Target Localization and Tracking in Wireless Sensor Networks

This thesis addresses the target localization problem in wireless sensor networks (WSNs) by employing statistical modeling and convex relaxation techniques. The first and the second part of the thesis focus on received signal strength (RSS)- and RSS-angle of arrival (AoA)-based target localization problem, respectively. Both non-cooperative and cooperative WSNs are investigated and various settings of the localization problem are of interest (e.g. known and unknown target transmit power, perfectly and imperfectly known path loss exponent). For all cases, maximum likelihood (ML) estimation problem is first formulated. The general idea is to tightly approximate the ML estimator by another one whose global solution is a close representation of the ML solution, but is easily obtained due to greater smoothness of the derived objective function. By applying certain relaxations, the solution to the derived estimator is readily obtained through general-purpose solvers. Both centralized (assumes existence of a central node that collects all measurements and carries out all necessary processing for network mapping) and distributed (each target determines its own location by iteratively solving a local representation of the derived estimator) algorithms are described. More specifically, in the case of centralized RSS-based localization, second-order cone programming (SOCP) and semidefinite programming (SDP) estimators are derived by applying SOCP and SDP relaxation techniques in non-cooperative and cooperative WSNs, respectively. It is also shown that the derived SOCP estimator can be extended for distributed implementation in cooperative WSNs. In the second part of the thesis, derivation procedure of a weighted least squares (WLS) estimator by converting the centralized non-cooperative RSS-AoA localization problem into a generalized trust region sub-problem (GTRS) framework, and an SDP estimator by applying SDP relaxations to the centralized cooperative RSS-AoA localization problem are described. Furthermore, a distributed SOCP estimator is developed, and an extension of the centralized WLS estimator for non-cooperative WSNs to distributed conduction in cooperative WSNs is also presented. The third part of the thesis is committed to RSS-AoA-based target tracking problem. Both cases of target tracking with fixed/static anchors and mobile sensors are investigated. First, the non-linear measurement model is linearized by applying Cartesian to polar coordinates conversion. Prior information extracted from target transition model is then added to the derived model, and by following maximum a posteriori (MAP) criterion, a MAP algorithm is developed. Similarly, by taking advantage of the derived model and the prior knowledge, Kalman filter (KF) algorithm is designed. Moreover, by allowing sensor mobility, a simple navigation routine for sensors’ movement management is described, which significantly enhances the estimation accuracy of the presented algorithms even for a reduced number of sensors. The described algorithms are assessed and validated through simulation results and real indoor measurements

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

Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks

This paper investigates the hybrid source localization problem using the four radio measurements - time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS) and angle of arrival (AOA). First, after invoking tractable approximations in the RSS and AOA models, the maximum likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model is derived. Then, in the MLE, which has the least-squares objective, weights determined using the range-based characteristics of the four heterogeneous measurements, are introduced. The resultant weighted least-squares problem obtained, which is non-smooth and non-convex, is solved using the principle of the majorization-minimization (MM), leading to an iterative algorithm that has a guaranteed convergence. The key feature of the proposed method is that it provides a unified framework where localization using any possible merger out of these four measurements can be implemented as per the requirement/application. Extensive numerical simulations are conducted to study the estimation efficiency of the proposed method. The proposed method employing all four measurements is compared against a conventionally used method and also against the proposed method employing only limited combinations of the four measurements. The results obtained indicate that the hybrid localization model improves the localization accuracy compared to the heterogeneous measurements. The integration of different measurements also yields good accuracy in the presence of non-line of sight (NLOS) errors

Low-complexity three-dimensional AOA-cross geometric center localization methods via multi-UAV network

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE

Accurate angle-of-arrival measurement using particle swarm optimization

As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates

Design and theoretical analysis of advanced power based positioning in RF system

Accurate locating and tracking of people and resources has become a fundamental requirement for many applications. The global navigation satellite systems (GNSS) is widely used. But its accuracy suffers from signal obstruction by buildings, multipath fading, and disruption due to jamming and spoof. Hence, it is required to supplement GPS with inertial sensors and indoor localization schemes that make use of WiFi APs or beacon nodes. In the GPS-challenging or fault scenario, radio-frequency (RF) infrastructure based localization schemes can be a fallback solution for robust navigation. For the indoor/outdoor transition scenario, we propose hypothesis test based fusion method to integrate multi-modal localization sensors. In the first paper, a ubiquitous tracking using motion and location sensor (UTMLS) is proposed. As a fallback approach, power-based schemes are cost-effective when compared with the existing ToA or AoA schemes. However, traditional power-based positioning methods suffer from low accuracy and are vulnerable to environmental fading. Also, the expected accuracy of power-based localization is not well understood but is needed to derive the hypothesis test for the fusion scheme. Hence, in paper 2-5, we focus on developing more accurate power-based localization schemes. The second paper improves the power-based range estimation accuracy by estimating the LoS component. The ranging error model in fading channel is derived. The third paper introduces the LoS-based positioning method with corresponding theoretical limits and error models. In the fourth and fifth paper, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) system and power contour circle fitting (PCCF) algorithm are proposed to address antenna directivity effect on power-based localization. Overall, a complete LoS signal power based positioning system has been developed that can be included in the fusion scheme --Abstract, page iv

Wireless Localization for mmWave Networks in Urban Environments

Millimeter wave (mmWave) technology is expected to be a major component of 5G wireless networks. Ultra-wide bandwidths of mmWave signals and the possibility of utilizing large number of antennas at the transmitter and the receiver allow accurate identification of multipath components in temporal and angular domains, making mmWave systems advantageous for localization applications. In this paper, we analyze the performance of a two-step mmWave localization approach that can utilize time-of-arrival, angle-of-arrival, and angle-of-departure from multiple nodes in an urban environment with both line-of-sight (LOS) and non-LOS (NLOS) links. Networks with/without radio-environmental mapping (REM) are considered, where a network with REM is able to localize nearby scatterers. Estimation of a UE location is challenging due to large numbers of local optima in the likelihood function. To address this problem, a gradient-assisted particle filter (GAPF) estimator is proposed to accurately estimate a user equipment (UE) location as well as the locations of nearby scatterers. Monte Carlo simulations show that the GAPF estimator performance matches the Cramer-Rao bound (CRB). The estimator is also used to create an REM. It is seen that significant localization gains can be achieved by increasing beam directionality or by utilizing REM