The internet of things (IoT) currently has a large range of applications, from wearable to smart cities. Many of these applications require that the nodes inside the networks know their relative or absolute position. To this end, multiple positioning methods can be applied, among such methods are Global Positioning Systems (GPS) or methods that employ time delay of arrival (TDOA). This work presents node localization methods that employ a dual polarization receiver on a single node, or a virtual array when multiple nodes are capable of coop- erating. The proposed approaches aim to minimize the economic cost associated with implementing localization methods, and can be done with simple hardware. The accuracy of the proposed methods is measured trough a set of numerical simulations. © IFIP

Vehicular networks allow for a variety of applications ranging from platooning to fully automated driving. Most of such applications require the vehicles that constitute the networks to be aware of their relative or absolute position as well as the position of nearby vehicles. To this end, multiple positioning methods can be employed, among such methods are Global Positioning Systems or methods that employ time delay of arrival. This work presents a localization method that employs a dual polarized antenna at the transmitter and receiver side of wireless communications in vehicular networks. The proposed approach does not increase network load as it does not require extra data packets to be sent for localization purposes, and can be used to mitigate position spoofing inside the network. The accuracy and reliability of the proposed method are measured trough a set of numerical simulations, showing sufficient performance for acting as a secondary positioning mechanism capable of providing improved security and reliability to the network. © VDE VERLAG GMBH

Multi-band or multi-frequency antennas have become essential for many GNSS applications [1]. These antennas allow a receiver to simultaneously receive from multiple bands such as L1, L2, L2C, E5A, L5, and so on, which is essential for ionosphere corrections, can help mitigating multipath induced biases, and improve overall system availability. Furthermore, they also allow for multiple GNSS to be used simultaneously, improving accuracy and robustness due to the larger number of satellites available.Another advancement that has recently attracted attention in the GNSS community is the usage of antenna arrays at the receiver [2], [3]. These arrays, which can assume multiple shapes and sizes, can be used to enhance system performance in multiple ways. Beamforming can be used to null out interferers or multipath components and improve gain over a designated direction of arrival. Some antenna array geometries can also enable a receiver to estimate its attitude while relying solely on received GNSS signals.While both multi-band antennas and antenna arrays offer attractive advantages for precise GNSS positioning, merging such systems on a single receiver can be challenging. Antenna arrays have their performance largely dictated by their geometries and the spacing between antenna elements [4]. This spacing is defined with respect to the frequency of the signal that is received at the antenna array. If the spacing is too large the receiver will suffer from inaccuracy introduced by ambiguities that will be present when trying to filter out undesired signals or when trying to estimate the direction of arrival of received signals. If the spacing is too small, the total array directivity will be lower, which will lead to more biased direction of arrival estimations or to beamformers with lobes that are too broad to filter out undesired signals.The relationship between frequency and geometry makes it impossible to create a multi-band antenna array that is optimal for every frequency received, as optimizing one frequency will inevitably lead to performance degradation in the remaining ones. To tackle this issue, a technique known as array interpolation can be employed [5]. Array interpolation consists of creating a mathematical transformation that projects the signal received at a real and imperfect array onto an ideal and abstract receiver. This allows arrays whose geometries are not optimal, and even heavily distorted with respect to an optimal geometry, to achieve high levels of performance, with improved direction of arrival estimation accuracy. A different array interpolation can be constructed for each individual frequency received at the array. Thus, array interpolation can be a valuable tool for allowing multi-band antenna arrays to achieve high performance over the entire range of frequencies they are designed to receive.This work studies the effects of optimizing antenna array geometries for a given frequency band while applying array interpolation over the array response for the remaining frequency bands. Furthermore, the possibility of choosing a geometry that is not optimal for any given array geometry but achieving an overall improved performance over the entire range of frequency bands to which the array is tuned is also studied. The performance of multiple array interpolation methods is verified, and the tradeoffs between performance and computational complexity is studied.[1] J. Li, H. Shi, H. Li, and A. Zhang, “Quad-band probe-fed stacked annular patch antenna for GNSS applications,” IEEE Antennas Wirel. Propag. Lett., vol. 13, pp. 372–375, 2014.[2] S. Caizzone, “Miniaturized E5a/E1 antenna array for robust GNSS navigation,” IEEE Antennas Wirel. Propag. Lett., vol. 16, pp. 485–488, 2016.[3] S. Caizzone, W. Elmarissi, M. A. M. Marinho, and F. Antreich, “Direction of arrival estimation performance for compact antenna arrays with adjustable size,” in IEEE MTT-S International Microwave Symposium Digest, 2017.[4] Y. T. Lo, S. W. Lee, and Q. H. Lee, “Optimization of directivity and signal-to-noise ratio of an arbitrary antenna array,” Proc. IEEE, vol. 54, no. 8, pp. 1033–1045, 1966.[5] M. A. M. Marinho, F. Antreich, S. Caizzone, J. P. C. L. da Costa, A. Vinel, and E. P. de Freitas, “Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals,” Signal Processing, vol. 144, 2018. © 1995-2021, The Institute of Navigation, Inc.

This work presents a radio based localization approach that is capable of accurately positioning radio emitters even when no direct line-of-sight signal is available. A dual polarized array is employed along with the space alternating generalized expectation maximization (SAGE) algorithm. To lighten the computational load and improve the accuracy of the proposed method, Global Navigation Satellite Systems (GNSS) positioning is used to initialize and limit the search area of SAGE. A set of numerical simulations is presented, highlighting the performance of the proposed method. © 2020 for this paper by its authors.

Smart vehicles are emerging as a possible solution for multiple concerns in road traffic, such as mobility and safety. This work presents radio localization methods based on simultaneous direction of arrival (DOA), time-delay, and range estimation using the SAGE algorithm. The proposed methods do not rely on external sources of information, such as global navigation satellite systems (GNSS). The proposed methods take advantage of signals of opportunity and do not require the transmission of location-specific signals; therefore, they do not increase the network load. A set of simulations using synthetic and measured data is provided to validate the proposed methods, and the results show that it is possible to achieve accuracy down to decimeter and centimeter-level. © 2013 IEEE.

Array signal processing in wireless communication has been a topic of interest in research for over three decades. In the fourth generation (4G) of the wireless communication systems, also known as Long Term Evolution (LTE), multi antenna systems have been adopted according to the Release 9 of the 3rd Generation Partnership Project (3GPP). For the fifth generation (5G) of the wireless communication systems, hundreds of antennas should be incorporated to the devices in a massive multi-user Multiple Input Multiple Output (MIMO) architecture. The presence of multiple antennas provides array gain, diversity gain, spatial gain, and interference reduction. Furthermore, arrays enable spatial filtering and parameter estimation, which can be used to help solve problems that could not previously be addressed from a signal processing perspective. The aim of this thesis is to bridge some gaps between signal processing theory and real world applications. Array processing techniques traditionally assume an ideal array. Therefore, in order to exploit such techniques, a robust set of methods for array interpolation are fundamental and are developed in this work. In this dissertation, novel methods for array interpolation are presented and their performance in real world scenarios is evaluated. Problems in the field of wireless sensor networks and vehicular networks are also addressed from an array signal processing perspective. Signal processing concepts are implemented in the context of a wireless sensor network. These concepts provide a level of synchronization sufficient for distributed multi antenna communication to be applied, resulting in improved lifetime and improved overall network behaviour. Array signal processing methods are proposed to solve the problem of radio based localization in vehicular network scenarios with applications in road safety and pedestrian protection.