.8947111","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947111","url":null,"abstract":"Progress of mobile communication and VLSI technology has aided in development of smart devices. These devices process the information of various formats and sizes in a limited amount of time. This information will be either stored in the devices or in cloud, hence there is a need for some kind of methodology to process and secure the data. Implementation of new algorithms to secure the information is always of immense interest. These algorithms will improve the performance of smart devices and helps for better human-machine interaction. Generally, symmetric and asymmetric approaches are used to secure the data from unauthorized users or attacks. Considering the amount of delay and complexity involved in processing the data, various forms of algorithms are used. In this paper, we propose a novel algorithm to secure the data from vulnerable attacks. These algorithms can be implemented on various platforms. The experimental results demonstrate an improvement of 10% for contacts and 15% for the encryption of images as compared to other conventional approaches.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122679628","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 : 2019-10-01DOI: 10.1109/ICCT46805.2019.8947076
Linxi Wang, Xiaoxi Hu, Xun Han, Yin Kuang, Xinquan Yang
Multi-target tracking technologies have important research value in many fields. Algorithms based on random finite set theory can achieve a better tracking effect without data association, which have attracted wide attentions. In this paper, after establishing a real multi-target motion scenario, CBMeMBer filtering algorithm is simulated and implemented on the linear Gauss condition, and is compared with PHD, CPHD and MeMBer filtering algorithm. The simulation results show that CBMeMBer filtering algorithm is correct and effective. Under the same simulation conditions, its tracking performance is obviously improved, and it has good application prospects in multi-target tracking field.
{"title":"Simulation of CBMeMber Multi-target Tracking Algorithm Based on Gauss Mixture","authors":"Linxi Wang, Xiaoxi Hu, Xun Han, Yin Kuang, Xinquan Yang","doi":"10.1109/ICCT46805.2019.8947076","DOIUrl":"https://doi.org/10.1109/ICCT46805.2019.8947076","url":null,"abstract":"Multi-target tracking technologies have important research value in many fields. Algorithms based on random finite set theory can achieve a better tracking effect without data association, which have attracted wide attentions. In this paper, after establishing a real multi-target motion scenario, CBMeMBer filtering algorithm is simulated and implemented on the linear Gauss condition, and is compared with PHD, CPHD and MeMBer filtering algorithm. The simulation results show that CBMeMBer filtering algorithm is correct and effective. Under the same simulation conditions, its tracking performance is obviously improved, and it has good application prospects in multi-target tracking field.","PeriodicalId":306112,"journal":{"name":"2019 IEEE 19th International Conference on Communication Technology (ICCT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124283423","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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