Location Estimation Technology Research Articles

A high-resolution microseismic source location method based on contrast source algorithm

SUMMARY Passive source location estimation technology plays an extremely important role in microseismic monitoring. However, the energy of microseismic signals is generally weak and prone to be interrupted as noise, which will affect the detection and location accuracy of microseismic events. Besides, the existing wave-equation-based source location estimation methods are not suitable for strongly heterogeneous strata, such as faults, salt bodies, fractures filled with proppant and other fluids, etc. These complex geological structures often trigger scattered waves. Thus, based on scattering theory, a high-resolution microseismic source location estimation method with a new grouping imaging condition is proposed by implementing a contrast source numerical simulation method in the process of wavefield backpropagation. First, the contrast source algorithm is used to perform wavefield extrapolation by solving the Helmholtz equation in the frequency domain, which can provide more comprehensive, effective information to get high-precision locations. Moreover, an optimized grouping imaging condition is proposed, which can improve the imaging resolution and computational efficiency. The effectiveness of the method we propose is illustrated using numerical examples. Compared with traditional source location estimation methods, including with low signal-to-noise ratio or multisource location estimation, this method results in strong energy focusing achieved in an effecient manner.

Development of an Ultrasound Technology-Based Indoor-Location Monitoring Service System for Worker Safety in Shipbuilding and Offshore Industry

We have built a worker-safety monitoring system (WSMS) for use in the shipbuilding and offshore industry (S&OI). The WSMS combines metal-conductor communication wireless networks with location-estimation technology that uses ultrasound signals in the frequency band 18 to 22 kHz, which is above the normal range of hearing (<15 kHz) in human adults. This system can be used in environments that include many metal barriers. The developmental process included deriving stakeholder requirements, transforming system requirements, designing system architecture, and developing a prototype. The prototype WSMS was tested by applying it to H Company, a Korean S&OI. Use of the WSMS increased the productivity of workers and managers by reducing the cost of on-site patrols and handwritten input. As a result, the number of worker location information updates increased from 2/day to 20/min, and productivity increased by 30 min/day compared to the previous method. Therefore, shipyards that introduce worker-safety management systems may consider applying this method.

Multimodel Framework for Indoor Localization Under Mobile Edge Computing Environment

Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to technical constraints of global positioning system technology for indoor environment and the popularity of the mobile edge computing servers. In this paper, the widely used single-model framework for indoor localization is presented as an introduction, which consists of three stages: 1) sample data collection; 2) model building; and 3) localization estimation. And then, through analyzing of the actual scene of indoor localization, a new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective. It is mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously. In order to make up for the shortcomings of this framework, two combinatorial optimization problems are proposed. Later, we discuss the NP-hardness of them in several different cases. In addition, two heuristic algorithms are given, and the performance of which are illustrated by the corresponding experimental results.

An Indoor Location-Based Positioning System Using Stereo Vision with the Drone Camera

Stereo vision is used to reconstruct 3D information of the space by estimating the depth value from the simulation of human eyes. Spatial restoration can be used as a means of location estimation in an indoor area, which is impossible to accomplish using the relative location estimation technology, GPS. By mapping the real world in virtual space, it is feasible to clear the boundary between real space and virtual space. This paper presents a method to control the drone indoors through a positioning system using Structure from Motion algorithm (SfM). SfM calculates the relative relationship between cameras based on images to be acquired from various locations and obtains disparity to enable restoration of 3D space. First, the 3D virtual space is reconstructed using several photographs taken from an indoor environment. Second, the real-time drone position is determined by comparing the 3D virtual space camera with the image displayed on the drone camera. In this case, if the direction of the virtual camera used for 3D virtual space construction is the same as the amount of yaw rotation of the drone, it is possible to quickly find the same position as the image seen in the real drone camera in the virtual space. As a result, if the scale of the actual camera image and the virtual camera image is 1 : 1 matched, then it is possible to know that the drone is in the position of the virtual camera. The proposed indoor location-based drone controlling method can be applied to various drone applications such as group flight in an indoor environment because of its ability to fly the drone without the use of the traditional remote-control and flight trajectory programming.

실내 환경에서 RSSI 차이를 이용한 AOA 기반 위치 추정 알고리즘

최근 실내 위치측위 기술을 이용하여 다양한 서비스가 이루어지고 있다. 실내 위치측위 방식에는 대표적으로 fingerprinting 방식과 삼변측량 방식이 있으나 활용의 제한성 및 위치추정 오차 등의 문제점이 있다. 이러한 문제점을 해결하기 위해 기존의 측위 방식인 AOA, TOA, TDOA 등의 측위 기술을 응용한 연구가 진행되고 있다. 본 논문에서는 실내 환경에서 RSSI 차이를 이용한 AOA 기반 위치 추정 알고리즘에 대해 연구한다. 4개의 안테나를 가지는 하나의 AP를 가정하여 연구를 진행하며, RSSI를 기반으로 도래각을 추정 후 AOA 알고리즘에 적용한다. RSSI의 보정을 위해 재귀식 평균 필터를 이용하며, 도래각 추정을 위해 보정된 RSSI와 피타고라스 정리를 이용한다. 실험 결과 좁은 간격으로 배치된 4개의 무지향성 안테나의 방사 패턴으로 인하여 18%의 오차율을 보였으며, 지향성 안테나를 이용할 경우 실내 환경에서 AOA 알고리즘을 활용할 수 있을 것으로 판단된다. There have recently been various services that use indoor location estimation technologies. Representative methods of location estimation include fingerprinting and triangulation, but they lack accuracy. Various kinds of research which apply existing location estimation methods like AOA, TOA, and TDOA are being done to solve this problem. In this paper, we study the location estimation algorithm based on AOA using a RSSI difference in indoor environments. We assume that there is a single AP with four antennas, and estimate the angle of arrival based on the RSSI value to apply the AOA algorithm. To compensate for RSSI, we use a recursive averaging filter, and use the corrected RSSI and the Pythagorean theorem to estimate the angle of arrival. The results of the experiment, show an error of 18% because of the radiation pattern of the four non-directional antennas arranged at narrow intervals.

Network Selection Algorithm at the Starting Point of Mobile Device in Indoor Wi-Fi Environment

In the location estimation technology using WLAN, RSSI coming from each AP has a large deviation due to complicated and varying indoor environments. Especially when a laptop is used for work inside a building or when a location-based service is required, the AP needs to be changed depending on RSSI coming from the laptop or the type of work. However, most studies are about hand-over technology between different types of devices based on mobile devices with good portability. Hand-over technology is generally controlled and managed at the server to identify the AP information and select a network quickly. Though this is the best choice to select network and connect communication quickly, any mobile device that is not registered in the server cannot be provided with the service. The current paper proposes a network selection algorithm at the starting point of mobile device as a way to solve the problem through simulation and performance evaluation.

Using the ellipse propagation model for mobile location estimation

Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. Among different kinds of mobile location estimation technologies, only the class of the signal strength based algorithm which estimates the location of Mobile Stations (MSs) by the Received Signal Strength (RSS) can be applied to different kinds of radio cellular networks, and therefore, is more general. We have designed a directional propagation model – the Ellipse Propagation Model (EPM), which makes use of a common signal propagation model to perform a location estimation. EPM enhanced the traditional propagation model by resembling the contour line of the signal strength as an ellipse rather than a circle and hence becoming more realistic. We have tested EPM with real data taken in Hong Kong and it is proven that EPM outperforms other existing location estimation algorithms among different kinds of terrains.