Signal Strength Measurements Research Articles

A Majorization-Minimization Algorithm for Hybrid TOA-RSS Based Localization in NLOS Environment

This letter investigates the problem of accurate localization of a target node in wireless sensor networks using time of arrival (TOA) and received signal strength (RSS) measurements in an adverse non-line of sight (NLOS) environment. The proposed methodology works without any requirement of the NLOS path identification or the prior knowledge of NLOS bias distribution. A non-linear weighted least squares (NLWLS) problem is formulated through general approximations on the hybrid data model. The formulated NLWLS problem is solved using a computationally efficient majorization-minimization (MM) algorithm in which the NLWLS objective is iteratively minimized via simple update steps. The proposed MM algorithm is guaranteed to converge to a stationary point of the NLWLS objective. Simulation results and computational complexity analysis validate that the proposed MM algorithm attains fast convergence with lower latency. Moreover, the proposed hybrid localization algorithm outperforms the state-of-art methods in terms of estimation accuracy and computational complexity.

Robust UWB Indoor Localization for NLOS Scenes via Learning Spatial-Temporal Features

Ultra-wide band (UWB) localization system suffers from deteriorating performance in complex scenes, especially in non-line-of-sight (NLOS) conditions. In order to improve the accuracy and robustness of the localization system in NLOS environments, we propose an end-to-end deep neural network with both distance and received signal strength (RSS) measurements. On one hand, high-level spatial-temporal features can be learned through the proposed network from both RSS and distance data, which benefits the localization performance. On the other hand, the proposed network is robust to the variance in the number of available anchors, leading to high adaptability to different scenes. Specifically, three modules are designed in the deep network: 1) a module based on convolutional neural network (CNN) is presented to extract the local spatial features from the input data, and the structure of this module lends itself to varying-dimension input. 2) To manage the correlations between consecutive frames, we develop a deep long short-term memory (LSTM) model to extract temporal features and provide a high-level representation for a series of input data. 3) Finally, the fully-connected layers are utilized to estimate the 3D positions of the UWB tag. We conduct extensive experiments in three real-world scenarios to evaluate the proposed deep network. The experimental results indicate that the proposed network can significantly improve the accuracy and robustness of the UWB localization results, especially in NLOS situations.

Accurate indoor positioning system based on modify nearest point technique

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).

A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication

The fifth generation (5G) communication has the potential to achieve ubiquitous positioning when integrated with a global navigation satellite system (GNSS). The device-to-device (D2D) communication, serving as a key technology in the 5G network, provides the possibility of cooperative positioning with high-density property. The mobile users (MUs) collaborate to jointly share the position and measurement information, which can make use of more references for positioning. In this paper, a GNSS/5G integrated three-dimensional positioning scheme based on D2D communication is proposed, where the time of arrival (TOA) and received signal strength (RSS) measurements are jointly utilized in the 5G network. The density spatial clustering of application with noise (DBSCAN) is exploited to reduce the position uncertainty of the cooperative nodes, and the positions of the requesting nodes are obtained simultaneously. The particle filter (PF) algorithm is further conducted to improve the position accuracy of the requesting nodes. Numerical results show that the position deviation of the cooperative nodes can be significantly decreased and that the proposed algorithm performs better than the nonintegrated one. The DBSCAN brings an increase of about 50% in terms of the positioning accuracy compared with GNSS results, and the PF further increases the accuracy about 8%. It is also verified that the algorithm suits the fixed and dynamic condition well.

On the High Fluctuations of Received Signal Strength Measurements With BLE Signals for Contact Tracing and Proximity Detection

This paper presents a measurement-based analysis of the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) signals, under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios, performed in tandem at two universities in Tampere, Finland, and Bucharest, Romania. We adopted the same hardware and methodology for measurements in both places, and paid particular attention to the impact of RSS on various environmental factors, such as LOS and NLOS scenarios and interference in 2.4 GHz band. In addition, we considered the receiver orientation and the different frequencies of BLE advertising channels. We show that snapshot RSS measurements typically have high variability, not easily explainable by classical path-loss models. A snapshot recording is defined here as one continuous recording at fixed device locations in a static setup. Our observations also show that aggregated RSS data (i.e., considering several snapshot measurements together) is more informative from a statistical point of view and more in agreement with current theoretical path-loss models than snapshot measurements. However, in BLE applications such as contact tracing and proximity detection, the receivers typically have access only to snapshot measurements (e.g., taken over a short duration of 10–20 minutes or less), so the accuracy of contact-tracing and proximity detection can be highly affected by RSS instabilities. In addition to presenting the measurement-based BLE RSS analysis in a comprehensive and well-documented format, our paper also emphasizes open challenges when BLE RSS is used for contact tracing, ranging, and positioning applications.

Robust Localization Based on Constrained Total Least Squares in Wireless Sensor Networks

Source localization based on signal strength measurements has become very popular due to its wide applications. This paper focuses on differential received signal strength-based localization with model uncertainties in case of unknown transmit power. The error caused by the measurement noise and the reference power offset and the first-order approximate error generated in the process of DRSS receiving power linearization are merged into the constrained total least square regression matrix equation after linearization. The location optimization problem is formed by minimizing the influence of the errors. Based on the explicit closed form expressions of the first step vector and the second derivative matrix of the optimization problem, an iterative technique based on Newton’s method is proposed. The simulation results show that the root mean square error of the new algorithm is closer to the Cramér–Rao lower bound.

Spatial distribution of frequency modulated signals in Uyo, Nigeria

The kernel of this study is to x-ray the extent of radio coverage of two Frequency Modulated (FM) radio stations (AKBC 90.5MHz and Passion FM 94.5MHz), both located in Uyo, Nigeria. Measurement of signal strengths were carried out across 14 locations using a digital spectrum analyzer. A GPS app was used to take measurements of distance, elevation, longitude and latitude. The collected data was analyzed in the form of scattered plot, contour plots, bar charts, graphs and correlation analysis. For AKBC, good reception was received by users at locations N, C, L, and F. For Passion FM, excellent reception was obtained at locations N, F, A and B. For both stations under investigation, location N had the best reception while location E had poor reception. To establish the effects of Line of Sight (LOS) distance on signal strength, correlation values of – 0.84 and - 0.67 were obtained for AKBC and Passion FM. For signal strength against elevation, 0.71 and 0.55 were the correlation values obtained for AKBC and Passion FM. This shows that an increase in distance from the transmitting antenna led to a decrease in reception while an increase in elevation led to an increase in reception. Existing and intending radio stations in Uyo, are advised to increase the height of their transmitter and transmitting power for better reception and coverage.

Transition Model–driven Unsupervised Localization Framework Based on Crowd-sensed Trajectory Data

The rapid popularization of mobile devices makes it more convenient and cost-efficient to collect synchronized WiFi received signal strength (RSS) and inertial measurement unit sequences by crowdsensing. The transition model has proven to be a promising unsupervised localization approach that captures the transition relationship between the change of RSS signal space and the change of physical space, alleviating the need of extra knowledge for creating radio map. However, it faces two essential challenges in real-world deployments. First, model coverage affects its locating performance, because a specific transition model only represents its local space. Second, the instability of RSS leads to a conflicting relationship between changes of two spaces because of the complex environment and the heterogeneous type of devices. To address these challenges, we propose Lightgbm-CTMM, a novel unsupervised localization framework. First, a clustering method is adopted to capture the expected relationship to ensure robust coverage. Second, direction filter is employed to guarantee that the change in signal space corresponds to the change in physical space. The feasibility and effectiveness of Lightgbm-CTMM are evaluated by extensive experiments, and the locating performance of Lightgbm-CTMM is better than that of conventional approaches. Moreover, Lightgbm-CTMM reduces the work on quality assessment of trajectories.

Comparison of semi-airborne transient electromagnetic data from double-line and single-line grounded-wire sources

The semi-airborne transient electromagnetic method (TEM) involves a ground-based electrical source and an airborne magnetic field component receiver and is an effective method for exploration in complex terrains. However, the ground transmitter selection for these methods is typically challenging. In the present study, we examine the magnetic field responses of double-line and single-line source devices. Response attenuation and plane distribution are utilized to analyze and compare the characteristics of the two sources. In the double-line source, the inline and cross-line components of magnetic field are axisymmetric, with distribution characteristics of six-petaled flowers. The vertical component exhibits a four-quadrant distribution comparable to that of the x component in the single-line source; the components are generated separately or jointly by current-carrying wires and grounding ends. Although the double-line source is generally characterized by weaker response intensities than the single-line source, its components (especially the cross-line and vertical components) exhibit higher resolutions for low- and high-resistivity targets. Thus, with adequate measured signal strength, the double-line source is preferable for semi-airborne TEM involving an electrical source.

Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation

Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance. Recognizing that this data collection may be at odds with growing awareness of privacy concerns, we consider obfuscating such data before the data leaves the mobile device. The goal is to increase privacy such that it is difficult to recover sensitive features from the obfuscated data (e.g. user ids and user whereabouts), while still allowing network providers to use the data for improving network services (i.e. create accurate signal maps). To examine this privacy-utility tradeoff, we identify privacy and utility metrics and threat models suited to signal strength measurements. We then obfuscate the measurements using several preeminent techniques, spanning differential privacy, generative adversarial privacy, and information-theoretic privacy techniques, in order to benchmark a variety of promising obfuscation approaches and provide guidance to real-world engineers who are tasked to build signal maps that protect privacy without hurting utility. Our evaluation results, based on multiple, diverse, real-world signal map datasets, demonstrate the feasibility of concurrently achieving adequate privacy and utility, with obfuscation strategies which use the structure and intended use of datasets in their design, and target average-case, rather than worst-case, guarantees.

Robotic Ad-hoc Networks Connectivity Maintenance based on RF Signal Strength Mapping

Network connectivity preservation is one of the substantial factors in achieving efficient mobile robot teams' maneuverability. We present a connectivity maintenance method for a robot team's communication. The proposed approach augments the Radio Frequency Mapping Recognition (RFMR) method and the signal strength gradient decent approach for an overall goal to create a Proactive Motion Control Algorithm (PMCA). The PMCA algorithm controls and helps strengthen mobile communicating robots' connectivity in the existent Radio Frequency (RF) obstacles. The RFMR method takes advantage of Hidden Markov Models (HMMs) results, which assist in learning electromagnetic environments depending on measurements of RF signal strength. The classification results of HMM lead the robots to resolve whether to continue the current trajectory for avoiding the obstacle shadow or move back to desirable robust Signal Strength (SS) positions. In both cases, the robot will run the gradient approach to determine the signal change trend and drive the robot toward the strong SS direction for maintaining link connectivity. The PMCA, depending on the results of RFMR and gradient approaches, promises to preserve robots' motion control and link connectivity maintenance.

Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of the unknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though the optimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linear classifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability.

Designing Multi-Task Convolutional Variational Autoencoder for Radio Tomographic Imaging

Radio tomographic imaging (RTI) emerges to model the environment and detect the passive targets by a wireless network. In this work, the received signal strength (RSS) measurements are collected from an uncalibrated network, and a multi-task convolutional variational autoencoder model is proposed to realize RTI. The presented model is trained end-to-end to denoise the RSS measurements, reconstruct the static tomographic images, estimate the parameters of the wireless network, and classify the measurement noise level, simultaneously. The multi-task variational learning strategy is able to improve the generalization of the model. Numerical experiments demonstrate the efficacy of our RTI method.

Improving Radio Tomographic Imaging Accuracy by Attention Augmented Optimization Technique

Radio tomographic imaging (RTI) has become a popular approach to reconstruct spatial loss fields (SLFs) in an area covered by a wireless network based on received signal strength (RSS) measurements. SLF images quantify the attenuation rate of the radio-frequency waves at each location in the network. The attenuation for the propagation path can be modeled as the 2-dimensional integral of SLF scaled by a weight function, which is the foundation of RTI techniques and makes the SLF reconstruction possible. In recent years, many methods, including machine-learning-based schemes, have been proposed to achieve more accurate SLF estimates. In this letter, we develop an attention neural network-augmented optimization SLF estimation scheme by taking advantage of deep learning and the traditional RTI technique. Our proposed method achieves the best reconstruction performance among the existing approaches.

Distributed anchor activation schemes for RSS localisation

The activation of a well-chosen set of anchors for localisation is vital for preserving network energy and satisfying localisation error requirements. Herein, we propose two distributed anchor activation schemes for agent localisation employing received signal strength (RSS) measurements. The proposed schemes incorporate anchor energies, localisation error requirement and submodular behaviour of anchor activation on the localisation error. In the first scheme, a distributed probabilistic anchor activation scheme is proposed. An anchor is activated according to an evolving activation probability that is locally calculated. In the second scheme, activation is executed over a number of rounds with each round having its own activation threshold. A proposed dynamic model for an anchor's inclination to activate is compared against the round's threshold and an activation decision is made. Simulation results show the efficacy of the proposed schemes in satisfying the error requirement as well as decreasing energy consumption in the network and thereby extending its life.

Preliminary performance evaluation and verification of digital terrestrial television signal propagation

This article presents the computer simulation and field test measurement results on Channel 29 for the preliminary performance evaluation and verification of the newly-installed Lesotho digital terrestrial television network based on DVB-T2 standard following the guidelines and techniques specified by the ITU-R BT.2035-2. It evaluates, at predetermined outdoor locations for fixed and mobile reception, parameters such as received signal strength, signal quality, bit-error rate (BER) and threshold-of-visibility (ToV) together with TV signal decoding (observation of screen artefacts) for quasi error-free reception. The results indicate that at over 97% of the test sites/points at the university town of Roma, the main Berea Plateau transmitter from the capital city (Maseru) broadcasts digital television service with enough signal level and quality to be properly decoded. The measured signal strength threshold ranges above -50 dBm for good reception, -64 dBm to -50 dBm for acceptable reception and -69 dBm to -64 dBm for poor reception. With the noise floor at about -73 dBm, the minimum required C/N of around 23 dB for good reception and about 4 dB for ToV have been recorded. The relative values of minimum required respective signal strength and signal quality for ToV obtained from the set-top box are 33% and 18% for stationary reception, while they give 37% and 20% for mobile reception.

Detection of spin coherence in cold atoms via Faraday rotation fluctuations

We report noninvasive detection of spin coherence in a collection of Raman-driven cold atoms using dispersive Faraday rotation fluctuation measurements, which opens possibilities of probing spin correlations in quantum gases and other similar systems. We demonstrate five orders of magnitude enhancement of the measured signal strength as compared to traditional spin noise spectroscopy with thermal atoms in equilibrium. Our observations are in good agreement with the comprehensive theoretical modeling of the driven atoms at various temperatures. The extracted spin relaxation rate of cold rubidium atoms with atom number density $\ensuremath{\sim}10{\phantom{\rule{0.16em}{0ex}}}^{9}/{\mathrm{cm}}^{3}$ is of the order of $3\phantom{\rule{0.16em}{0ex}}\ifmmode\times\else\texttimes\fi{}\phantom{\rule{0.16em}{0ex}}10{\phantom{\rule{0.16em}{0ex}}}^{3}\phantom{\rule{4pt}{0ex}}{\mathrm{s}}^{\ensuremath{-}1}$ at 150 $\ensuremath{\mu}\mathrm{K}$, two orders of magnitude less than $3\phantom{\rule{0.16em}{0ex}}\ifmmode\times\else\texttimes\fi{}\phantom{\rule{0.16em}{0ex}}10{\phantom{\rule{0.16em}{0ex}}}^{5}\phantom{\rule{4pt}{0ex}}{\mathrm{s}}^{\ensuremath{-}1}$ of that of a thermal atomic vapor with atom number density $\ensuremath{\sim}10{\phantom{\rule{0.16em}{0ex}}}^{12}/{\mathrm{cm}}^{3}$ at 373 K.

An Efficient Radio Map Learning Scheme Based on Kernel Density Function

Training an accurate and up-to-date radio map has always been a primary concern for implementing a WiFi fingerprint-based localization system. This paper presents a novel radio map learning scheme for online learning a set of kernel density functions, which function like a traditional radio map in WiFi fingerprint-based localization systems. To be specific, each kernel density function corresponds to one specific access point (AP) and predicts the probability of having a received signal strength (RSS) measurement from this AP at any position. In the offline phase, a set of coarse-grained kernel density functions are initially established by the Multivariate Kernel Density Estimation (MKDE) through a fast site survey. In the online phase, these kernel density functions are recursively refined through Incremental-MKDE (IMKDE) using freshly crowdsourced fingerprints, which essentially fuses the historical features in the previous kernel density functions and up-to-date features in the current fingerprints. A compression technique is applied to improve the computation and storage efficiency. In addition, pedestrian dead reckoning (PDR) is leveraged to calibrate the location labels of crowdsourced fingerprints. Extensive experiments are carried out in a real scenario of nearly 1000 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^2$</tex-math></inline-formula> for five months, and a comparison made between the IMKDE based method and several existing popular solutions confirms the superiority of the proposed IMKDE in terms of both computational complexity and localization accuracy.

A Survey on Techniques in the Circular Formation of Multi-Agent Systems

Distributed control solutions to the multi-agent systems in terms of coordination, formation, and consensus problems are extensively studied in the literature. The circular formation control of multi-agent systems becomes one of the most challenging and active research topics in distributed cooperative control. The research potential of circular formation control has grown especially in satellite formation and control. The circular formation control aims to drive a group of agents to move on a circular trajectory around a center with spacing adjustment to avoid the collision. This paper covers the fundamental research in consensus and formation control and the latest developments in the applications of formation control especially in circular formation control and stability analysis. Literature related to various control streams to achieve circular formation is discussed. Algebraic graph theory is used as a building block in problem definition, problem formulation, and solutions. A review of bearing, range, and received signal strength measurements with respect to circular formation control is presented. Leader following, behavior-based, artificial potential function, virtual structure, and cyclic pursuit are the methods discussed most in this context. The stabilization of the collective circular motion with the unicycles approach is also discussed. Further results on circular formation, several applications, and future research directions are presented.

Role of geometric configurations in optimizing the LIBS signal enhancement

The quest for improving the limits of trace elemental detection and reproducibility in Laser-Induced Breakdown Spectroscopy (LIBS) resulted in the development of novel methodologies for sample excitation and plasma collection. Efficient plasma collection and coupling to the detector is of utmost importance in LIBS studies for meaningful and logical data interpretations. Unfortunately, limited attention is paid to such critical aspects in comparison with the plethora of publications in advanced data analysis. In view of this, we probe the role of the different optical schemes on the collected plasma signal strength from three standard samples (Cu, Cd, Pb) while retaining all other experimental conditions such as laser energy, detector gain, gate width, and gate delay same. In addition, the role of coupling modalities on the plasma signal strength, an area not studied in depth, is investigated and found that the direct coupling scheme always provides better signal strength and repeatability. Moreover, the role of spectrographs in the measured signal strength is also investigated in detail for all the said standard samples. A comparative study elucidates that single shot experiments with low excitation pulse energies of the order of a few mJ are sufficient as compared to multi-short pulse laser excitation to provide the required limit of detection of the samples of interest which in turn makes the technique practically non-destructive. The efficacy of the optimized system was manifested by investigating idealistic and real-life samples viz. standard meals and teeth to differentiate between the normal and carious states. Our results reveal that the developed strategies may be useful in designing a robust system for fast ultra-trace elemental detection.