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
A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coef...A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.展开更多
Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed full-order optimal fusion Kalman filter (DFFKF) is given for discrete-time stochastic singular syste...Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed full-order optimal fusion Kalman filter (DFFKF) is given for discrete-time stochastic singular systems with multiple sensors, which involves the inverse of a high-dimension matrix to compute matrix weights. To reduce the computational burden, a distributed reduced-order fusion Kalman filter (DRFKF) is presented, which involves in parallel the inverses of two relatively low-dimension matrices to compute matrix weights. A simulation example shows the effectiveness.展开更多
For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting...For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.展开更多
In distributed multisensor data fusion systems, there are two types of track fusion approaches. One is sensor track fusion with feedback information, the other is without feedback information. This paper proves that t...In distributed multisensor data fusion systems, there are two types of track fusion approaches. One is sensor track fusion with feedback information, the other is without feedback information. This paper proves that the solutions of sensor track fusion with and without feedback information are both optimal and equal.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
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.
文摘A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.
基金Supported by National Natural Science Foundation of P. R. China (60504034) Youth Foundation of Heilongjiang Province (QC04A01) Outstanding Youth Foundation of Heilongjiang University (JC200404)
文摘Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed full-order optimal fusion Kalman filter (DFFKF) is given for discrete-time stochastic singular systems with multiple sensors, which involves the inverse of a high-dimension matrix to compute matrix weights. To reduce the computational burden, a distributed reduced-order fusion Kalman filter (DRFKF) is presented, which involves in parallel the inverses of two relatively low-dimension matrices to compute matrix weights. A simulation example shows the effectiveness.
基金supported by the National Natural Science Foundation of China(60874063)Science and Technology Research Foundation of Heilongjiang Education Department(11551355)Key Laboratory of Electronics Engineering,College of Heilongjiang Province(DZZD20105)
文摘For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.
基金Supported in part by the University of Colorado, the US National Science Foundation (Grants CMS-9625086,CMS-0201459, IIS-9711936, and HRD-0095944) the US Office of Naval Research (Grants N00014-97-1-0642 and N00014-02-1-0136) the Colorado Center for Information Storage, the Colorado Advanced Software Institute, Maxtor Corporation, Quantum Corporation, Storage Technology Corporation, and Data Fusion Corporation
文摘Research in control systems, sensor fusion and haptic interfaces is reviewed.
文摘In distributed multisensor data fusion systems, there are two types of track fusion approaches. One is sensor track fusion with feedback information, the other is without feedback information. This paper proves that the solutions of sensor track fusion with and without feedback information are both optimal and equal.
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.
