Localisation of sensor nodes with hybrid measurements in wireless sensor networks

Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution

Exploiting Orientation Information to Improve Range-Based Localization Accuracy

Funding Information: This work supported in part by the Fundação para a Ciência e a Tecnologia under Project IF/00325/2015, Project foRESTER PCIF/SSI/0102/2017, and Project UIDB/04111/2020, and in part by the Universidade Lusófona/ILIND internal project TESLA.This work addresses target localization problem in precarious surroundings where possibly no links are line of sight. It exploits the known architecture of available reference points to act as an irregular antenna array in order to estimate the azimuth angle between a reference point and a target, based on distance estimates withdrawn from integrated received signal strength (RSS) and time of arrival (TOA) observations. These fictitious azimuth angle observations are then used to linearize the measurement models, which triggers effortless derivation of a new estimator in a closed-form. It is shown here that, by using fixed network geometry in which target orientation with respect to a line formed by a pair of anchors can be correctly estimated, the localization performance can be significantly enhanced. The new approach is validated through computer simulations, which corroborate our intuition of profiting from inherent information within a network.publishersversionpublishe

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Optimised Localisation in Wireless Sensor Networks

Wireless sensor networks (WSNs) comprise of tens, hundreds or thousands of low powered, low cost wireless nodes, capable of sensing environmental data such as humidity and temperature. Other than these sensing abilities, these nodes are also able to locate themselves. Different techniques can be found in literature to localise wireless nodes in WSNs. These localisation algorithms are based on the distance estimates between the nodes, the angle estimates between the nodes or hybrid schemes. In the context of range based algorithms, two prime techniques based on the time of arrival (ToA) and the received signal strength (RSS) are commonly used. On the other hand, angle based approach is based on the angle of arrival (AoA) of the signal. A hybrid approach is sometimes used to localise wireless nodes. Hybrid algorithms are more accurate than range and angle based algorithms because of additional observations. Modern WSNs consist of a small group of highly resourced wireless nodes with known locations called anchor nodes (ANs) and a large group of low resourced wireless nodes known as the target nodes (TNs). The ANs can locate themselves through GPS or they may have a predetermined location given to them during network deployment. Based on these known locations and the range/angle estimates, the TNs are localised. Since hybrid algorithms (a combination of RSS, ToA and AoA) are more accurate than other algorithms, a major portion of this thesis will focus on these approaches. Two prime hybrid signal models are discussed: i) The AoA-RSS hybrid model and ii) the AoA-ToA hybrid signal model. A hybrid AoA-ToA model is first studied and is further improved by making the model unbiased and by developing a new weighted linear least squares algorithm for AoA-ToA signal (WLLS-AoA-ToA) that capitalise on the covariance matrix of the incoming signal. A similar approach is taken in deriving a WLLS algorithm for AoA-RSS signal (WLLS-AoA-RSS). Moreover expressions of theoretical mean square error (MSE) of the location estimate for both signal models are derived. Performances of both signal models are further improved by designing an optimum anchor selection (OAS) criterion for AoA-ToA signal model and a two step optimum anchor selection (TSOAS) criterion for AoA-RSS signal model. To bound the performance of WLLS algorithms linear Cramer Rao bounds (LCRB) are derived for both models, which will be referred to as LCRB-AoA-ToA and LCRB-AoA-RSS, for AoA-ToA and AoA-RSS signal models, respectively. These hybrid localisation schemes are taken one step further and a cooperative version of these algorithms (LLS-Coop) is designed. The cooperation between the TNs significantly improves the accuracy of final estimates. However this comes at a cost that not only the ANs but the TNs must also be able to estimate AoA and ToA/RSS simultaneously. Thus another version of the same cooperative model is designed (LLS-Coop-X) which eliminates the necessity of simultaneous angle-range estimation by TNs. A third version of cooperative model is also proposed (LLS-Opt-Coop) that capitalises the covariance matrix of incoming signal for performance improvement. Moreover complexity analysis is done for all three versions of the cooperative schemes and is compared with its non cooperative counterparts. In order to extract the distance estimate from the RSS the correct knowledge of path-loss exponent (PLE) is required. In most of the studies this PLE is assumed to be accurately known, also the same and fixed PLE value is used for all communication links. This is an oversimplification of real conditions. Thus error analysis of location estimates with incorrect PLE assumptions for LLS technique is done in their respective chapters. Moreover a mobile TN and an unknown PLE vector is considered which is changing continuously due to the motion of TN. Thus the PLE vector is first estimated using the generalized pattern search (GenPS) followed by the tracking of TN via the Kalman filter (KF) and the particle filter (PF). The performance comparison in terms of root mean square error (RMSE) is also done for KF, extended Kalman filter (EKF) and PF

Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

A Robust NLOS Bias Mitigation Technique for RSS-TOA-Based Target Localization

This letter proposes a novel robust mitigation technique to address the problem of target localization in adverse nonline- of-sight (NLOS) environments. The proposed scheme is based on combined received signal strength and time of arrival measurements. Influence of NLOS biases is mitigated by treating them as nuisance parameters through a robust approach. Due to a high degree of difficulty of the considered problem, it is converted into a generalized trust region sub-problem by applying certain approximations, and solved efficiently by merely a bisection procedure. Numerical results corroborate the effectiveness of the proposed approach, rendering it the most accurate one in all considered scenarios.IEEE SIGNAL PROCESSING LETTERS, VOL. 26, NO. 1, JANUARY 201

Angle of Arrival Estimation Utilising Frequency Diverse Radio Antenna Arrays

The purpose of this research is to investigate a novel way of combining carrier signals that are transmitted successively over Multiple Frequencies (MF) and traditional metrics to improve AoA estimation. Every signal contains three metrics, amplitude, phase, and frequency. To achieve localisation, current systems utilise the metrics of amplitude (also known as Received Signal Strength (RSS)) and phase that resolves the AoA. However, the metric of frequency is mostly used with Orthogonal Frequency-Division Multiplexing (OFDM) to increase the number of RSS and AoA metrics, which is not optimal. This research answers two questions. Can the use of MF improve AoA estimation? Also, how can MF and traditional metrics be combined for AoA estimation? The aim is to prove that the metric of frequency can be utilised more optimally. Therefore, measurements of RSS and AoA are performed in different environments for MF. To perform these measurements, ten frequency diverse Software Defined Radios (SDRs) are employed. A novel technique to time/frequency synchronise the SDRs is developed and presented. Moreover, a ten element Uniform Linear Array (ULA) is designed, simulated and manufactured. The outcomes of this research are two novel algorithms for the MF AoA estimation of a carrier transmitter. Findings of the first algorithm show that the use of MF with the RSS metric performs equally with current systems that have a higher cost and complexity. The second algorithm that utilises MF with the AoA metric demonstrates a significant reduction in the AoA estimation error, compared to current systems. Specifically, for 50\% of the measured cases the AoA estimation error is reduced by 3.7 degrees, while for 95\% of the measured cases the AoA estimation error is reduced by 27 degrees. Hence, this research proves that MF with traditional metrics can reduce system complexity and greatly improve AoA estimation

Development of a 2-dimensional angulation algorithm target locating error estimation technique

A multiangulation (MANG) system determines an emitting target location using the angle of arrival (AOA) measurement estimated from its emission with an angulation algorithm. Prior to deployment of the system, it is important to know if the horizontal coordinate (HC) root mean square error (RMSE)s obtained by the system at certain target locations given an AOA error are within approved standards set by the international regulatory bodies. For this reason, a MANG system target locating error estimation technique based on Euclidean geometrical analysis and linear regression is proposed in this paper. This is to assist in the systematic determination and prediction of the HC RMSE obtained by the angulation algorithm of the MANG system. The proposed technique is validated by comparison with the Monte Carlo (MC) simulation at some randomly selected target locations using a square receiving station (RS) configuration. Result comparison shows that the proposed technique predicts the target HC RMSE obtained by the angulation algorithm within a system coverage of 10 km by 10 km with a prediction accuracy of about ±5 m.Keywords: Multiangulation system; Angle of arrival: Angulation Algorithm; Error prediction; Linear regressio

Advanced Wireless Localisation Methods Dealing with Incomplete Measurements

Positioning techniques have become an essential part of modern engineering, and the improvement in computing devices brings great potential for more advanced and complicated algorithms. This thesis first studies the existing radio signal based positioning techniques and then presents three developed methods in the sense of dealing with incomplete data. Firstly, on the basis of received signal strength (RSS) location fingerprinting techniques, the Kriging interpolation methods are applied to generate complete fingerprint databases of denser reference locations from sparse or incomplete data sets, as a solution of reducing the workload and cost of offline data collection. Secondly, with incomplete knowledge of shadowing correlation, a new approach of Bayesian inference on RSS based multiple target localisation is proposed taking advantage of the inverse Wishart conjugate prior. The MCMC method (Metropolis-within-Gibbs) and the maximum a posterior (MAP) / maximum likelihood (ML) method are then considered to produce target location estimates. Thirdly, a new information fusion approach is developed for the time difference of arrival (TDOF) and frequency difference of arrival (FDOA) based dual-satellite geolocation system, as a solution to the unknown time and frequency offsets. All proposed methods are studied and validated through simulations. Result analyses and future work directions are discussed

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors