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
本文研究了一类具有不同采样率的分布式多传感器动态系统的数据融合问题,针对一类采样率呈有理数倍关系的动态系统,提出一种基于多源异步采样数据的新融合算法.新算法首先是将来自各个传感器的测量值在融合中心的坐标系中和时钟下进行...本文研究了一类具有不同采样率的分布式多传感器动态系统的数据融合问题,针对一类采样率呈有理数倍关系的动态系统,提出一种基于多源异步采样数据的新融合算法.新算法首先是将来自各个传感器的测量值在融合中心的坐标系中和时钟下进行映射统一;其次,以对目标状态下一时刻的预测值与目标在该时刻状态的估计值之差为基础,建立起描述该融合周期内各个观测点处的目标状态向量之间的动态模型;然后,以该时刻目标状态基于全局信息的估计值为条件,结合建立的新模型和传统的K a lm an滤波器,利用本周期内按序到达的各传感器观测值,依次对各个观测点处目标的状态进行估计和更新;最后,在顺序得到本周期内各个观测点处目标估计值的同时,也将获得下一时刻目标状态基于全局信息的估计值或预测估计值.文中在给出新算法基本思想的同时,也较为详细地对融合算法进行了推导,并通过计算机仿真的方法,将新算法与基于时间校准的算法在估计精确度上进行了比较,从而验证了新算法的有效性.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.
文摘本文研究了一类具有不同采样率的分布式多传感器动态系统的数据融合问题,针对一类采样率呈有理数倍关系的动态系统,提出一种基于多源异步采样数据的新融合算法.新算法首先是将来自各个传感器的测量值在融合中心的坐标系中和时钟下进行映射统一;其次,以对目标状态下一时刻的预测值与目标在该时刻状态的估计值之差为基础,建立起描述该融合周期内各个观测点处的目标状态向量之间的动态模型;然后,以该时刻目标状态基于全局信息的估计值为条件,结合建立的新模型和传统的K a lm an滤波器,利用本周期内按序到达的各传感器观测值,依次对各个观测点处目标的状态进行估计和更新;最后,在顺序得到本周期内各个观测点处目标估计值的同时,也将获得下一时刻目标状态基于全局信息的估计值或预测估计值.文中在给出新算法基本思想的同时,也较为详细地对融合算法进行了推导,并通过计算机仿真的方法,将新算法与基于时间校准的算法在估计精确度上进行了比较,从而验证了新算法的有效性.
