Existing Localization Techniques Research Articles

Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect.

In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to its high signal strength, which can provide localization information. In this paper, we formulate the sensing matrix optimization problem in compressed sensing for device-free localization scenarios based on multiple reflections. To solve this problem, we model it as a constrained combinatorial optimization problem and propose a hybrid meta-heuristic algorithm. First, smart reflection surfaces and virtual node models are used to construct the desired communication links. Second, we iteratively improve the properties of the measurement matrix by using K-means clustering to obtain reasonable thresholds, and use a meta-heuristic algorithm to optimize the sensing matrix. Finally, the simulation results show that the proposed method efficiently optimizes the sensing matrix and achieves fast and high-precision localization while conserving communication resources.

A UAV Localization System Based on Double UWB Tags and IMU for Landing Platform

The successful landing of UAVs on unmanned vehicle landing platforms is critical for resolving the problem of insufficient range of UAVs in practical missions. Existing localization techniques for autonomous UAV landing are primarily vision-based, but this method is limited due to reliance on external lighting conditions. In this paper, we propose a localization system for UAV landing on a mobile platform, which can be conveniently deployed on an unmanned vehicle landing platform. Four UWB anchors were deployed on the 0.8m×1.4m rectangular unmanned vehicle landing platform. The spacing of the two tags installed on the UAV and the ranging information were used as constraints, and the linear least squares method was used to calculate the iterative initial coordinates of the gradient descent method to solve for the positions of the two UWB tags and fuse them with the IMU data to provide accurate coordinate for the safe landing of the UAV. By comparing the experiments with the linear least squares method in five experiments, it is demonstrated that the average RMSE of the localization system proposed in this paper is reduced by 61%.

Mobile Anchor Assisted Effective Node Localization Algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) is collection of thousands of randomly, spatially distributed low-cost Sensor Nodes (SN) deployed normally in harsh environments. These deployed SNs collects data and delivers to Base Station (BS) located in a remote region. In a sensor network it is necessary to know about the location of sensor node and data collected by that node as it has a strong impact on overall performance of the WSN. An algorithm to transfer data using energy efficient mobile anchor based localization mechanism is presented in this paper. Firstly, the distance between anchor node and unknown node is determined using Received Signal Strength (RSS) and then using Trilateration method location of the unknown node is identified. Proposed algorithm aims to decrease the consumable energy that improves life of the network. Simulation results are compared with the existing localization technique to evaluate performance of the proposed algorithm in terms of energy consumption, packet delivery ratio and control overheads. The proposed scheme performs better than the existing Mobile Anchor Assisted Localization algorithm based on Regular Hexagon (MAALRH) for WSNs

Group teaching optimization with improved Chan-Taylor algorithm for 3D indoor localization

In recent years, indoor localization has become a hot research topic and several researchers have paid attention to design accurate 3-Dimensional (3D) indoor localization techniques. Existing localization techniques cannot be sufficient for 3D indoor localization due to the existence of interferences and the need for high precision. The most widely used option for outdoor localization is known as the global positioning system (GPS), and its precision in location is often within a 10-meter margin of error. Problems arise in the "final mile" of the localization field due to the complicated impediments that buildings provide, which encourages a momentum of indoor localization. Traditional methods of indoor localization are either dependent on range or fingerprinting, which means that the predeployment process takes a significant amount of time and work to complete. The non-line of sight propagation due to the building shielding and background interferences results in high error on the application of Ultra-Wideband technologies to indoor localization. For resolving this issue, this paper presents a novel group teaching optimization algorithm with improved Chan-Taylor algorithm (GTOA-ICTA) for 3D indoor localization. The GTO-ICTA technique initially employs the GTOA for transforming the target problem into the target of variations among the determined and original positions. Besides, the ICTA technique is employed for computing the accurate positions of the targets. The integration of GTOA and ICTA techniques helps to properly achieve maximum localization performance. For validating the enhanced performance of the presented GTOA-ICTA technique, a set of simulations were performed and the results are examined interms of different aspects. A comprehensive results analysis of the proposed GTOA-ICT technique takes place in positioning error with the minimum error of 0.1245, and the proposed method has reached increased results with the maximum error of 0.9660. Moreover the GTOA-ICT has obtained minimum average error of 0.6432, least Standard deviation of 0.4387. The resultant experimental outcomes highlighted the enhanced performance of the GTOA-ICTA techniques over the recent state of art techniques interms of different measures.

GAUSS-NEWTON MULTILATERATION LOCALIZATION ALGORITHM IN LARGE-SCALE WIRELESS SENSOR NETWORKS FOR IoT APPLICATIONS

The location information of sensor nodes plays an important role in critical applications like health monitoring, fire detection, and intruder detection. Installing global positioning system (GPS) modules with the sensor node hardware is not a cost-effective solution for knowing the location coordinates. This has lead to rigorous research in defining nascent localization techniques for wireless sensor networks. But, the existing localization techniques use more number of anchor nodes to compute the location coordinates of sensor nodes, and the network deployment becomes costly. This article presents a low complex, range-based localization algorithm called gauss-newton multilateration that uses received signal strength indicator (RSSI) values of the anchor nodes' signals received at the target nodes. The proposed algorithm uses only four static anchor nodes, which are deployed at the corners of the network terrain to locate the sensor nodes with localization accuracy of 90.21% and increased up to 98.59%. Based on the results obtained, the proposed algorithm provides higher localization accuracy, and it is well suited for locating sensor nodes with high accuracy in large scale wireless sensor networks.

Multidirectional Differential RSS Technique for Indoor Vehicle Navigation

While indoor vehicle navigation is an essential component in large-scale parking garages of major cities, technical limitations and challenging propagation environments considerably affect the accuracy of existing localization techniques. We propose practical and robust proximity and direction detection schemes of vehicles for indoor navigation services using low-cost beacons. Specifically, the proposed solution allows a handheld mobile device within a moving vehicle to autonomously detect its approximate position and moving direction by only observing received signal strength (RSS) values of beacon signals. The proposed approach essentially exploits the differential RSS technique over perpendicularly oriented directional beams to reduce the impact of uncontrolled factors, including the vehicle and the mobile device. A low-cost multidirectional beacon prototype is developed using Bluetooth technology. The localization performance is evaluated using 96 beacons in an underground parking garage within an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$394.8 \mathrm {m} \times 304.3 \mathrm {m}$ </tex-math></inline-formula> . Experimental results show that the 90th percentiles of the average proximity and direction detection errors are 0.8 and 1.64 m, respectively. Furthermore, our proposed scheme provides robust proximity and direction detection performance with various vehicles and mobile devices’ orientations.

A PERFORMANCE ANALYSIS ON DRONE LOCO POSITIONING SYSTEM FOR TWO-WAY RANGING PROTOCOL

Localization is vital in UAV operation as it monitors the position of each drone in a workspace. Existing localization techniques such as GPS are limited for outdoor implementations and cannot be implemented inside closed spaces or GPS denied areas. To address this concern localization techniques, such as vision systems and radio systems, are developed. The drawback of vision systems is the cost of implementation as the system usually requires multiple cameras strategically positioned around the experimental space to monitor the aerial drone’s position and orientation. Radio localization, on the other hand, is a cheaper alternative for indoor localization as it requires only a set of anchor and tags that communicates through a certain radio frequency; however, experimental setups and materials on this localization technique is limited at this time. This paper offers an analysis of the performance of the loco positioning system, a form of radio localization, through varying configurations for swarm drone applications. The Loco Positioning System possesses two protocols; and this paper focuses on the Two-Way Ranging protocol. The study presents different setup configurations governed by 2 parameters; number of anchors used, and the distance set between anchors, and their corresponding performances. Data showed that an increase in anchor count from 3 to 6 decreases error from 25.96% to 8.45%, and that decreasing the distance between anchors 0.6 m to 1 m would give a minimal increase in error. Users may use these performance reports to determine their ideal setup based on the mentioned parameters.

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

SummaryA great interest among researchers to establish Underwater Wireless Sensor Networks (UWSN) with mobile nodes have been created in recent past because of numerous potential applications in aquatic science. In comparison of radio communication, acoustic communication has lower bandwidth and larger propagation delay. This enacts additional restrictions on any localization algorithm. Existing range‐based approaches involve increased localization latency and error even with large deployment of anchor nodes. Some of the optimized localization techniques involve higher energy consumption and cost. In order to solve these issues, a hybrid optimized localization technique is proposed that can accurately localize mobile nodes in UWSN. In this algorithm, a fitness function is concluded based on the number of hops from the anchor node, Time of Arrival (ToA) distance estimation error and transmission delay. In comparison of existing localization techniques, proposed algorithm helps in reducing the energy consumption, localization error, localization delay and localization cost and experiment results proved the same.

An accurate target tracking method in wireless sensor networks

&lt;span&gt;Localization is a challenging research issue in various sectors of activity, particularly in dynamic indoor environment with high perturbation. Many existing localization techniques in wireless sensor networks are not efficient because of many constraints such as the high cost, the memory and energy limitation and the environmental noise effects. Thus, the development of novel methods of localization has become a great concern for the wireless sensor networks. In this paper, we propose a tracking method that combines regression tree and Kalman smoother filtering. Previously, regression tree has been suggested for static positioning by means of received signal strength indicator measurements. In this work, we employ this strategy to solve the mapping relation between these measurements and the target position by means of an optimized propagation model. Moreover, the predicted position considered as the observed information is introduced to the Kalman smoother algorithm, to have more precise state of the moving target. The proposed algorithm has been assessed and compared to other existing methods using real measurements of the received power by the moving target in an indoor environment. The evaluation shows that our solution outperforms other methods regarding localization accuracy.&lt;/span&gt;

An optimized location service for the fifth generation VANETs inspired by traffic lights.

Vehicular ad-hoc networks (VANETs) address a steadily expanding demand, particularly for public emergency applications. Real-time localization of destination vehicles is important for determining the route to deliver messages. Existing location administration services in VANETs are classified as flooding-based, flat-based, and geographic-based location services. Existing localization techniques suffer from network disconnection and overloading because of 5G VANET topology changes. 5G VANETs have low delay and support time-sensitive applications. A traffic light-inspired location service (TLILS) is proposed to manage localization inspired by traffic lights. The proposed optimized localization service uses roadside units (RSUs) as location servers. RSUs with the maximum traffic weight metrics were chosen. Traffic weight metrics are based on speed of vehicles, connection time and density of neighboring vehicles. The proposed TLILS outperforms both Name-ID Hybrid Routing (NIHR) and Zoom-Out Geographic Location Service (ZGLS) for packet delivery ratio (PDR) and delay. TLILSs guarantee the highest PDR (0.96) and the shortest end-to-end delay (0.001 s) over NIHR and ZGLS.

A Big Data Analysis Framework for Localization in Cloud based Sensor and Robot Networks

Internet of Things (IoT) is constructed from billions of sensor devices connected over Internet. Wireless sensor networks (WSNs) are very important communication technologies of IoT for providing large scale data from the environment. Since the data collected grows exponentially, the design of cloud based big data analysis techniques is crucial. Localization in sensor networks is finding the location of a node based on the reference nodes’s coordinates. In many applications such as target tracking and military surveillance, providing localization is necessary. Manually entering the locations of nodes during the deployment phase is not applicable especially for large scale sensor networks. Besides, integrating a GPS receiver to each sensor node is a very costly solution and may not be affordable for large scale networks. Since sensor nodes are mostly battery-powered, design of an energy-efficient localization method is highly desirable to prolong the lifetime of the network. Existing localization techniques may require many message transmission which causes high energy consumption. To tackle with this problem, we propose an energy-efficient localization framework in this paper. A mobile robot is traversed along the sensing area and communicate with sensor nodes to localize these nodes. Different than the previous approaches, our proposed approach requires only 3 messages per node. Besides, most of the execution needed for the localization is not carried by ordinary nodes. We simulate our proposed approach in ns2 simulator. We measure the localization quality and energy consumptions of our proposed approach with its counterparts. Also, we measure the localization quality and energy consumption against varying node counts and degrees. From extensive simulation results, we obtain that the localization qualities of our proposed approach is significantly better than its competitors. Besides, the energy consumption of our proposed algorithm is just 0.06 J per node and far more better than the distributed algorithm. Conclusively, our proposed framework is a significant candidate for IoT and big data applications requiring energy-efficient localization.

SWIM: Speed-Aware WiFi-Based Passive Indoor Localization for Mobile Ship Environment

Accurate and pervasive device-free indoor localization with meter-level resolution is critical for large cruise and passenger ships due to safety-critical rescue and evacuation requirements when accidents occur. However, existing localization techniques would severely suffer on ships because of their unique mobility characteristics. In this paper, we take the first attempt to build a ubiquitous passive localization system using WiFi fingerprints for the mobile ship environment. By conducting extensive experiments and measurements during several cruise trips, we identified a major influence factor on the fingerprints in the mobile environment: varying the ship speeds may significantly change the patterns of fingerprints at runtime. Since it may be too expensive to identify the fingerprints associated with different speeds, we propose an efficient localization method, namely SWIM, which calibrates the fingerprints from only a single-speed scenario to multiple-speed scenarios using a signal reconstruction analysis. SWIM is designed to learn the predictive fingerprint variation introduced by environmental speed changes and reconstruct the original fingerprints to adapt to the runtime speed scenarios. We have implemented and extensively evaluated SWIM on actual cruise ships. Experimental results demonstrate that SWIM improves localization accuracy from 63.2 to 82.9 percent, while reducing the overall system deployment cost by 87 percent.

Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature.

An overview of massive MIMO localization techniques in wireless cellular networks: Recent advances and outlook

The massive multiple-input-multiple-output (mMIMO) antenna systems are well known for their capability to achieve high spectral efficiency in wireless communication systems thanks to millimeter waves (mmWaves) which allow a large number of antennas to be deployed at the base stations (BSs). Aside from communication-based services, mMIMO BSs are presently being exploited for location estimation of user equipment due to their high angular resolution, low-cost implementation, and excellent performance in the indoor and clutter urban environments where line-of-sight may not be available. Although various mMIMO localization solutions have been proposed, there are still pressing issues yet to be resolved. To this end, this article first provides an overview of recent and relevant state-of-the-art survey papers on localization. Further to this, we provide various foundational background concepts based on the existing localization techniques applicable to mMIMO localization systems. Furthermore, we discuss various methods under each technique and we also identify some critical factors to be considered in a practical radio environment. Based on these techniques, we provide a comprehensive review of recent works on mMIMO localization. Finally, we suggest key research directions to be addressed in the future and also we discuss key enabling technologies that will enhance the performance of localization systems in 6G communication networks.

Effects of aberration on super-resolution ultrasound imaging using microbubbles

Visualizating vasculature beyond the diffraction limit can be achieved using ultrasound super-resolution (USR). Typically, ultrasound (US) scanners model the target medium as homogeneous, assuming a constant speed-of-sound for time-of-flight based calculations. However, variations in US propagation velocity caused by varying tissue layers affect beamforming operations. This aberration is likely to have considerable effect on USR accuracy when imaging at depth. Here we investigate the effect of aberration on USR localization accuracy. Wave propagation through different tissue media was modelled from a linear transducer using k-Wave, and the resulting pulse-echo data from a point scatterer located at 45mm depth was beamformed. Media included a control homogeneous propagation medium, and aberrating media typical of liver imaging. Aberration effects were estimated using RMS arrival-time fluctuations (ATF) and energy-level fluctuations (ELF) at the scatterer location. Signals were extracted and localized with existing localization techniques. Results indicated USR localisation accuracy decreased with increasing aberration. Axial localisation errors reached 836.6 mm for an ATF 30.9 ns. Furthermore, errors increased with decreasing frequency from 4 to 3 MHz. The scale of these errors relative to micro-vascular structures of interest suggests that aberration will have considerable impact on USR performance and requires attention to ensure its success.

DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization

In this paper, we proposed an improved DV-Hop localization using runner-root algorithm to overcome the existing disadvantage of traditional DV-Hop. In proposed algorithm, we have introduced a correction factor in order to modify the hop size of the anchor nodes. The proposed algorithm lessens the communication between unknown and anchor nodes by calculating hop size of all anchors at unknown nodes. To show the applicability of proposed algorithm in anisotropic network, radio irregularity model is also considered. Simulations have been carried out on MATLAB, and comparison is made for our proposed algorithm with traditional DV-Hop, genetic algorithm-based DV-Hop and particle swarm optimization-based DV-Hop. Simulation results confirm that our proposed localization algorithm minimizes the localization error, localization error variance and computational time in comparison with existing localization techniques.

Solution of network localization problem with noisy distances and its convergence

The network localization problem with convex and non-convex distance constraints may be modeled as a nonlinear optimization problem. In the existing localization techniques, the non-convex distance constraints are either removed or relaxed into convex constraints to use the convex optimization methods like semi-definite programming, least square approximation, etc. for solving the problem. We use the nonlinear Lagrangian technique for non-convex optimization to convert the localization problem with noisy distance measurements to a root finding problem of a single variable continuous function without any modification of these constraints. This problem is then solved using an iterative method. In this iterative method, the computation of the functional values involves a finite mini-max problem. We use smoothing gradient method to compute the functional value. The proposed iterative method converges to a solution of the network localization problem with a desired label of accuracy in real time.

A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines

Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures.

Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Diverse fault types, fast reclosures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a convolutional neural network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN-based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units placement strategy are proposed and validated against other methods. A significant aspect of our methodology is that under very low observability ( ${7\%}$ of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68-bus power systems under varying uncertain conditions, system observability, and measurement quality.

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

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.