Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201

Distributed AOA-based source positioning in NLOS with sensor networks

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non lineof-sight (LOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.Peer ReviewedPostprint (author's final draft

Robust Target Localization for Multistatic Passive Radar Networks

Passive multistatic radar networks localize a non-cooperative target exploiting the bistatic measurements associated with signals emitted by several transmitters of opportunity and acquired by multiple receivers. However, in realistic scenarios, it might happen that one or more receivers are damaged and/or under malicious attacks with a consequent degradation of the system performance. This paper proposes a procedure to reveal sensors that return anomalous measurements due to an attack or a failure. Specifically, we first detect such measurements by solving a binary hypothesis test, then we cancel them and estimate the final delays as well as the target position. The performance of the overall architecture is assessed using both synthetic data and real-recorded data

Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares

In this paper, the problem of target localization in the presence of outlying sensors is tackled. This problem is important in practice because in many real-world applications the sensors might report irrelevant data unintentionally or maliciously. The problem is formulated by applying robust statistics techniques on squared range measurements and two different approaches to solve the problem are proposed. The first approach is computationally efficient; however, only the objective convergence is guaranteed theoretically. On the other hand, the whole-sequence convergence of the second approach is established. To enjoy the benefit of both approaches, they are integrated to develop a hybrid algorithm that offers computational efficiency and theoretical guarantees. The algorithms are evaluated for different simulated and real-world scenarios. The numerical results show that the proposed methods meet the Cr'amer-Rao lower bound (CRLB) for a sufficiently large number of measurements. When the number of the measurements is small, the proposed position estimator does not achieve CRLB though it still outperforms several existing localization methods.Comment: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS): http://ieeexplore.ieee.org/document/8108770

Malicious and Malfunctioning Node Detection via Observed Physical Layer Data

There are many mechanisms that can cause inadequate or unreliable information in sensor networks. A user of the network might be interested in detecting and classifying specific sensors nodes causing these problems. Several network layer based trust methods have been developed in previous research to assess these issues; in contrast this work develops a trust protocol based on observations of physical layer data collected by the sensors. Observations of physical layer data are used for decisions and calculations, and are based on just the measurements collected by the sensors. Although this information is packaged and distributed on the network layer, only the physical measurement is considered. This protocol is used to detect faulty nodes operating in the sensor network. The context of this research is Wireless Network Discovery (WND), which refers to modeling all layers of a non-cooperative wireless network. The focus in particular is the localization of transmitters, and detection of sensors affecting the localization. To accomplish this, a model for faulty sensors and two methods of detection are developed. Detection rates are analyzed with Receiver Operating Characteristic (ROC) curves, and the trade-off of detection versus localization error is discussed. Classification between faulty sensors is also considered to determine appropriate response to potential network attacks

Sensor Fusion for Mobile Robot Localization using UWB and ArUco Markers

Uma das principais características para considerar um robô autónomo é o facto de este ser capaz de se localizar, em tempo real, no seu ambiente, ou seja saber a sua posição e orientação. Esta é uma área desafiante que tem sido estudada por diversos investigadores em todo o mundo. Para obter a localização de um robô é possível recorrer a diferentes metodologias. No entanto há metodologias que apresentam problemas em diferentes circunstâncias, como é o caso da odometria que sofre de acumulação de erros com a distância percorrida pelo robô. Outro problema existente em diversas metodologias é a incerteza na deteção do robô devido a ruído presente nos sensores. Com o intuito de obter uma localização mais robusta do robô e mais tolerante a falhas é possível combinar diversos sistemas de localização, combinando assim as vantagens de cada um deles. Neste trabalho, será utilizado o sistema Pozyx, uma solução de baixo custo que fornece informação de posicionamento com o auxílio da tecnologia Ultra-WideBand Time-of-Flight (UWB ToF). Também serão utilizados marcadores ArUco colocados no ambiente que através da sua identificação por uma câmara é também possível obter informação de posicionamento. Estas duas soluções irão ser estudadas e implementadas num robô móvel, através de um esquema de localização baseada em marcadores. Primeiramente, irá ser feita uma caracterização do erro de ambos os sistemas, uma vez que as medidas não são perfeitas, havendo sempre algum ruído nas medições. De seguida, as medidas fornecidas pelos sistemas irão ser filtradas e fundidas com os valores da odometria do robô através da implementação de um Filtro de Kalman Extendido (EKF). Assim, é possível obter a pose do robô (posição e orientação), pose esta que é comparada com a pose fornecida por um sistema de Ground-Truth igualmente desenvolvido para este trabalho com o auxílio da libraria ArUco, percebendo assim a precisão do algoritmo desenvolvido. O trabalho desenvolvido mostrou que com a utilização do sistema Pozyx e dos marcadores ArUco é possível melhorar a localização do robô, o que significa que é uma solução adequada e eficaz para este fim.One of the main characteristics to consider a robot truly autonomous is the fact that it is able to locate itself, in real time, in its environment, that is, to know its position and orientation. This is a challenging area that has been studied by several researchers around the world. To obtain the localization of a robot it is possible to use different methodologies. However, there are methodologies that present problems in different circumstances, as is the case of odometry that suffers from error accumulation with the distance traveled by the robot. Another problem existing in several methodologies is the uncertainty in the sensing of the robot due to noise present in the sensors. In order to obtain a more robust localization of the robot and more fault tolerant it is possible to combine several localization systems, thus combining the advantages of each one. In this work, the Pozyx system will be used, a low-cost solution that provides positioning information through Ultra-WideBand Time-of-Flight (UWB ToF) technology. It will also be used ArUco markers placed in the environment that through their identification by a camera it is also possible to obtain positioning information. These two solutions will be studied and implemented in a mobile robot, through a beacon-based localization scheme. First, an error characterization of both systems will be performed, since the measurements are not perfect, and there is always some noise in the measurements. Next, the measurements provided by the systems will be filtered and fused with the robot's odometry values by the implementation of an Extended Kalman Filter (EKF). In this way, it is possible to obtain the robot's pose, i.e position and orientation, which is compared with the pose provided by a Ground-Truth system also developed for this work with the aid of the ArUco library, thus realizing the accuracy of the developed algorithm. The developed work showed that with the use of the Pozyx system and ArUco markers it is possible to improve the robot localization, meaning that it is an adequate and effective solution for this purpose