scholarly journals Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5544 ◽  
Author(s):  
Abdallah Alma’aitah ◽  
Baha’ Alsaify ◽  
Raed Bani-Hani
Keyword(s):  
Antenna Arrays ◽  
Low Cost ◽  
Three Dimensional ◽  
Reference Points ◽  
Average Error ◽  
Navigation Systems ◽  
Angle Of Arrival ◽  
Received Signal Strength Indicator ◽  
Localization Technique ◽  
Localization Accuracy

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of   10   m ×   10   m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

scholarly journals Assessing the Orange Tree Crown Volumes Using Google Maps as a Low-Cost Photogrammetric Alternative

Agronomy ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 893 ◽  
Author(s):  
Carmen Marín-Buzón ◽  
Antonio Pérez-Romero ◽  
Fabio Tucci-Álvarez ◽  
Francisco Manzano-Agugliaro
Keyword(s):  
Laser Scanning ◽  
Low Cost ◽  
Three Dimensional ◽  
Average Error ◽  
Tree Crown ◽  
High Definition ◽  
Google Maps ◽  
Traditional Methods ◽  
Aerial Photogrammetry ◽  
Dimensional Approximation

The accurate assessment of tree crowns is important for agriculture, for example, to adjust spraying rates, to adjust irrigation rates or even to estimate biomass. Among the available methodologies, there are the traditional methods that estimate with a three-dimensional approximation figure, the HDS (High Definition Survey), or TLS (Terrestrial Laser Scanning) based on LiDAR technology, the aerial photogrammetry that has re-emerged with unmanned aerial vehicles (UAVs), as they are considered low cost. There are situations where either the cost or location does not allow for modern methods and prices such as HDS or the use of UAVs. This study proposes, as an alternative methodology, the evaluation of images extracted from Google Maps (GM) for the calculation of tree crown volume. For this purpose, measurements were taken on orange trees in the south of Spain using the four methods mentioned above to evaluate the suitability, accuracy, and limitations of GM. Using the HDS method as a reference, the photogrammetric method with UAV images has shown an average error of 10%, GM has obtained approximately 50%, while the traditional methods, in our case considering ellipsoids, have obtained 100% error. Therefore, the results with GM are encouraging and open new perspectives for the estimation of tree crown volumes at low cost compared to HDS, and without geographical flight restrictions like those of UAVs.

scholarly journals AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2727 ◽  
Author(s):  
Ruonan Zhang ◽  
Jiawei Liu ◽  
Xiaojiang Du ◽  
Bin Li ◽  
Mohsen Guizani
Keyword(s):  
Antenna Arrays ◽  
Spread Spectrum ◽  
Signal Transmission ◽  
Three Dimensional ◽  
Target Localization ◽  
Sensor Nodes ◽  
Angle Of Arrival ◽  
Transmission Scheme ◽  
Industrial Wireless Sensor Networks ◽  
Estimation Errors

High-precision and fast relative positioning of a large number of mobile sensor nodes (MSNs) is crucial for smart industrial wireless sensor networks (SIWSNs). However, positioning multiple targets simultaneously in three-dimensional (3D) space has been less explored. In this paper, we propose a new approach, called Angle-of-Arrival (AOA) based Three-dimensional Multi-target Localization (ATML). The approach utilizes two anchor nodes (ANs) with antenna arrays to receive the spread spectrum signals broadcast by MSNs. We design a multi-target single-input-multiple-output (MT-SIMO) signal transmission scheme and a simple iterative maximum likelihood estimator (MLE) to estimate the 2D AOAs of multiple MSNs simultaneously. We further adopt the skew line theorem of 3D geometry to mitigate the AOA estimation errors in determining locations. We have conducted extensive simulations and also developed a testbed of the proposed ATML. The numerical and field experiment results have verified that the proposed ATML can locate multiple MSNs simultaneously with high accuracy and efficiency by exploiting the spread spectrum gain and antenna array gain. The ATML scheme does not require extra hardware or synchronization among nodes, and has good capability in mitigating interference and multipath effect in complicated industrial environments.

Localization of Distributed Wireless Sensor Networks using Two Sage SDP Optimization

2017 ◽  
Vol 7 (3) ◽  
pp. 1255
Author(s):  
Reza Shahbazian ◽  
Seyed Ali Ghorashi
Keyword(s):  
Optimization Problem ◽  
Low Cost ◽  
Reference Points ◽  
Wireless Sensor ◽  
Relaxation Techniques ◽  
Problem Size ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Cost Efficient

<span class="fontstyle0">A wireless sensor network (WSN) may comprise a large distributed set of low cost, low power sensing nodes. In many applications, the location of sensors is a necessity to evaluate the sensed data and it is not energy and cost efficient to equip all sensors with global positioning systems such as GPS. In this paper, we focus on the localization of sensors in a WSN by solving an optimization problem. In WSN localization, some sensors (called anchors) are aware of their location. Then, the distance measurements between sensors and anchors locations are used to localize the whole sensors in the network. WSN localization is a non-convex optimization problem, however, relaxation techniques such as semi-definite programming (SDP) are used to relax the optimization. To solve the optimization problem, all constraints should be considered simultaneously and the solution complexity order is O(n2) </span><span class="fontstyle0">where </span><span class="fontstyle2">n </span><span class="fontstyle0">is the number of sensors. The complexity of SDP prevents solving large size problems. Therefore, it would be beneficial to reduce the problem size in large and distributed WSNs. In this paper, we propose a two stage optimization to reduce the solution time, while provide better accuracy compared with original SDP method. We first select some sensors that have the maximum connection with anchors and perform the SDP localization. Then, we select some of these sensors as virtual anchors. By adding the virtual anchors, we add more reference points and decrease the number of constraints. We propose an algorithm to select and add virtual anchors so that the total solution complexity and time decrease considerably, while improving the localization accuracy.</span>

A LOW-COST RANGE-FREE LOCALIZATION SCHEME FOR THREE-DIMENSIONAL UNDERWATER SENSOR NETWORKS

2013 ◽  
Vol 05 (01) ◽  
pp. 1350005
Author(s):  
XIANLING LU ◽  
DEYING LI ◽  
YI HONG ◽  
WENPING CHEN
Keyword(s):  
Sensor Networks ◽  
Low Cost ◽  
Three Dimensional ◽  
Underwater Sensor Networks ◽  
Transmission Power ◽  
Localization Accuracy ◽  
Localization Scheme ◽  
Variable Transmission ◽  
Anchor Nodes ◽  
Range Free

Localization is one of the fundamental tasks for underwater sensors networks (USNs). It is required for data tagging, target detection, route protocols, and so on. In this paper, we propose an efficient low-cost range-free localization scheme for 3D underwater sensor networks (3D-LRLS) without any additional hardware infrastructure. In our scheme, each anchor node has variable transmission power levels. At first, the power levels of each anchor are decided by the Delaunay triangulation for the network space. Then, ordinary sensors listen to the beacons sent from the anchor nodes. Based on the beacon messages, each node calculates its location individually by a low computational complexity method. The extensive simulation results demonstrate that 3D-LRLS is efficient in terms of both localization ratio and localization accuracy.

scholarly journals A New Perspective on Low-Cost MEMS-Based AHRS Determination

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1383
Author(s):  
Neda Navidi ◽  
Rene Landry
Keyword(s):  
Reference System ◽  
Dimensional Space ◽  
Low Cost ◽  
Three Dimensional ◽  
Navigation Systems ◽  
Adaptive Kalman Filter ◽  
Initial Attitude ◽  
New Perspective ◽  
And Control ◽  
Three Dimensional Space

Attitude and heading reference system (AHRS) is the term used to describe a rigid body’s angular orientation in three-dimensional space. This paper describes an AHRS determination and control system developed for navigation systems by integrating gyroscopes, accelerometers, and magnetometers signals from low-cost MEMS-based sensors in a complementary adaptive Kalman filter. AHRS estimation based on the iterative Kalman filtering process is required to be initialized first. A new method for AHRS initialization is proposed to improve the accuracy of the initial attitude estimates. Attitude estimates derived from the initialization and iterative adaptive filtering processes are compared with the orientation obtained from a high-end reference system. The improvement in the accuracy of the initial orientation as significant as 45% is obtained from the proposed method as compared with other selected techniques. Additionally, the computational process is reduced by 96%.

scholarly journals Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique

2021 ◽  
Vol 51 (2) ◽  
pp. E20
Author(s):  
Gorkem Yavas ◽  
Kadri Emre Caliskan ◽  
Mehmet Sedat Cagli
Keyword(s):  
Augmented Reality ◽  
Mobile Device ◽  
Low Cost ◽  
Three Dimensional ◽  
Skin Incision ◽  
Optical Tracking ◽  
Patient Specific ◽  
Navigation Systems ◽  
Intracranial Tumors ◽  
3D Printed

OBJECTIVE The aim of this study was to assess the precision and feasibility of 3D-printed marker–based augmented reality (AR) neurosurgical navigation and its use intraoperatively compared with optical tracking neuronavigation systems (OTNSs). METHODS Three-dimensional–printed markers for CT and MRI and intraoperative use were applied with mobile devices using an AR light detection and ranging (LIDAR) camera. The 3D segmentations of intracranial tumors were created with CT and MR images, and preoperative registration of the marker and pathology was performed. A patient-specific, surgeon-facilitated mobile application was developed, and a mobile device camera was used for neuronavigation with high accuracy, ease, and cost-effectiveness. After accuracy values were preliminarily assessed, this technique was used intraoperatively in 8 patients. RESULTS The mobile device LIDAR camera was found to successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The targeting error that was measured ranged from 0.5 to 3.5 mm (mean 1.70 ± 1.02 mm, median 1.58 mm). The mean preoperative preparation time was 35.7 ± 5.56 minutes, which is longer than that for routine OTNSs, but the amount of time required for preoperative registration and the placement of the intraoperative marker was very brief compared with other neurosurgical navigation systems (mean 1.02 ± 0.3 minutes). CONCLUSIONS The 3D-printed marker–based AR neuronavigation system was a clinically feasible, highly precise, low-cost, and easy-to-use navigation technique. Three-dimensional segmentation of intracranial tumors was targeted on the brain and was clearly visualized from the skin incision to the end of surgery.

scholarly journals BDALT: Bilateration and Double Anchor-based Localization Technique for Wireless Sensor Networks using Angle of Arrival (AoA) and Received Signal Strength (RSS) -A Hybrid Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 4126-4135
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Research Work ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Hybrid Technique ◽  
Angle Of Arrival ◽  
Localization Technique ◽  
Localization Accuracy ◽  
Zigbee Technology

Anchor based localization techniques are better and cost free alternate solutions against GPS based localization techniques in Wireless Sensor Networks (WSN) applications. Anchor based localization techniques using Zigbee technology in WSN have attracted highly in the recent years. As the technology is growing, customers can easily buy the Zigbee technology based beacon nodes at reasonable price. Anchor based wireless sensor networks are used in the localization process because of their advantages in location tracking applications. Many localization systems depend only on RSS and that is not highly reliable, and not accurate. However, the accuracy and the reliability of the network is rather important when it is used in wireless environments. In this paper, a hybrid technique is proposed, which uses not only the RSS as distance estimation parameter, but also the AoA as another angular estimation parameter to improve the localization accuracy. The proposed localization technique uses both the dynamic vectors based localization method, and the bilateration based localization methods to get the target node’s coordinates with good accuracy. The main objective of this research work is to get the accurate location information of the target nodes by using double anchor nodes and simple mathematical computations. The proposed method is simulated in MATLAB and its performance is better than the existing localization techniques.

scholarly journals Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach

2014 ◽  
Vol 68 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Zhiwen Xian ◽  
Xiaoping Hu ◽  
Junxiang Lian
Keyword(s):  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Three Dimensional ◽  
Fast Response ◽  
Location Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Stereo Camera

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

scholarly journals Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3838
Author(s):  
Anh Tuyen Le ◽  
Le Chung Tran ◽  
Xiaojing Huang ◽  
Christian Ritz ◽  
Eryk Dutkiewicz ◽  
...  
Keyword(s):  
Antenna Arrays ◽  
Measurement Errors ◽  
Signal Strength ◽  
Least Square ◽  
Wireless Sensor ◽  
Angle Of Arrival ◽  
Received Signal Strength Indicator ◽  
Single Antenna ◽  
The Impact ◽  
Source Positioning

Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

Indoor localization algorithm based on behavior-driven predictive learning in crowdsourced Wi-Fi environments

2019 ◽  
Vol 33 (14n15) ◽  
pp. 1940036 ◽  
Author(s):  
Boney A. Labinghisa ◽  
Dong Myung Lee
Keyword(s):  
Shortest Path ◽  
Indoor Localization ◽  
Principal Component ◽  
Reference Points ◽  
Average Error ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Predictive Learning ◽  
Starting Location ◽  
Error Distance

The indoor localization algorithm based on the behavior-driven predictive learning (BDPLA) executes machine-learning predictions by computing the shortest path from a starting location to a destination. The proposed algorithm selects a set of reference points (RPs) to predict the shortest path using all available RPs from the crowdsourced Wi-Fi environment. In addition, the proposed algorithm utilizes the collected received signal strength indicator (RSSI) values to determine the error distance. Using principal component analysis (PCA), the existing crowdsourced RSSI data can be calibrated to help decrease the inconsistent RSSI values among all received signals by reconstructing the values. The average error distance of 3.68 m achieved better results compared with the traditional fingerprint map with an average result of 6.96 m.

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