图像超分辨率重构(superresolution reconstruction,SRR)是一个利用序列图像来求解理想图像的逆过程,基本原理就是把各图像上由于亚像素位移而存在的互补信息融合到同一幅图像中,从而达到提高分辨率的目的。我们采用最大后验概率(maximum a posteriori,MAP)算法进行超分辨率重构。根据贝叶斯原理,高分辨率图像的后验概率等价于以下两项之积:已知理想高分辨率图像的前提下,低分辨率图像出现的条件概率;理想高分辨率图像的先验概率。MAP算法的基本思想就是在已知低分辨率图像前提下,使出现高分辨率图像的后验概率达到最大。该方法的优点是在复原过程中可以直接加入先验约束,能确保解的存在和唯一,降噪能力强和收敛稳定性高等。
In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.
