Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigat...Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.展开更多
Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unkn...Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.展开更多
To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to ac...To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.展开更多
The D-S evidential reasoning algorithm is invalid when the evidence is completely contradicted. Therefore,a modified algorithm is proposed based on the elemental correlation and the influence of elemental weights in t...The D-S evidential reasoning algorithm is invalid when the evidence is completely contradicted. Therefore,a modified algorithm is proposed based on the elemental correlation and the influence of elemental weights in the evidence. The modified algorithm is more powerful ability to rectify errors and less computational complexity in the circumstance of multi-evidence fusion processing than those of the D-S evidential reasoning algorithm.展开更多
In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that cons...In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.展开更多
A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse ...A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse d with self-adaptive weighing technique. Finally performance of the method is d emonstrated by an online vibration measurement case. The results show that the f used data are more stable, sensitive, accurate, reliable than that of single sen sor data.展开更多
针对卫星双向时间频率传递(two-way satellite time and frequency transfer, TWSTFT)存在周日效应、短期稳定度不高的问题,通过引入基于注意力机制的Transformer权值矩阵,利用Vondrak-Cepek组合滤波的方法将中国科学院国家授时中心(Nat...针对卫星双向时间频率传递(two-way satellite time and frequency transfer, TWSTFT)存在周日效应、短期稳定度不高的问题,通过引入基于注意力机制的Transformer权值矩阵,利用Vondrak-Cepek组合滤波的方法将中国科学院国家授时中心(National Time Service Center, NTSC)、德国物理技术研究院(Physikalisch-Technische Bundesanstalt, PTB)之间的TWSTFT和全球定位系统(Global Positioning System, GPS)P3码共视法的时间比对链路进行融合,分析融合前后链路的性能指标并与没有周日效应、短期稳定度高的GPS精密单点定位(GPS precise point positioning, GPS PPP)时间比对参考链路进行比较。结果表明,引入注意力机制权值的Vondrak-Cepek组合滤波融合方法与参考链路GPS PPP的标准差为0.310 9 ns,具有改善TWSTFT周日效应、提升链路整体稳定性的作用。展开更多
高放废物地质处置特别是地下实验室研发过程中的多源数据融合挖掘研究具有重要意义(Wang Ju et al.,2018)。然而,目前阶段尚未实现对研发过程中多源数据的融合挖掘与二次应用。针对上述问题,从地下实验室多源监测数据特点出发,在确定地...高放废物地质处置特别是地下实验室研发过程中的多源数据融合挖掘研究具有重要意义(Wang Ju et al.,2018)。然而,目前阶段尚未实现对研发过程中多源数据的融合挖掘与二次应用。针对上述问题,从地下实验室多源监测数据特点出发,在确定地下实验室多源监测数据模型构建的基础上,结合深度学习技术,初步构建了地下实验室多源监测数据融合技术方法,并初步开展了数据融合设计,为处置库场址评价和安全评价等综合评价工作提供了新的研究思路。展开更多
文摘Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.
基金supported by the National Natural Science Foundation of China(61931015,62071335)the Science and Technology Program of Shenzhen(JCYJ20170818112037398)the Technological Innovation Project of Hubei Province of China(2019AAA061).
文摘Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.
基金Projects(51375222,51175242)supported by the National Natural Science Foundation of China
文摘To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.
文摘The D-S evidential reasoning algorithm is invalid when the evidence is completely contradicted. Therefore,a modified algorithm is proposed based on the elemental correlation and the influence of elemental weights in the evidence. The modified algorithm is more powerful ability to rectify errors and less computational complexity in the circumstance of multi-evidence fusion processing than those of the D-S evidential reasoning algorithm.
文摘In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.
文摘A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse d with self-adaptive weighing technique. Finally performance of the method is d emonstrated by an online vibration measurement case. The results show that the f used data are more stable, sensitive, accurate, reliable than that of single sen sor data.
文摘针对卫星双向时间频率传递(two-way satellite time and frequency transfer, TWSTFT)存在周日效应、短期稳定度不高的问题,通过引入基于注意力机制的Transformer权值矩阵,利用Vondrak-Cepek组合滤波的方法将中国科学院国家授时中心(National Time Service Center, NTSC)、德国物理技术研究院(Physikalisch-Technische Bundesanstalt, PTB)之间的TWSTFT和全球定位系统(Global Positioning System, GPS)P3码共视法的时间比对链路进行融合,分析融合前后链路的性能指标并与没有周日效应、短期稳定度高的GPS精密单点定位(GPS precise point positioning, GPS PPP)时间比对参考链路进行比较。结果表明,引入注意力机制权值的Vondrak-Cepek组合滤波融合方法与参考链路GPS PPP的标准差为0.310 9 ns,具有改善TWSTFT周日效应、提升链路整体稳定性的作用。
文摘高放废物地质处置特别是地下实验室研发过程中的多源数据融合挖掘研究具有重要意义(Wang Ju et al.,2018)。然而,目前阶段尚未实现对研发过程中多源数据的融合挖掘与二次应用。针对上述问题,从地下实验室多源监测数据特点出发,在确定地下实验室多源监测数据模型构建的基础上,结合深度学习技术,初步构建了地下实验室多源监测数据融合技术方法,并初步开展了数据融合设计,为处置库场址评价和安全评价等综合评价工作提供了新的研究思路。
