Received Signal Strength Localization Research Articles

IRS-Aided Received Signal Strength Localization Using a Wireless Sensor Network

IRS-Aided Received Signal Strength Localization Using a Wireless Sensor Network

An extreme value based algorithm for improving the accuracy of WiFi localization

More mobile devices can obtain WiFi Received Signal Strength (RSS) for indoor positioning, so selecting reasonable Access Points (APs) for rapid positioning is important. Furthermore, RSS is vulnerable to diverse interference, making the positioning accuracy decrease. To solve the above mentioned issues, two novel algorithms, AP selection and positioning algorithms, are proposed, in this paper, which both use the location distance between RPs to construct a circle and treat the RSS from each AP individually. RPs in the same circle are seen as having similar RSS. 1) For each received signal, the proposed selection algorithm leverages the RSS extreme values (maximum and minimum values) of circles to find out RPs which may surround Test Point (TP), then it uses the degree of RSS difference and location concentration to evaluate the importance of each AP. 2) For the positioning algorithm, it uses the extreme values to find the useful APs and determines the weight of RPs. Extensive experiments are conducted on simulation and two real-world sites, results show the effectiveness of the proposed algorithms.

Localization of Partial Discharge Using All-Fiber System Based on Crystal Fluorescent Fiber

Partial discharge (PD) is a common defect to determine the quality of high-voltage power equipment insulation systems. Localization of PD is very important for equipment maintenance and risk assessment. A localization algorithm of received signal strength (RSS) using demodulating PD optical signal is proposed. This is based on the optical effects caused by PD, which influence the optical signal that is transmitted through an optical fiber sensor placed inside the equipment. Compared with plastic fluorescent fiber, SiO2 cladding YAP:Ce crystal core fluorescent fiber (SYCF) with high luminous efficiency, low loss, and strong anti-electromagnetic interference capability is selected as the sensing unit to detect the received optical signal strength of PD, through the all-fiber optic sensing system to achieve the PD location in 3-D space. Based on the optical RSS localization method, the range of localization deviation of PD under 37-kV applied voltage is measured and studied. The average localization deviation and the error are 6.9 cm and 7.4%, respectively. Comparing the optical signal location method based on RSS with other methods of PD localization, the advantages and accuracy of the optical location method proposed in this article are outstanding. Therefore, the all-fiber system with SYCF via optical signal has a wide application prospect in the spatial localization of PD.

Semidefinite Programming for Wireless Cooperative Localization Using Biased RSS Measurements

Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.

RSS Localization Using an Optimized Fusion of Two Deep Neural Networks

The problem of received signal strength (RSS) source localization in wireless sensor networks has raised attention in recent years due to its low complexity. However, the performance of the RSS localization algorithms is degraded due to malicious phenomena such as multipath and fading. Hence, machine learning and especially deep neural networks (DNNs) are applied to solve this problem. So far, convolutional the neural network (CNN), the recurrent neural network (RNN), and multilayer perceptron (MLP) are used to this end. In this letter, neural networks such as MLP, CNN, and RNN are suggested to be combined pairwise. In this regard, we propose to use a fusion of two general neural network localization estimators. To design the combination coefficients, a heuristic approach based on the errors of the training step of two neural networks are suggested. Moreover, two optimized approaches for selecting the combination coefficients are provided based on mean and variances and also based on the mean square error of the combined estimator. The simulation results show improvement of the localization accuracy by fusion of two neural networks.

An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks.

This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small N. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.

Source localization in inhomogeneous underwater medium using sensor arrays: Received signal strength approach

Source localization is one of the major challenges of the underwater acoustic sensor network (UASN). The received signal strength (RSS) and the time of flight (TOF) are the main localization approaches. The requirement of the TOF method is the synchronization of sensors, however, synchronization is a challenging issue in an underwater environment and the RSS approach can be considered as an appropriate choice to overcome it. In this paper, we aim to improve the performance of the RSS localization approach by applying a network of acoustic sensor arrays in an inhomogeneous underwater medium. Considering the inhomogeneity as one of the basic challenges in underwater beamforming, at first we analyze the problem of wave propagation in inhomogeneous underwater medium and derive a new steering vector for both far- and near-field scenarios. Then, we develop an Array-RSS underwater narrowband source localization method based on an iterative algorithm. Several computer simulations are accomplished for evaluating the performance of our proposed method. Simulation results demonstrate the considerable effect of using a sensor array in the performance improvement of the RSS localization, such that under a particular signal bandwidth the performance of the Array-RSS method is comparable with that of the TOF at high SNRs.

Beyond RSS: A PRR and SNR Aided Localization System for Transceiver-free Target in Sparse Wireless Networks

Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.

CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

Wireless local area network (WLAN) fingerprint-based localization has become the most attractive and popular approach for indoor localization. However, the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints. The Semi-supervised extreme learning machine (SELM) performs well in reducing calibration effort. Traditional SELM methods only use Received signal strength (RSS) information to construct the neighbor graph and ignores location information, which helps recognizing prior information for manifold alignments. We propose Composite SELM (CSELM) method by using both RSS signals and location information to construct composite graph. Besides, the issue of unlabeled RSS data quality has not been solved. We propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation (CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph, which considers location information in the semi-supervised extreme learning machine. Experimental results show that the CSELM-QE could construct a precise localization model, reduce the calibration effort for radio map construction and improve localization accuracy. Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.

An Indoor Localization and Tracking System Using Successive Weighted RSS Projection

This letter proposes a novel successive weighted received signal strength (RSS) indoor localization and tracking system that projects previous time instance estimated mobile device (MD) position to provide projected RSS values. Such RSS projection increases the number of available RSS from Nm to Nm + N AP , where N AP is the total number of access points and Nm is the number of RSS values measured by MD, ranging from 0 to N AP . Our proposed system thus resolves the issues associated with insufficient or no RSS values received by MD. Inertial navigation system (INS) is merged with RSS localization system to provide a weighted fusion of projected and measured RSS values. The weighting factors are derived based on the INS and RSS localization accuracy where the former is initially accurate but deteriorates with time and the latter is time-independent but environment-dependent. The proposed system was tested in indoor environments and outperformed other existing localization systems such as RSS and INS fusion using extended Kalman filter and non-line-of-sight (NLOS) selection scheme, especially in heavy multipath environment, by 42% and 75%, respectively.

Research on RSS Data Optimization and DFL Localization for Non-Empty Environments.

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments.

A Novel Hybrid RSS and TOA Positioning Algorithm for Multi-Objective Cooperative Wireless Sensor Networks

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

Performance analysis and path planning for UAVs swarms based on RSS measurements

In the localization estimation system, it is well known that the sensor-emitter geometry can seriously impact the accuracy of the location estimate. In this paper, we analyze the optimal deployment for received signal strength (RSS) localization with the measurement noise is set to be distance dependent. First, the Cramer–Rao low bound (CRLB) with distance-dependent noise in RSS localization is calculated and chosen to be the optimality criterion. The optimal deployment is analyzed via angle and distance criterion, respectively. Then, the analytic solutions to the optimal deployment are derived in both static and movable target scenarios. Finally, we extend our work to the path planning problem with constraints and interior penalty method is applied to settle the constrained nonlinear optimization problem. The simulation results show that the path optimization verifies the accuracy of the analytical findings.

Characterization of Received Signal Strength Perturbations Using Allan Variance

The received signal strength (RSS) of wireless signals conveys important information that has been widely used in wireless communications, localization, and tracking. Traditional RSS-based research and applications model the RSS signal using a deterministic component plus a white noise term. This paper investigates the assumption of white noise to have a further understanding of the RSS signal and proposes a methodology based on the Allan variance (AVAR) to characterize it. Using AVAR, we model the RSS unknown perturbations as correlated random terms. These terms can account for both colored noise or other effects such as shadowing or small-scale fading. Our results confirm that AVAR can be used to obtain a flexible model of the RSS perturbations, as expressed by colored noise components . The study is complemented by introducing two straightforward applications of the proposed methodology: the modeling and simulation of RSS noise using Wiener processes, and RSS localization using the extended Kalman filter.

Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model–based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.

A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network

Wireless Sensor Network is deployed in many fields including military operations, mechanical applications, human services, smart homes, etc. However, deploying WSN encounters many challenges. One of the challenges is localizing the node position, especially mobile targets in critical situations. In this paper, the authors compare two types from range-free localization algorithms and one type from range-based algorithms, namely: Received Signal Strength (RSS), Centroid, and Distance Vector Hop (DV-Hops) protocols, using Cooja simulator. RSS localization algorithms require determining values of the RSS from the anchor nodes around the mobile node, to calculate the distance between the unknown mobile and the first three anchor nodes in the mobile range. The centroid localization requires only three anchors to compute the location of the mobile sensor without the need for distance measuring. Lastly, the DV-Hop algorithm uses routing tables of each anchor in the network topology to compute the Average Distance of Hops. The results show that rang-based algorithms are more accurate than range-free.

RSS-Based Method for Sensor Localization with Unknown Transmit Power and Uncertainty in Path Loss Exponent.

The localization of a sensor in wireless sensor networks (WSNs) has now gained considerable attention. Since the transmit power and path loss exponent (PLE) are two critical parameters in the received signal strength (RSS) localization technique, many RSS-based location methods, considering the case that both the transmit power and PLE are unknown, have been proposed in the literature. However, these methods require a search process, and cannot give a closed-form solution to sensor localization. In this paper, a novel RSS localization method with a closed-form solution based on a two-step weighted least squares estimator is proposed for the case with the unknown transmit power and uncertainty in PLE. Furthermore, the complete performance analysis of the proposed method is given in the paper. Both the theoretical variance and Cramer-Rao lower bound (CRLB) are derived. The relationships between the deterministic CRLB and the proposed stochastic CRLB are presented. The paper also proves that the proposed method can reach the stochastic CRLB.

Simplified Pedestrian Tracking Filters with Positioning and Foot-Mounted Inertial Sensors

Pedestrian tracking is one of the bases for many ubiquitous context-aware services, but it is still an open issue in indoor environments or when GPS estimations are not optimal. In this paper, we propose two novel different data fusion algorithms to track a pedestrian using current positioning technologies (i.e., GPS, received signal strength localization from Wi-Fi or Bluetooth networks, etc.) and low cost inertial sensors. In particular, the algorithms rely, respectively, on an extended Kalman filter (EKF) and a simplified complementary Kalman filter (KF). Both approaches have been tested with real data, showing clear accuracy improvement with respect to raw positioning data, with much reduced computational cost with respect to previous high performance solutions in literature. The fusion of both inputs is done in a loosely coupled way, so the system can adapt to the infrastructure that is available at a specific moment, delivering both outdoors and indoors solutions.

A Study of Localization Accuracy Using Multiple Frequencies and Powers

Wireless localization using the received signal strength (RSS) can have tremendous savings over using specialized positioning infrastructures. In this work, we explore improving RSS localization performance in multipath environments by varying the transmitter's signal power and frequency. We first derive and analyze the Cramér-Rao Lower Bound (CRLB) of RSS-based localization based on the frequency dependent path loss propagation model that considers the transmitter's signal power and frequency. The derived CRLB shows the feasibility of improving localization performance by applying frequency and power level selection for RSS-based localization. Using this analysis, we develop two new selection metrics based on the observed standard deviations of RSS as well as residuals. We then show a set of selection methods that attempt to select the combinations of power and frequencies which minimize the localization error in a representative class of localization algorithms. Our simulation results confirm the proposed selection methods can improve the localization accuracy under CRLB. Additionally, using active RFID tags, we experimentally characterize the effect of using multiple signal powers and frequencies on a wide spectrum of RSS-based algorithms. We found that the performance of all the algorithms improves when leveraging on multiple power levels and frequencies, although different algorithms present different sensitivity in terms of localization accuracy under different selection methods.

RSS Localization Algorithm Based on Nonline of Sight Identification for Wireless Sensor Network

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.