Pub Date : 2017-07-01DOI: 10.23919/ICIF.2017.8009692
Honglei Lin, Shu-Li Sun
This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor non-uniform sampling systems with correlated noises and fading measurements. The state is updated uniformly and the sensors sample measurement data randomly. The process noise and different measurement noises are correlated at the same instant. Moreover, the fading measurement phenomena may occur in different sensor channels. The independent random variables obeying different certain probability distributions over different known intervals are employed to describe the phenomena. Based on the measurement augmentation method, the state space model is reconstructed in which the asynchronous sampling estimation problem is transformed to the synchronous one. Afterwards, local optimal filters are designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrices between any two local filters are derived. At last, the optimal matrix-weighted distributed fusion filter is given in the linear unbiased minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.
{"title":"Distributed fusion estimation for multi-sensor non-uniform sampling systems with correlated noises and fading measurements","authors":"Honglei Lin, Shu-Li Sun","doi":"10.23919/ICIF.2017.8009692","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009692","url":null,"abstract":"This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor non-uniform sampling systems with correlated noises and fading measurements. The state is updated uniformly and the sensors sample measurement data randomly. The process noise and different measurement noises are correlated at the same instant. Moreover, the fading measurement phenomena may occur in different sensor channels. The independent random variables obeying different certain probability distributions over different known intervals are employed to describe the phenomena. Based on the measurement augmentation method, the state space model is reconstructed in which the asynchronous sampling estimation problem is transformed to the synchronous one. Afterwards, local optimal filters are designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrices between any two local filters are derived. At last, the optimal matrix-weighted distributed fusion filter is given in the linear unbiased minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116716102","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}
The ability to recognize physical activity, such as sedentary, driving, riding, daily activities and effective training, is useful for health conscious users to catalogue their daily activities and to develop good exercise routines. Conventional activity recognition algorithms require complex calculations, which are not suitable for wearable devices developed on low-cost, low-power hardware platforms. In this paper, inspired by the text mining related work, we design a novel activity recognition algorithm, which is named “Motionword”. In the wearable device proper, a lightweight recognition algorithm is adopted to compute in real-time predefined atomic events, and count the frequency that these events occur, resulting in a data summary, and then the data summary is transmitted to the platform. On the platform, intelligent method is used to identify and categorize the user's main activity into 5 classes. The test results on a dataset composed of 110 user∗day real world data, contributed by 10 users, show that the recognition accuracy is 95.52%. The Motionword algorithm is capable of achieving accurate activity recognition results without additional hardware cost or power consumption.
{"title":"Motionword: An activity recognition algorithm based on intelligent terminal and cloud","authors":"Zhen-Jie Yao, Zhi-Peng Zhang, Junyan Wang, Li-Qun Xu","doi":"10.23919/ICIF.2017.8009780","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009780","url":null,"abstract":"The ability to recognize physical activity, such as sedentary, driving, riding, daily activities and effective training, is useful for health conscious users to catalogue their daily activities and to develop good exercise routines. Conventional activity recognition algorithms require complex calculations, which are not suitable for wearable devices developed on low-cost, low-power hardware platforms. In this paper, inspired by the text mining related work, we design a novel activity recognition algorithm, which is named “Motionword”. In the wearable device proper, a lightweight recognition algorithm is adopted to compute in real-time predefined atomic events, and count the frequency that these events occur, resulting in a data summary, and then the data summary is transmitted to the platform. On the platform, intelligent method is used to identify and categorize the user's main activity into 5 classes. The test results on a dataset composed of 110 user∗day real world data, contributed by 10 users, show that the recognition accuracy is 95.52%. The Motionword algorithm is capable of achieving accurate activity recognition results without additional hardware cost or power consumption.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"20 19-20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116721236","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009818
Xiaolei Bian, X. Li
This paper deals with the problem of estimating the state of a discrete-time stochastic linear system based on data collected from multiple sensors with limited communication resources. For the cases of transmitting measurements and local state estimates, respectively, we design data-driven communication schemes based on a normalized innovation vector and corresponding fusion rules in the (approximate) minimum mean square error (MMSE) sense. These communication schemes can achieve a trade-off between communication costs and estimation performance. These fusion rules can allow the estimator to improve its estimate based on the fact that no transmission of data indicates a small innovation. A simulation example is provided to confirm the effectiveness of the proposed strategies.
{"title":"Estimation fusion with data-driven communication","authors":"Xiaolei Bian, X. Li","doi":"10.23919/ICIF.2017.8009818","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009818","url":null,"abstract":"This paper deals with the problem of estimating the state of a discrete-time stochastic linear system based on data collected from multiple sensors with limited communication resources. For the cases of transmitting measurements and local state estimates, respectively, we design data-driven communication schemes based on a normalized innovation vector and corresponding fusion rules in the (approximate) minimum mean square error (MMSE) sense. These communication schemes can achieve a trade-off between communication costs and estimation performance. These fusion rules can allow the estimator to improve its estimate based on the fact that no transmission of data indicates a small innovation. A simulation example is provided to confirm the effectiveness of the proposed strategies.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131518306","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009845
Yu Liu, Jun Liu, Cong'an Xu, Lin Qi, Shun Sun, Ziran Ding
Considering the convergence rate is a very important issue as distributed sensors networks usually consist of low-powered wireless devices and speeding up the consensus convergence rate is also important to reduce the number of messages exchanged among neighbors, a new adaptive method for weight assignment of communication links between sensor nodes is proposed based on the dynamic network topology. Based on the adaptive weight assignment method, an improved Kalman consensus filter (KCF) named IKCF is tailored in this letter for distributed state estimation in sensor networks with cluster structure. Furthermore, the experiments demonstrate the adaptive weight assignment method is effective for distributed state estimation when the sensor network is sparsely deployed. In addition, the simulation results also validate the superior performance of the new algorithm and show that IKCF is an excellent algorithm for multi-clusters sensor networks. And there is no additional communication overhead in IKCF because only some local knowledge is used to autonomously calculate the adaptive consensus rate parameter for each node.
{"title":"Consensus algorithm for distributed state estimation in multi-clusters sensor network","authors":"Yu Liu, Jun Liu, Cong'an Xu, Lin Qi, Shun Sun, Ziran Ding","doi":"10.23919/ICIF.2017.8009845","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009845","url":null,"abstract":"Considering the convergence rate is a very important issue as distributed sensors networks usually consist of low-powered wireless devices and speeding up the consensus convergence rate is also important to reduce the number of messages exchanged among neighbors, a new adaptive method for weight assignment of communication links between sensor nodes is proposed based on the dynamic network topology. Based on the adaptive weight assignment method, an improved Kalman consensus filter (KCF) named IKCF is tailored in this letter for distributed state estimation in sensor networks with cluster structure. Furthermore, the experiments demonstrate the adaptive weight assignment method is effective for distributed state estimation when the sensor network is sparsely deployed. In addition, the simulation results also validate the superior performance of the new algorithm and show that IKCF is an excellent algorithm for multi-clusters sensor networks. And there is no additional communication overhead in IKCF because only some local knowledge is used to autonomously calculate the adaptive consensus rate parameter for each node.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132289589","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009873
Chao Wan, Yongxin Gao, X. Li
This paper studies and formulates the problem of distributed filtering with a diffusion strategy for state estimation of a dynamic system by using observations from sensors in a network. The sensor-nodes have estimation ability and work in a collaborative manner. The information transmission across the network abides by the diffusion strategy that each node communicates only with its neighbors. First, we propose a cost function for a trade-off between accuracy and consensus. Then, we derive our algorithm based on this cost and analyze its mean-square performance. Illustrative numerical examples are provided to verify the good performance of our method.
{"title":"Distributed filtering over networks based on diffusion strategy","authors":"Chao Wan, Yongxin Gao, X. Li","doi":"10.23919/ICIF.2017.8009873","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009873","url":null,"abstract":"This paper studies and formulates the problem of distributed filtering with a diffusion strategy for state estimation of a dynamic system by using observations from sensors in a network. The sensor-nodes have estimation ability and work in a collaborative manner. The information transmission across the network abides by the diffusion strategy that each node communicates only with its neighbors. First, we propose a cost function for a trade-off between accuracy and consensus. Then, we derive our algorithm based on this cost and analyze its mean-square performance. Illustrative numerical examples are provided to verify the good performance of our method.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124957397","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009796
Fabian Sigges, M. Baum, U. Hanebeck
We present a particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main idea is to avoid the explicit computation of the likelihood function by means of simulation. For this purpose, a large amount of particles in the state space is simulated from the prior, transformed into measurement space, and then compared to the real measurement by using an appropriate distance function, i.e., the OSPA distance. By selecting the closest simulated measurements and their corresponding particles in state space, the posterior distribution is approximated. The algorithm is evaluated in a multi-object scenario with and without clutter and is compared to a global nearest neighbour Kalman filter.
{"title":"A likelihood-free particle filter for multi-obiect tracking","authors":"Fabian Sigges, M. Baum, U. Hanebeck","doi":"10.23919/ICIF.2017.8009796","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009796","url":null,"abstract":"We present a particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main idea is to avoid the explicit computation of the likelihood function by means of simulation. For this purpose, a large amount of particles in the state space is simulated from the prior, transformed into measurement space, and then compared to the real measurement by using an appropriate distance function, i.e., the OSPA distance. By selecting the closest simulated measurements and their corresponding particles in state space, the posterior distribution is approximated. The algorithm is evaluated in a multi-object scenario with and without clutter and is compared to a global nearest neighbour Kalman filter.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"62 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131312591","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009712
Zhong Wang, Yan Li
This paper considers the robust filtering problem for a class of nonlinear discrete-time systems, and a conjugate unscented transform (CUT) based strong tracking H∞ filter is proposed. Firstly, an extended strong tracking H∞ filter is presented based on the fusion of the extended H∞ filter and strong tracking filter. By online estimating the time-varying noises, the fading factor in the strong tracking filter is incorporated into the H∞ filtering framework to approximately orthogonalize the residual error series. Secondly, a CUT based strong tracking H∞ filter is proposed by combining the extended strong tracking H∞ filtering framework with the recently emerging CUT method. The CUT method is an efficient and accurate approach to calculate the Gaussian quadrature. Numerical simulations show the effectiveness and efficiency of the proposed method.
{"title":"Conjugate unscented transform based strong tracking H∞ filter","authors":"Zhong Wang, Yan Li","doi":"10.23919/ICIF.2017.8009712","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009712","url":null,"abstract":"This paper considers the robust filtering problem for a class of nonlinear discrete-time systems, and a conjugate unscented transform (CUT) based strong tracking H<inf>∞</inf> filter is proposed. Firstly, an extended strong tracking H<inf>∞</inf> filter is presented based on the fusion of the extended H<inf>∞</inf> filter and strong tracking filter. By online estimating the time-varying noises, the fading factor in the strong tracking filter is incorporated into the H<inf>∞</inf> filtering framework to approximately orthogonalize the residual error series. Secondly, a CUT based strong tracking H<inf>∞</inf> filter is proposed by combining the extended strong tracking H<inf>∞</inf> filtering framework with the recently emerging CUT method. The CUT method is an efficient and accurate approach to calculate the Gaussian quadrature. Numerical simulations show the effectiveness and efficiency of the proposed method.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132527246","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009786
Haoyue Zeng, Yong Xia
Automated recognition of spacecraft and space debris using imaging plays an important role in securing space safety and space exploration. Although deep learning is now the most successful solution for image-based object classification, it requires a myriad number of training data, which are not available for most real applications. In this paper, we investigate different single and hybrid data augmentation methods for both training and testing images, and thus propose a data augmentation-based deep learning approach to space target recognition. Experimental results on 400 synthetic space target images rendered by the Systems Tool Kit (STK) demonstrate that our proposed algorithm achieves higher accuracy than several traditional methods.
{"title":"Space target recognition based on deep learning","authors":"Haoyue Zeng, Yong Xia","doi":"10.23919/ICIF.2017.8009786","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009786","url":null,"abstract":"Automated recognition of spacecraft and space debris using imaging plays an important role in securing space safety and space exploration. Although deep learning is now the most successful solution for image-based object classification, it requires a myriad number of training data, which are not available for most real applications. In this paper, we investigate different single and hybrid data augmentation methods for both training and testing images, and thus propose a data augmentation-based deep learning approach to space target recognition. Experimental results on 400 synthetic space target images rendered by the Systems Tool Kit (STK) demonstrate that our proposed algorithm achieves higher accuracy than several traditional methods.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132579401","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009863
Nils Rexin, Dominik Nuss, Stephan Reuter, K. Dietmayer
The dynamic grid map illustrates the environment of robots with moving and static obstacles. Nuss et al. describe in [1] an implementation of this grid map, in which the state of the grid cells is to be modeled as a random finite set (RFS) based on a stochastic measurement system. For a real-time implementation this approach was approximated with Dempster-Shafer (DS). For this Nuss et al. design the areas without information (unknown areas) so, that no probabilistic calculations are executed. Only in the field of view, hypotheses represent the dynamic behavior of objects. This hypotheses are generated with particles. Therefore, in [1] it was proposed to extend this modeling. In this paper a pure Bayes approach is presented, which calculates all areas of the dynamic grid map probabilistic. Now, the resulting modeling generates hypotheses, which represent the dynamic behavior of unobservable objects. Thus, objects moving out of unknown areas can be detected more quickly. This leads to a more intuitive understanding as well as representation of the environment.
{"title":"Modeling occluded areas in dynamic grid maps","authors":"Nils Rexin, Dominik Nuss, Stephan Reuter, K. Dietmayer","doi":"10.23919/ICIF.2017.8009863","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009863","url":null,"abstract":"The dynamic grid map illustrates the environment of robots with moving and static obstacles. Nuss et al. describe in [1] an implementation of this grid map, in which the state of the grid cells is to be modeled as a random finite set (RFS) based on a stochastic measurement system. For a real-time implementation this approach was approximated with Dempster-Shafer (DS). For this Nuss et al. design the areas without information (unknown areas) so, that no probabilistic calculations are executed. Only in the field of view, hypotheses represent the dynamic behavior of objects. This hypotheses are generated with particles. Therefore, in [1] it was proposed to extend this modeling. In this paper a pure Bayes approach is presented, which calculates all areas of the dynamic grid map probabilistic. Now, the resulting modeling generates hypotheses, which represent the dynamic behavior of unobservable objects. Thus, objects moving out of unknown areas can be detected more quickly. This leads to a more intuitive understanding as well as representation of the environment.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"136 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113991612","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 : 2017-07-01DOI: 10.23919/ICIF.2017.8009621
J. Liu, XiaoChao Li
We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing the state space is directly reconstructed, which transforms the nonlinear estimation problem into a linear one through sparse representation. A number of significant grids are obtained based on the sparse vector, indicating the positions of multiple potential targets in the state space. Therefore, the multi-model posterior distribution of the state can be sparsely represented by a number of modes centering around the significant grids at each scan. Consequently, a novel algorithm named sparse mixture particle filter is proposed in this work, which provides a sparse representation of the multi-model posterior distribution by identifying the significant grids. Furthermore, a novel adaptive sparse mixture particle filter algorithm is proposed to tackle the high coherence and high computation burden problems, by constructing a compact dictionary based on the state space with low resolution. The simulation results show that the proposed adaptive sparse mixture particle filter based joint detection and tracking algorithm can successfully detect and track multiple targets, which appear and disappear at different times, as well as track closely spaced targets with similar dynamic model.
{"title":"Adaptive sparse mixture particle filter","authors":"J. Liu, XiaoChao Li","doi":"10.23919/ICIF.2017.8009621","DOIUrl":"https://doi.org/10.23919/ICIF.2017.8009621","url":null,"abstract":"We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing the state space is directly reconstructed, which transforms the nonlinear estimation problem into a linear one through sparse representation. A number of significant grids are obtained based on the sparse vector, indicating the positions of multiple potential targets in the state space. Therefore, the multi-model posterior distribution of the state can be sparsely represented by a number of modes centering around the significant grids at each scan. Consequently, a novel algorithm named sparse mixture particle filter is proposed in this work, which provides a sparse representation of the multi-model posterior distribution by identifying the significant grids. Furthermore, a novel adaptive sparse mixture particle filter algorithm is proposed to tackle the high coherence and high computation burden problems, by constructing a compact dictionary based on the state space with low resolution. The simulation results show that the proposed adaptive sparse mixture particle filter based joint detection and tracking algorithm can successfully detect and track multiple targets, which appear and disappear at different times, as well as track closely spaced targets with similar dynamic model.","PeriodicalId":148407,"journal":{"name":"2017 20th International Conference on Information Fusion (Fusion)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116224104","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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