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Markov Model Inferencing in Distributed Systems
Published in S. Sitharama Iyengar, Richard R. Brooks, Distributed Sensor Networks, 2016
Chen Lu, Jason M. Schwier, Richard R. Brooks, Christopher Griffin, Satish Bukkapatnam
In Ref. [21], CSSR algorithm is used for modeling behaviors of ships. For symbols, they use the Military Grid Reference System (MGRS) [38]. As a ship traverses the globe, it produces symbols corresponding to the grid regions in MGRS. They can model the behavior of the ship using CSSR. The result is a Markov model describing the probabilistic motion of a vessel as it traverses the planet. Within the regions, they also use linear models of a ship’s behavior to further refine the predictions. The CSSR model and the linear models work together to produce predictions of ship location and to identify anomalies in ship behavior. Figure Figure 26.2 shows an example of a ship prediction using models derived from CSSR for the Disney Magic available from http://sailwx.org
Optimizing Computational Cost and Communication Overhead in Cooperative Localization
Published in Chao Gao, Guorong Zhao, Hassen Fourati, Cooperative Localization and Navigation, 2019
Panagiotis Agis Oikonomou-Filandras, Kai-Kit Wong
An often-unexplored issue in localization is how the coordinate system itself works. Localization occurs relative to some commonly accepted reference points. These are called geodetic datum. Two of the more commonly known ones are the WGS84 and the NAD83, cf. [1]. GPS uses a polar system, and the origin of the coordinate system is the center of the planet. In cooperative localization systems, though, typically a Cartesian system is assumed, and the nodes localize relative to themselves. Hence, all nodes localize with respect to a local coordinate system (LCS). As a result, an implicit assumption is hidden in every model: All nodes know the precise location of the origin point and their relative position to it. This assumption means that even though the nodes use their LCS to localize, a global coordinate system (GCS) is assumed to be known by everyone so that every node can convert their LCS to the GCS coordinates. Otherwise, we could either arbitrarily rotate the whole coordinate system or move the origin arbitrarily around and the solutions of the node coordinates in the LCS would still be valid. The problem has been identified in [17], and a solution assuming the anchors can provide knowledge of their orientation is suggested in [6]. Grid-BP as a general solution using the NATO Military Grid Reference System (MGRS) has been suggested in [32]. In practice, besides the MGRS, any arbitrary coordinate system can be easily used as long as it is fixed and shared across all nodes, and uniquely identifies every grid “box” in the area of interest. For example, a plan map of an airport, split into grids.
Cloud-based storage and computing for remote sensing big data: a technical review
Published in International Journal of Digital Earth, 2022
Chen Xu, Xiaoping Du, Xiangtao Fan, Gregory Giuliani, Zhongyang Hu, Wei Wang, Jie Liu, Teng Wang, Zhenzhen Yan, Junjie Zhu, Tianyang Jiang, Huadong Guo
Scenes are the most basic organization for remote sensing data and have been widely applied during the past several decades. Conventionally, satellites collect remote sensing data in strips and transmit them to the ground segment. The ground segment processes the remote sensing data through corrections and evaluations and profiles them according to a regular grid (e.g. the Military Grid Reference System adopted by Sentinel). The pre-processed remote sensing data are the most common form of remote sensing data corresponding to the remote sensing images fetched from data providers (e.g. USGS, ESA Copernicus). The use of cloud computing greatly diminishes the cost of acquiring scene data. In addition, COG technology also enhances the efficiency of remote sensing data access with higher degrees of freedom.
Constant-level spatio-temporal integrated search algorithm for repeating sun-synchronous orbit satellite images
Published in International Journal of Digital Earth, 2021
More generally, this method is good for RSSO satellites because they have a regular reference grid system and a periodic revisiting characteristic. Using this algorithm for other satellites just consists of five steps: building the one-dimensional grid sequence in time order, grouping the historical images according to the grid sequence, analyzing the spatio-temporal pattern of the historical images in each grid unit, building the st-LUT spatial–temporal integrated index, and finally realizing spatio-temporal query algorithms. The structure of the st-LUT is decided by the organization of the native reference grid systems employed by individual satellite systems. For example, Landsat 8 uses WRS-2, and Sentinel-2′s tiling grid is based on the Military Grid Reference System (MGRS). Regardless of the type of reference grid system used, the revisiting feature is always applicable for these RSSO satellites.