Received Signal Strength Difference Research Articles

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Abstract Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

An efficient estimator for source localization in WSNs using RSSD and TDOA measurements

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

A Novel Iterative Positioning Method Based on Difference RSS Model With 5G Field Experiments

In the 5th Generation (5G) positioning, the accurate values of environmental parameters such as Pathloss exponent (PLE) and transmit power are difficult to obtain accurately. Methods based on the difference of received signal strength (DRSS) can be utilized to solve the problems. An estimation method to obtain the distance ratio is given, and a distance ratio positioning model is proposed. The initial positioning results are calculated with linear least squares (LLS) algorithm. A Levenberg-Marquardt (LM) based maximum a posteriori (MAP) algorithm is proposed to obtain the final result by performing a series of iterations. We have completed the 5G field data collection in urban environments and preprocessed the raw data in 5G field experiments. We also propose a judgment method for 5G positioning based on horizontal dilution of precision (HDOP). Compared to the state-of-art DRSS-based algorithms, the proposed method has the best positioning accuracy and achieves a positioning error of 43.8 meters for 80% of samples with six base stations.

Indoor localization for an unknown radio emitter employing graph-based optimization and improved RSSD

Accurately locating an indoor unknown radio emitter (URE) is a challenging target to ensure telecommunication security. The URE positioning method based on received signal strength difference (RSSD) has attracted considerable attention due to the advantage of not being affected by transmit power and frequency. However, the RSSD-based fingerprint technique cannot accurately express the constraint equations between signal characteristics and geographic coordinates because of redundant databases and false matching. In this paper, a novel RSSD-based indoor positioning method using factor graph (FG) for an URE is proposed to improve positioning accuracy and reduce computational complexity. Firstly, the databases are reconstructed by singular value decomposition (SVD) to eliminate redundant factor nodes. Secondly, Pearson correlation coefficient (PCC) is utilized to determine the sub-positioning area. Combing SVD with PCC, the hyperplane equations are reconstructed to build an optimized FG model, called SP-FG. Considering simulation and experiment, the proposed SP-FG algorithm improves the cumulative distribution function (CDF) of average positioning error within 0.5 m by 10% and 14% compared with conventional FG algorithm and K-nearest neighbor (KNN) algorithm, respectively. In addition, this paper discusses the superiority of proposed SP-FG algorithm in positioning accuracy under different reference side lengths, access point (AP) coordinates and numbers.

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

Improved differential evolution for RSSD-based localization in Gaussian mixture noise

Signal strength-based localization approaches are prevalent in wireless sensor networks due to their low cost and simplicity. However, factors such as human interference and heterogeneous sources make approaches based on Gaussian noise and known transmit power unreliable. To address these issues, a received signal strength difference (RSSD) based approach is proposed to localize a source with unknown transmit power and Gaussian mixture noise. First, an RSSD-based nonconvex maximum likelihood (ML) problem is formulated which does not require an approximation or good initial point. Then, an improved differential evolution (IDE) method is given to obtain a global solution. Opposition-based learning (OL) combined with a chaotic map (CM) is used to obtain a robust population and adaptive mutation (AM) with two subpopulations is employed to balance global exploration and convergence. The corresponding Cramér–Rao lower bound (CRLB) for Gaussian mixture noise is derived for comparison purposes. Numerical results are presented which show that the proposed OLAM-IDE method provides better localization accuracy than state-of-the-art approaches.

An Indoor Unknown Radio Emitter Positioning Approach Using Improved RSSD Location Fingerprinting

The accurate location of an unknown radio emitter (URE) is a critical task in wireless communication security. The URE localization method based on the received signal strength difference (RSSD) has become popular due to the identification of unknown transmitting power and frequency. However, high computational complexity and low positioning accuracy have been caused by the RSSD fingerprint data’s redundancy and cross-correlation. In this article, an indoor RSSD-based positioning algorithm combining principal component analysis (PCA) and Pearson correlation coefficient (PCC), called RSSD-PCA-PCC, is proposed to realize efficient feature extraction and reduce false fingerprint matching. Firstly, to achieve reduction and decorrelation, the principal components of the RSSD fingerprint database are extracted by the singular value decomposition (SVD) method. Secondly, the PCC is applied to measure the relative distance between the principal component features. In particular, the PCC is used for selecting the reference points (RPs) in order to match the position accurately. The results show that the proposed algorithm can obtain a more superior performance compared with the conventional RSSD-based weighted k-nearest neighbor algorithm (RSSD-WKNN) and COS matching algorithm (RSSD-PCA-COS) in the case of different selected RP numbers, AP numbers, and grid distances.

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.

Optimal Configuration for Monitoring Stations in a Wireless Localisation Network Based on Received Signal Strength Differences.

A smart city is a city equipped with many sensors communicating with each other for different purposes. Cybersecurity and signal security are important in such cities, especially for airports and harbours. Any signal interference or attack on the navigation of autonomous vehicles and aircraft may lead to catastrophes and risks in people's lives. Therefore, it is of tremendous importance to develop wireless security networks for the localisation of any radio frequency interferer in smart cities. Time of arrival, angle of arrival, time-difference of arrivals, received signal strength and received signal strength difference (RSSD) are known observables used for the localisation of a signal interferer. Localisation means to estimate the coordinates of an interferer from some established monitoring stations and sensors receiving such measurements from an interferer. The main goal of this study is to optimise the geometric configuration of the monitoring stations using a desired dilution of precision and/or variance-covariance matrix (VCM) for the transmitter's location based on the RSSD. The required mathematical models are developed and applied to the Arlanda international airport of Sweden. Our numerical tests show that the same configuration is achieved based on dilution of precision and VCM criteria when the resolution of design is lower than 20 m in the presence of the same constraints. The choice of the pathloss exponent in the mathematical models of the RSSDs is not important for such low resolutions. Finally, optimisation based on the VCM is recommended because of its larger redundancy and flexibility in selecting different desired variances and covariances for the coordinates of the transmitter.

Fast RSSD multi-target localization in NLOS environments

Signal strength–based localization is commonly employed in wireless sensor networks due to its low complexity and simplicity. However, in non-line-of-sight (NLOS) environments with unknown transmit power, effective and efficient multi-target localization is a challenging task. In this paper, a fast multi-target localization based on a neural network (FMLNN) is proposed. The received signal strength difference (RSSD) is employed and NLOS bias is considered. Determining the maximum likelihood (ML) estimator is a complex and highly non-convex problem, so it is solved indirectly using a neural network. First, prior data composed of known target information and RSSD values are used in offline training to learn the nonlinear relationship. Then, the locations of multiple targets are estimated online using the trained network. Results are presented which show the proposed method provides fast and efficient localization of multiple targets, and has greater robustness to NLOS bias than conventional state-of-the-art methods.

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.

Robust DRSS Based Localization in Sensor Networks With Generalized Gaussian Noise

This letter addresses the problem of robust differential received signal strength (DRSS) based localization in the presence of generalized Gaussian noise. Instead of transforming the nonlinear equation into a pseudo-linear equation, we develop a maximum likelihood (ML) estimator which is based on the unconstrained <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{p}$ </tex-math></inline-formula> -norm optimization problem for highly nonlinear cost functions. A non-iterative Monte Carlo importance sampling (MCIS) method is proposed to solve the optimization problem. To obtain the global optimal solution using relatively a fair number of random particles, a robust <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> -norm based estimator for initial position is developed by solving the linear programming problem. The MCIS- <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> method can yield a nearly unbiased estimate and avoid derivation or matrix multiplication operations compared to these algorithms that are based on pseudo-linear measurement equations. Simulation shows that the localization algorithm proposed in this letter achieves significant performance improvement in wireless sensor network localization with generalized Gaussian noise.

Unknown Transmit Power RSSD-Based Localization in a Gaussian Mixture Channel

Localization is a critical task in wireless sensor networks (WSNs). However, accurate localization is difficult when there is noise from external sources and faulty nodes, especially when the transmit power is unknown. Signal strength-based localization methods are popular due to their low complexity and simple implementation. In this paper, robust localization based on the received signal strength difference (RSSD) is considered with unknown transmit power and Gaussian mixture noise in the presence of faulty nodes. A robust fault-tolerant localization (RFLT) technique is proposed by reformulating the original localization problem using a generalized trust-region subproblem (GTRS) framework. The resulting problem is non-convex so it is divided into two subproblems using a regularization item (RI) with a block-update surrogate function (BUSF). The active set method (ASM) is used to obtain an initial solution. The corresponding Cramer–Rao lower bound (CRLB) for Gaussian mixture noise is derived as a benchmark. Simulation results are presented which show that the performance of the proposed method is superior to other state-of-the-art methods.

RSSD-Based MSE-SDP Source Localization With Unknown Position Estimation Bias

Passive source positioning is of great interest due to the numerous applications. Energy based localization methods are popular because of their low cost and simplicity. In this paper, source localization with unknown transmit power is considered based on received signal strength difference (RSSD) measurements. An efficient three stage estimator is presented. First, a nonlinear RSSD-based model is formulated and the corresponding Fisher information for Gaussian distributed noise is derived. Next, a mean squared error (MSE) estimator is developed based on an unknown linear bias and Fisher information minimization. This results in a nonlinear optimization problem to minimize the MSE directly considering the linear bias. Finally, semidefinite relaxation is employed to transform this nonconvex problem into a convex minimization problem. This can be solved efficiently to obtain the optimal solution of the corresponding semidefinite programming (SDP) problem. Necessary and sufficient conditions for the optimality of the proposed RSSD based linear biased MSE SDP method (RLBM-SDP) are derived. Further, the corresponding bias sensitivity is formulated which yields an extension of the proposed method to a bounded RLBM-SDP (BRLBM-SDP) algorithm that includes a new constraint on the norm of the bias sensitivity. The computational complexity of the proposed methods is evaluated. Performance results are presented which confirm the efficiency of the proposed methods for sufficiently large signal to noise ratios.

Closed-Form Pseudolinear Estimators for DRSS-AOA Localization.

This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.

Research on the Minimum Size of Received Signal Strength Difference Localization Network

The received signal strength difference (RSSD) localization is a kind of method to locate emission sources by measuring the differences of received signal strength level between the monitoring stations and is essentially the truth value ratios of measured signal strength. In the existing literatures, only the rule of RSSD localization circle of two monitoring stations and the geometric relation of RSSD localization circle of five monitoring stations were analyzed, but the number and the station layout of the minimum RSSD localization network have not been investigated. In the present work, first, based on the existing RSSD localization equation, the constants of the commonly used wave propagation models are provided. Then, the minimum RSSD localization network is proved through algebraic analysis, which is that four monitoring stations not distributed on a straight line can locate the signal source at one point. The relationship between the localization accuracy and the signal strength error of the RSSD location network with different scales is studied further and formulated as a nonlinear programming optimization problem. It is found that the localization stability of the network with four stations is poor, and there is a serious localization deviation outlier phenomenon. Therefore, the network with four stations is not available for radio monitoring networks with a signal strength error of ± 5 to ± 10 dB. The RSSD network with five stations is basically the minimum available size, and the RSSD network with nine stations can approach the localization accuracy of the angle of arrival (AOA) network with three stations.

Improved RSSD-Based Source Localization With Unknown Sensor Position Errors

In this letter, a received signal strength difference (RSSD) approach is presented to localize a source with unknown transmit power in the presence of sensor position errors. The performance of conventional least squares (LS) algorithms is degraded because they consider measurement errors rather than estimation errors. An algorithm is presented here to overcome this problem. First, a robust minimax mean squared error (MSE) estimator is developed based on the estimation error for bounded location estimation and sensor position errors to minimize the worst case sum of the variance and squared norm of the bias. This nonlinear problem is solved by transforming the nonconvex objective function into a convex optimization problem using the S-procedure, relaxation, and semidefinite programming. This problem is extended to the unknown path loss exponent case. Necessary and sufficient conditions are given for convergence of the proposed RSSD convex relaxation of MSE semidefinite programming (RCRM-SDP) approach. Simulation results are presented which confirm the robustness of this method for sufficiently large signal to noise ratios (SNRs).

RSS Localization Under Gaussian Distributed Path Loss Exponent Model

We consider localization from the received signal strength (RSS) when the transmit power and the log-distance pathloss exponent (PLE) are unknown. The unknown transmit power problem is handled by working with the difference of RSS (DRSS) from a reference node. The unknown PLE is statistically modelled as a Gaussian distributed random variable. A maximum-likelihood estimation procedure is firstly proposed to obtain the ratio-of-distances in closed-form. Next, in order to obtain the source location from the ratio-of-distance estimates, we propose a two-step linear least squares (TLLS) estimator which exploits the known relation between the source coordinates and the range variable. Finally, we propose a maximum-a-posteriori (MAP) estimator which jointly estimates the source location and the PLE by maximizing the posterior likelihood of the DRSS values, given the distribution of the PLE. Numerical studies validate the improved localization accuracy of the proposed estimators over the state-of-the-art.

Robust Recursive RSSD Based Source Localization in Gaussian Mixture Channels

Signal strength based localization methods are of great interest due to their low cost and simple implementation. In this letter, a received signal strength difference (RSSD) based approach is presented to localize a source with unknown transmit power. A robust two stage estimator is proposed. First, an RSSD based optimization problem is formulated in the presence of Gaussian mixture measurement noise based on Huber's minimax model. This yields a nonlinear problem which can be approximated by Taylor series expansion. Then, a robust recursive least squares (RRLS) method is developed to provide a robust solution of the approximated problem. The corresponding Cramér-Rao lower bound (CRLB) for correlated Gaussian mixture noise is also derived as a performance benchmark. Results are presented which show that the RRLS method attains the CRLB for a sufficiently large signal to noise ratio (SNR).