An All-Time-Domain Moving Object Data Model, Location Updating Strategy, and Position Estimation

Abstract

To solve the problems from the existing moving objects data models, such as modeling spatiotemporal object continuous action, multidimensional representation, and querying sophisticated spatiotemporal position, we firstly established an object-oriented alltime-domain data model for moving objects. The model added dynamic attributes into object-oriented model, which supported all-time-domain data storage and query. Secondly, we proposed a new dynamic threshold location updating strategy. The location updating threshold was given dynamically in accordance with the velocity, accuracy, and azimuth positioning information from the GPS. Thirdly, we presented several different position estimation methods to estimate the historical location and future location. The cubic Hermite interpolation function is used to estimate the historical location. Linear extended positioning method, velocity mean value positioning method, and cubic exponential smoothing positioning method were designed to estimate the future location. We further implemented the model by abstracting the data types of moving object, which was established by PL\SQL and extended Oracle Spatial. Furthermore, the model was tested through the different moving objects. The experimental results illustrate that the location updating frequency can be effectively reduced, and thus the position information transmission flow and the data storage were reduced without affecting the moving objects trajectory precision.

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

“…To verify the effectiveness of the proposed ship trajectory feature extraction method, some popular single classifier learning algorithms in the machine learning field were selected in this paper for training and testing the ship classification model, including Decision Tree (DT), Naïve Bayers (NB), Logistic Regression (LR), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) [ 21 ]. The test was implemented with the Python programming language and the scientific computing environment Anaconda.…”

“…The SituationAnalysis class contains the object dbConfig indicating the database configuration, the objects spatialRange and timeRange indicating the time and spatial ranges selected by a user, the object simStep determining the speed of replay, and the object trajData for the storage and query of trajectory data. Among the main methods for the class, SelectTrajs() is used to check the trajectory in the trajectory database based on the time and spatial ranges given by a user, and depends on the DatabaseModel class; RunSituation() calculates the location of each target at each time of simulation by calling the simulation module (the SimulationModel class) and replays the maritime situation using the simulation step simStep through the geographical information display module (the GISDisplayModel class); TargetStats() is employed for the statistics of the information on various targets, e.g., quantity, and displays the information in the form of a report [ 20 , 21 , 22 ].…”

Using Geographic Ontologies and Geo-Characterization to Represent Geographic Scenarios

“…Among these models, time, space, and attribute are three basic components that are used to characterize each geo-object [24]. Object-oriented models support direct mapping to represent moving objects over multiple granularities [25][26][27]. The second category of GIS models attempts to capture events and changes, and it arouses enormous interest in GIS to engage geographic processes and causality in discussions [28][29][30][31][32].…”

Vehicle Position Updating Strategy Based on Kalman Filter Prediction in VANET Environment

“…Wu et al [8] established an object-oriented all-time-domain data model for moving objects. The model added dynamic attributes to object-oriented model, which supported alltime-domain data storage and query.…”