scholarly journals Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2400
Author(s):  
Ziyong Zhang ◽  
Xiaoling Xu ◽  
Jinqiang Cui ◽  
Wei Meng
Keyword(s):  
Quadratic Programming ◽  
Wide Band ◽  
Area Coverage ◽  
Localization Algorithm ◽  
Placement Problem ◽  
Position Information ◽  
Relative Localization ◽  
Coverage Control ◽  
Localization Algorithms ◽  
Multi Uav

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

scholarly journals A Survey on DV-Hop localization Techniques in Three- Dimensional Wireless Sensor Networks

2020 ◽  
Vol 16 (10) ◽  
pp. 23
Author(s):  
Fengrong Han ◽  
Izzeldin Ibrahim Mohamed Abdelaziz ◽  
Xinni Liu ◽  
Kamarul Hawari Ghazali ◽  
Hao Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Future Research ◽  
Localization Algorithm ◽  
Monitoring And Control ◽  
Position Information ◽  
Localization Algorithms ◽  
Range Free

Location information is prerequisite for wireless sensor networks (WSNs) monitoring and control applications, since there is no meaning without position information for collected data. Distance vector hop (DV-Hop) localization algorithm as the typical range-free algorithm that has been widely applied in various applications. Nowadays, the research on range-free localization for WSNs is mostly based on two-dimensional (2D) space. Hence, there are few surveys concentrated on range-free localization in three-dimensional (3D) WSNs. This motivated us to present an extensive overview of enhanced DV-Hop localization algorithms in 3D WSNs. This paper focused on critical challenge between 2D and 3D in localization model, representative range-free 3D localization technique surveys. Moreover, a comprehensive taxonomy of most essential enhanced methods applied in 3D DV-Hop is illustrated. A considerable comparison in term of localization error, computational complexity and node type is given. Future research directions dealing with localization under 3D DV-Hop is also discussed.

scholarly journals Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (8) ◽  
pp. 1483
Author(s):  
Yuan Sun
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Data ◽  
Wide Band ◽  
Relative Navigation ◽  
Monitoring Method ◽  
Integrity Monitoring ◽  
Position Information ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Multi Uav

Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.

Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

scholarly journals NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2991 ◽  
Author(s):  
Jingyu Hua ◽  
Yejia Yin ◽  
Weidang Lu ◽  
Yu Zhang ◽  
Feng Li
Keyword(s):  
Hypothesis Test ◽  
Intermediate Position ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Algorithms ◽  
Anchor Nodes ◽  
Positioning Algorithm ◽  
Non Line Of Sight

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

scholarly journals V2X-Based Mobile Localization in 3D Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Iram Javed ◽  
Xianlun Tang ◽  
Kamran Shaukat ◽  
Muhammed Umer Sarwar ◽  
Talha Mahboob Alam ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Three Dimensional ◽  
Link Quality ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Position Information ◽  
Mobile Anchor ◽  
Anchor Nodes

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies.

scholarly journals Scalarized Q Multi-Objective Reinforcement Learning for Area Coverage Control and Light Control Implementation

2018 ◽  
Vol 16 (2) ◽  
pp. 72-82
Author(s):  
Akkhachai Phuphanin ◽  
Wipawee Usaha
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Area Coverage ◽  
Coverage Area ◽  
Lighting Control ◽  
Multi Objective ◽  
Coverage Control ◽  
Control Schemes ◽  
Multi Agent ◽  
Testbed Experiments

Coverage control is crucial for the deployment of wireless sensor networks (WSNs). However, most coverage control schemes are based on single objective optimization such as coverage area only, which do not consider other contradicting objectives such as energy consumption, the number of working nodes, wasteful overlapping areas. This paper proposes on a Multi-Objective Optimization (MOO) coverage control called Scalarized Q Multi-Objective Reinforcement Learning (SQMORL). The two objectives are to achieve the maximize area coverage and to minimize the overlapping area to reduce energy consumption. Performance evaluation is conducted for both simulation and multi-agent lighting control testbed experiments. Simulation results show that SQMORL can obtain more efficient area coverage with fewer working nodes than other existing schemes.  The hardware testbed results show that SQMORL algorithm can find the optimal policy with good accuracy from the repeated runs.

scholarly journals Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2790 ◽  
Author(s):  
Jitong Zhang ◽  
Mingrong Ren ◽  
Pu Wang ◽  
Juan Meng ◽  
Yuman Mu
Keyword(s):  
Random Field ◽  
Indoor Localization ◽  
Conditional Random Field ◽  
A Priori ◽  
Three Dimensional ◽  
Map Matching ◽  
Localization Algorithm ◽  
Position Information ◽  
Feedback Information ◽  
Poor Lighting

High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.

scholarly journals Research on Localization of Unknown Nodes in Wireless Sensor Network based on Centroid Iteration

2017 ◽  
Vol 13 (03) ◽  
pp. 149
Author(s):  
Dina Li
Keyword(s):  
Relative Error ◽  
Iteration Algorithm ◽  
Stopping Criterion ◽  
Node Localization ◽  
Relative Localization ◽  
Accurate Localization ◽  
Radio Range ◽  
Localization Algorithms ◽  
Calculation Results ◽  
Unknown Node

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">This paper proposes the localization of unknown nodes based on the centroid iteration algorithm. It uses the range-free localization method and proposes the iteration stopping criterion for centroid iteration algorithm and node localization flow chart according to the location of the unknown nodes based on the plane enclosed by N beacon nodes and those where the received signals of the unknown node are gradually weakened, and finally achieves the accurate localization of unknown communication nodes. Simulation results show that with the proportion of beacon nodes increasing, the relative error of calculation is gradually reduced; when the proportion of beacon nodes remains the same, with the radio range of the node increasing, the relative error of localization gradually decreases. When the radio range is small (R=15m, R=20m), with the proportion of beacon nodes increasing, the relative error of localization increases instead of decreasing. Compared with other three traditional centroid localization algorithms, the algorithm proposed in this paper can achieve the minimum relative localization error at different radio ranges. Under this algorithm, the localization is accurate and highly resistant to RSSI errors. Calculation results show that the optimal radio range R = 30m.</span>

scholarly journals Underwater TDOA Acoustical Location Based on Majorization-Minimization Optimization

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4457
Author(s):  
Shuangshuang Li ◽  
Haixin Sun ◽  
Hamada Esmaiel
Keyword(s):  
Information Matrix ◽  
Initial Point ◽  
Auxiliary Function ◽  
Localization Algorithm ◽  
Underwater Acoustic ◽  
Acoustic Localization ◽  
Mm Algorithm ◽  
Localization Scheme ◽  
Localization Algorithms ◽  
Gradient Based

Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater environment. This paper proposes a TDOA-based localization algorithm for an underwater acoustic sensor network using the maximum-likelihood (ML) ratio criterion. To relax the complexity of the proposed localization complexity, we construct an auxiliary function, and use the majorization-minimization (MM) algorithm to solve it. The proposed localization algorithm proposed in this paper is called a T-MM algorithm. T-MM is applying the MM algorithm to the TDOA acoustic-localization technique. As the MM algorithm iterations are sensitive to the initial points, a gradient-based initial point algorithm is used to set the initial points of the T-MM scheme. The proposed T-MM localization scheme is evaluated based on squared position error bound (SPEB), and through calculation, we get the SPEB expression by the equivalent Fisher information matrix (EFIM). The simulation results show how the proposed T-MM algorithm has better performance and outperforms the state-of-the-art localization algorithms in terms of accuracy and computation complexity even under a high presence of underwater noise.

Evaluation of Localization Algorithms

2009 ◽  
pp. 348-379 ◽  
Author(s):  
Michael Allen ◽  
Sebnem Baydere ◽  
Elena Gaura ◽  
Gurhan Kucuk
Keyword(s):  
Performance Metrics ◽  
Methodological Approach ◽  
Evaluation Criteria ◽  
Simulation Models ◽  
Data Sets ◽  
Localization Algorithm ◽  
Localization Algorithms ◽  
Development Cycle ◽  
Complete Test ◽  
And Performance

This chapter introduces a methodological approach to the evaluation of localization algorithms. The chapter contains a discussion of evaluation criteria and performance metrics followed by statistical/ empirical simulation models and parameters that affect the performance of the algorithms and hence their assessment. Two contrasting localization studies are presented and compared with reference to the evaluation criteria discussed throughout the chapter. The chapter concludes with a localization algorithm development cycle overview: from simulation to real deployment. The authors argue that algorithms should be simulated, emulated (on test beds or with empirical data sets) and subsequently implemented in hardware, in a realistic Wireless Sensor Network (WSN) deployment environment, as a complete test of their performance. It is hypothesised that establishing a common development and evaluation cycle for localization algorithms among researchers will lead to more realistic results and viable comparisons.

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