Positioning Locality Using Cognitive Directions Based on Indoor Landmark Reference System

Abstract

Locality descriptions are generally communicated using reference objects and spatial relations that reflect human spatial cognition. However, uncertainty is inevitable in locality descriptions. Positioning locality with locality description, with a mapping mechanism between the qualitative and quantitative data, is one of the important research issues in next-generation geographic information sciences. Spatial relations play an important role in the uncertainty of positioning locality. In indoor landmark reference systems, the nearest landmarks can be selected when describing localities by using direction relations indoors. By using probability operation, we combine a set of uncertainties, that is, near and direction relations to positioning locality. Some definitions are proposed from cognitive and computational perspectives. We evaluate the performance of our method through indoor cognitive experiments. Test results demonstrate that a positioning accuracy of 3.55 m can be achieved with the semantically derived direction relationships in indoor landmark reference systems.

Indoors Positioning Based on Spatial Relationships in Locality Description

“…Compared to distance and direction relations, topological relations convey rough locality information and can be reflected by the distance and direction relations [11]. To avoid ambiguities, distance and direction are usually combined in locality descriptions, such as ''object A is 100 m in front, and object B is 50 m to the right'' [13]. However, locality descriptions are complex, either explicitly or implicitly [27], such as ''object A is 100 m in front, and object B is to the right'' implies that ''object A is 100 m in front, object B is to the right and object B is near me''.…”

“…The quantitative distance in locality descriptions for species data or Search and Rescue (SAR) confirms to record imprecision, so Barve [47] and Doherty et al [14] used this model to locate places. Conducting a cognitive experiment, Wang et al [13] determined that the uncertainty of numeric distance derived from measurement error is consistent with a normal distribution.…”

“…Quantitative distance in locality description is based on visual cognition and measurements, which confirms a normal distribution. According to [13], cognitive experiments are conducted to model quantitative distances (10 m, 30 m, 50 m). In the experiments, each group distance has two marked points (i.e., straight or inclined, which is almost 45 • ), and participants are asked to stand at the marked points to describe the distances between the marked points and reference objects.…”

“…Compared to GIS, locality descriptions focus more on direction and distance relations than topological relations [12]. Describing locality, distance and direction together provides more locality cues, which avoids ambiguities or uncertainties [13].…”

Design and Implementation of Synchronization-free TDOA Localization System Based on UWB

“…At present, the location-based service (LBS) in the indoor environment is of great importance in many applications [1], [2]. Common solutions used for indoor localization include Zigbee [3], RFID [4], WiFi [5], Bluetooth [6], indoor landmark reference system [7] and UWB [8]. Compared to other solutions, UWB can provide the fine time resolution and have the ability to resist multi-path interference [9], [10].…”