Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper...Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.展开更多
For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation....For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.展开更多
With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation sa...With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.展开更多
In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the e...In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.展开更多
Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on ...Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.展开更多
The Unmanned Aerial Helicopter(UAH)has attracted increasing attention in the military and civil areas with the unique flight performance.The significant impact on the attitude measurement performance of UAHs by the st...The Unmanned Aerial Helicopter(UAH)has attracted increasing attention in the military and civil areas with the unique flight performance.The significant impact on the attitude measurement performance of UAHs by the strong airflow disturbance is an essential factor threatening flight safety.To improve the attitude measurement performance of UAHs under atmospheric disturbance,an attitude fusion method over the factor graph is applied and provides the plug-and-play capability.Based on the relationship between position,velocity and attitude,a new attitude correction algorithm for the Modified Attitude Factor Graph Fusion(MAFGF)navigation method is designed and constructed through the fused position and velocity information.Finally,results of simulation and experiment are given to show the effectiveness of the proposed method.展开更多
Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the di...Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.展开更多
The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexit...The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.展开更多
A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion inf...A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.展开更多
A strong product graph is denoted by G_(1)■G_(2),where G_(1) and G_(2) are called its factor graphs.This paper gives the range of the minimum strong radius of the strong product graph.And using the relationship betwe...A strong product graph is denoted by G_(1)■G_(2),where G_(1) and G_(2) are called its factor graphs.This paper gives the range of the minimum strong radius of the strong product graph.And using the relationship between the cartesian product graph G_(1)■G_(2) and the strong product graph G_(1)■G_(2),another different upper bound of the minimum strong radius of the strong product graph is given.展开更多
Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronizati...Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.展开更多
Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are eas...Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.展开更多
Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,...Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.展开更多
Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.Firs...Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.展开更多
The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of vehicles.However,the GNSS signal will be faded or blocked in complex road environments,which will lead to a d...The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of vehicles.However,the GNSS signal will be faded or blocked in complex road environments,which will lead to a decrease in positioning accuracy.Owing to the higher-precision synchronization provided in the sixth generation(6G)network,the errors of ranging-based positioning technologies can be effectively reduced.At the same time,the use of terahertz in 6G allows excellent resolution of range and angle,which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied environment.This paper introduces a multi-vehicle cooperative localization method.In the proposed method,the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside units.The multi-vehicle cooperative localization problem is represented using a factor graph.An iterative algorithm based on belief propagation is applied to perform the inference over the factor graph.The results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy.展开更多
We consider even factors with a bounded number of components in the n-times iterated line graphs L^n(G). We present a characterization of a simple graph G such that L^n(G) has an even factor with at most k components,...We consider even factors with a bounded number of components in the n-times iterated line graphs L^n(G). We present a characterization of a simple graph G such that L^n(G) has an even factor with at most k components, based on the existence of a certain type of subgraphs in G. Moreover, we use this result to give some upper bounds for the minimum number of components of even factors in L^n(G) and also show that the minimum number of components of even factors in L^n(G) is stable under the closure operation on a claw-free graph G, which extends some known results. Our results show that it seems to be NP-hard to determine the minimum number of components of even factors of iterated line graphs. We also propose some problems for further research.展开更多
FBMC(Filter Bank Multicarrier)modulation is considered one of the waveform candidates in fifth generation wireless communication technology because of its several improved features compared to conventional orthogonal ...FBMC(Filter Bank Multicarrier)modulation is considered one of the waveform candidates in fifth generation wireless communication technology because of its several improved features compared to conventional orthogonal frequency division multiplexing schemes.A soft-input-soft-output factor-graph-based maximum-a-posterior detector is applied to FBMC systems.The detector achieves better performance than simple linear equalizers such as minimum mean square error and zero forcing in coded systems while exhibiting only a linear growth in complexity with the number of simultaneous interfering symbols.Furthermore,the proposed detector can be easily extended to cases where FBMC modulation is combined with multiple-input-multiple-output processing.The complexity of the detector is analyzed and the simulation results demonstrated its superior performance.展开更多
This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a poste...This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter （EKF） in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System （GNSS/INS） integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.展开更多
In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBol...In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model （TPFG）. Second, we extract network features for ranking nodes. Finally, we propose RWR- Model, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms： traditional Ranking SVM （RSVM） and the individual doctor recommendation model （IDR-Model）. The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.展开更多
Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers ...Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs （Single Nucleotide Polymorphisms）, which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.展开更多
基金Project supported by the National Hi-Tech Research and Develop-ment Program (863) of China (No. 2003AA123310) and the National Natural Science Foundation of China (No. 60332030)
文摘Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.
基金supported by the National Natural Science Foundation of China(62176214).
文摘For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.
基金supported in part by the Guangxi Power Grid Company’s 2023 Science and Technol-ogy Innovation Project(No.GXKJXM20230169)。
文摘With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.
文摘In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.
基金supported by the National Natural Science Foundation of China(No.61873207)the National Science and Technology Major Project,China(No.J2019-I-00210020)+2 种基金the Natural Science Basic Research Program of Shaanxi,China(No.2019JQ-344)the Science and Technology Program of Xi’an City,China(No.2019218314GXRC019CG020-GXYD19.3)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China。
文摘Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.
基金co-supported by the National Natural Science Foundation of China (Nos. 61533008, 61603181)the Fundamental Research Funds for the Central Universities of China (No. NS2018021)the Priority Academic Program Development of Jiangsu Higher Education Institutions, China
文摘The Unmanned Aerial Helicopter(UAH)has attracted increasing attention in the military and civil areas with the unique flight performance.The significant impact on the attitude measurement performance of UAHs by the strong airflow disturbance is an essential factor threatening flight safety.To improve the attitude measurement performance of UAHs under atmospheric disturbance,an attitude fusion method over the factor graph is applied and provides the plug-and-play capability.Based on the relationship between position,velocity and attitude,a new attitude correction algorithm for the Modified Attitude Factor Graph Fusion(MAFGF)navigation method is designed and constructed through the fused position and velocity information.Finally,results of simulation and experiment are given to show the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(No.51877013),(ZJ),(http://www.nsfc.gov.cn/)the Natural Science Foundation of Jiangsu Province(No.BK20181463),(ZJ),(http://kxjst.jiangsu.gov.cn/)sponsored by Qing Lan Project of Jiangsu Province(no specific grant number),(ZJ),(http://jyt.jiangsu.gov.cn/).
文摘Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.
基金Supported by the National Natural Science Foundation of China(61201181)Specialized Research Fund for the Doctoral Program of Higher Education(20121101120020)the Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.
文摘A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.
基金Supported by National Natural Science Foundation of China(Grant No.11551002)Natural Science Foundation of Qinghai Province(Grant No.2019-ZJ-7093)。
文摘A strong product graph is denoted by G_(1)■G_(2),where G_(1) and G_(2) are called its factor graphs.This paper gives the range of the minimum strong radius of the strong product graph.And using the relationship between the cartesian product graph G_(1)■G_(2) and the strong product graph G_(1)■G_(2),another different upper bound of the minimum strong radius of the strong product graph is given.
基金National Natural Science Foundation of China(No.61272432)Qingdao Science and Technology Development Plan(No.12-1-4-6-(10)-jch)
文摘Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.
基金National Natural Science Foundation of China(Grant No.62203111)the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(Grant No.21P01)the Foundation of Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology,Ministry of Education,China(Grant No.SEU-MIAN-202101)to provide fund for conducting experiments。
文摘Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.
基金This work is supported in part by the National Natural Science Foundation of China under contracts 61672242 and 61702199in part by China Spark Program under Grant 2015GA780002+1 种基金in part by The National Key Research and Development Program of China under Grant 2017YFD0701601in part by Natural Science Foundation of Guangdong Province under Grant 2015A030313413.
文摘Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.
基金supported by the National Natural Science Foundation of China(6120118161471037+1 种基金61571041)the Foundation for the Author of National Excellent Doctoral Dissertation of China(201445)
文摘Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.
基金supported by the National Natural Science Foundation of China(No.61701020)the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(No.BK19BF009)。
文摘The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of vehicles.However,the GNSS signal will be faded or blocked in complex road environments,which will lead to a decrease in positioning accuracy.Owing to the higher-precision synchronization provided in the sixth generation(6G)network,the errors of ranging-based positioning technologies can be effectively reduced.At the same time,the use of terahertz in 6G allows excellent resolution of range and angle,which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied environment.This paper introduces a multi-vehicle cooperative localization method.In the proposed method,the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside units.The multi-vehicle cooperative localization problem is represented using a factor graph.An iterative algorithm based on belief propagation is applied to perform the inference over the factor graph.The results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy.
基金supported by National Natural Science Foundation of China (Grant Nos. 11471037 and 11171129)Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20131101110048)
文摘We consider even factors with a bounded number of components in the n-times iterated line graphs L^n(G). We present a characterization of a simple graph G such that L^n(G) has an even factor with at most k components, based on the existence of a certain type of subgraphs in G. Moreover, we use this result to give some upper bounds for the minimum number of components of even factors in L^n(G) and also show that the minimum number of components of even factors in L^n(G) is stable under the closure operation on a claw-free graph G, which extends some known results. Our results show that it seems to be NP-hard to determine the minimum number of components of even factors of iterated line graphs. We also propose some problems for further research.
基金supported by the National Natural Science Foundation of China(No.91538103)。
文摘FBMC(Filter Bank Multicarrier)modulation is considered one of the waveform candidates in fifth generation wireless communication technology because of its several improved features compared to conventional orthogonal frequency division multiplexing schemes.A soft-input-soft-output factor-graph-based maximum-a-posterior detector is applied to FBMC systems.The detector achieves better performance than simple linear equalizers such as minimum mean square error and zero forcing in coded systems while exhibiting only a linear growth in complexity with the number of simultaneous interfering symbols.Furthermore,the proposed detector can be easily extended to cases where FBMC modulation is combined with multiple-input-multiple-output processing.The complexity of the detector is analyzed and the simulation results demonstrated its superior performance.
文摘This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter （EKF） in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System （GNSS/INS） integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.
基金the the National High Technology Research and Development 863 Program of China under Grant No. 2015AA124102, the Hebei Natural Science Foundation of China under Grant No. F2015203280, and the National Natural Science Foundation of China under Grant Nos. 61303130, 61272466, and 61303233.
文摘In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model （TPFG）. Second, we extract network features for ranking nodes. Finally, we propose RWR- Model, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms： traditional Ranking SVM （RSVM） and the individual doctor recommendation model （IDR-Model）. The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.
基金partly supported by the National Natural Science Foundation of China (Nos. 61632010 and 61602129)
文摘Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs （Single Nucleotide Polymorphisms）, which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.
