Rigid body localization (RBL) is to simultaneously estimate the position and attitude of a rigid target. In this paper, we focus on the RBL problem using a single base station (BS) and direction of arrival (DoA) measurements. Several wireless sensors are mounted on the rigid body of interest, and their topology information is known a priori. The single BS measures the DoAs of wireless sensor signals and fuses them with the sensor topology information to estimate the position and orientation of the rigid body and achieve RBL. We propose two RBL methods, namely, the observation matching (OM) algorithm and topology matching (TM) algorithm with refinement. The emerging participatory searching algorithm (PSA) is adopted in both methods to solve the nonlinear matching problems. Simulations show that, compared with the existing approach, the OM method can achieve better RBL accuracy under high DoA noise levels, while the performance of the TM algorithm with refinement is closer to the constrained Cramér–Rao bound (CCRB) under low DoA noise levels.
We propose a new approach for optical refocusing of three-dimensional (3-D) objects on their real depth without a pickup-range limitation based on subdivided-elemental image arrays (sub-EIAs) and local periodic δ-function arrays (L-PDFAs). The captured EIA from the 3-D objects locating out of the pickup-range, is divided into a number of sub-EIAs depending on the object distance from the lens array. Then, by convolving these sub-EIAs with each L-PDFA whose spatial period corresponds to the specific object's depth, as well as whose size is matched to that of the sub-EIA, arrays of spatially-filtered sub-EIAs (SF-sub-EIAs) for each object depth can be uniquely extracted. From these arrays of SF-sub-EIAs, 3-D objects can be optically reconstructed to be refocused on their real depth. Operational principle of the proposed method is analyzed based on ray-optics. In addition, to confirm the feasibility of the proposed method in the practical application, experiments with test objects are carried out and the results are comparatively discussed with those of the conventional method.
A novel method to computationally reconstruct perspective and orthographic view images with full resolution of a recording device from a single integral photograph is proposed. Firstly, a group of image slices that contain full yet redundant information to reconstruct the view image are generated, and the object surface is divided into pieces by the points that correspond to the centers of image slices. Secondly, the image slices that contribute to the pieces are extracted and redundant information embedded in them are figured out by common patches analysis. Finally, the view image is reconstructed by excluding the redundant information and resampling with maximum sampling rate. Each piece of the object surface is represented with 9 patches at most from 4 adjacent elemental images, and view images with high quality are reconstructed. Both simulations and experiments verify the validity of the method.
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