Optimal Precoders for Tracking the AoD and AoA of a mm-Wave Path

In millimeter-wave channels, most of the received energy is carried by a few paths. Traditional precoders sweep the angle-of-departure (AoD) and angle-of-arrival (AoA) space with directional precoders to identify directions with largest power. Such precoders are heuristic and lead to sub-optimal AoD/AoA estimation. We derive optimal precoders, minimizing the Cram\'{e}r-Rao bound (CRB) of the AoD/AoA, assuming a fully digital architecture at the transmitter and spatial filtering of a single path. The precoders are found by solving a suitable convex optimization problem. We demonstrate that the accuracy can be improved by at least a factor of two over traditional precoders, and show that there is an optimal number of distinct precoders beyond which the CRB does not improve.Comment: Resubmission to IEEE Trans. on Signal Processing. 12 pages and 9 figure

Optimization methods for active and passive localization

Active and passive localization employing widely distributed sensors is a problem of interest in various fields. In active localization, such as in MIMO radar, transmitters emit signals that are reflected by the targets and collected by the receive sensors, whereas, in passive localization the sensors collect the signals emitted by the sources themselves. This dissertation studies optimization methods for high precision active and passive localization. In the case of active localization, multiple transmit elements illuminate the targets from different directions. The signals emitted by the transmitters may differ in power and bandwidth. Such resources are often limited and distributed uniformly among the transmitters. However, previous studies based on the well known Cramer-Rao lower bound have shown that the localization accuracy depends on the locations of the transmitters as well as the individual channel gains between different transmitters, targets and receivers. Thus, it is natural to ask whether localization accuracy may be improved by judiciously allocating such limited resources among the transmitters. Using the Cr´amer-Rao lower bound for target localization of multiple targets as a figure of merit, approximate solutions are proposed to the problems of optimal power, optimal bandwidth and optimal joint power and bandwidth allocation. These solutions are computed by minimizing a sequence of convex problems. The quality of these solutions is assessed through extensive numerical simulations and with the help of a lower-bound that certifies their optimality. Simulation results reveal that bandwidth allocation policies have a stronger impact on performance than power. Passive localization of radio frequency sources over multipath channels is a difficult problem arising in applications such as outdoor or indoor geolocation. Common approaches that combine ad-hoc methods for multipath mitigation with indirect localization relying on intermediary parameters such as time-of-arrivals, time difference of arrivals or received signal strengths, are unsatisfactory. This dissertation models the localization of known waveforms over unknown multipath channels in a sparse framework, and develops a direct approach in which multiple sources are localized jointly, directly from observations obtained at distributed sources. The proposed approach exploits channel properties that enable to distinguish line-of-sight (LOS) from non-LOS signal paths. Theoretical guarantees are established for correct recovery of the sources’ locations by atomic norm minimization. A second-order-cone-based algorithm is developed to produce the optimal atomic decomposition, and it is shown to produce high accuracy location estimates over complex scenes, in which sources are subject to diverse multipath conditions, including lack of LOS

Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.Comment: 15 pages, 10 figures, in review, 201

Smart Port : Disseny i implementació d’un sistema de gestió intel·ligent per a ports recreatius.

El projecte que ens ocupa, consisteix en el desenvolupament d'un sistema "Smart Port" que es pot implementar en els ports ja existents per una gestió mes òptima en l'àmbit de reserves de places dels serveis pels clients. Es tractarà tan sols la planificació i disseny teòric dels sistemes sense implementar-los. Només es realitzaran proves tangibles amb els sistemes dels que es pugui disposar (servidor web, targeta RFID...)

Méthodes d'Optimisation pour la Localisation Active et Passive

La localisation active et passive par un réseau de capteurs distribués est un problème rencontré dans différents domaines d’application. En localisation active, telle que la localisation par radar MIMO (Multiple Input Multiple Output), les émetteurs transmettent des signaux qui sont réfléchis par les cibles visées, puis captés par les antennes réceptrices, alors qu’en localisation passive, les capteurs reçoivent des signaux transmis par les cibles elles-mêmes. L’objectif de cette thèse est d’étudier différentes techniques d’optimisation pour la localisation active et passive de haute précision. Dans la première partie de la thèse, on s’intéresse à la localisation active, où de multiples émetteurs illuminent les cibles depuis différentes directions. Les signaux peuvent être émis avec des puissances ou des largeurs de bande différentes. Ces différentes ressources, par nature en général fortement limitées, sont souvent, par défaut, réparties de façon uniforme entre les différents émetteurs. Or, la précision de la localisation dépend de la position des émetteurs, ainsi que des paramètres (les gains notamment) des différents canaux existant entre émetteurs, cibles, et capteurs. En utilisant comme critère d’optimisation la borne de Cramér-Rao sur la précision de la localisation de cibles multiples, nous proposons une méthode fournissant des solutions approchées aux problèmes d’allocation optimale de puissances seules, de largeurs de bande seules, ou au problème d’allocation conjointe de puissances et de largeurs de bande. Ces solutions sont obtenues en minimisant une suite de problèmes convexes. La qualité de ces solutions approchées est évaluée au travers de nombreuses simulations numériques, mais également par la comparaison avec une borne inférieure définie comme la solution d’un problème d’optimisation avec contraintes relaxées, cette borne pouvant être calculée de façon exacte (numériquement). Cette comparaison permet de constater la proximité de la solution approchée fournie par l’algorithme proposé par rapport à la solution théorique. D’autre part, les simulations ont montré que l’allocation de bande joue un rôle plus important dans les performances de localisation que l’allocation de puissance. Dans la seconde partie de la thèse, on considère le cas de la localisation passive de sources multiples dans un environnement multi-trajet. Ce problème se rencontre notamment dans le cadre de la géolocalisation indoor ou outdoor. Dans ce cas de figure, les approches généralement proposées dans la littérature sont basées sur une méthode ad-hoc de réduction d’interférence couplée à une localisation indirecte obtenue par une estimation de paramètres comme les temps d’arrivée des signaux ou les différences de temps d’arrivée, ou la puissance des signaux reçus. Cependant, les performances de ces approches sont limitées, notamment par le fait que la localisation indirecte d’une cible donnée ne prend pas en compte le fait que les signaux reçus par les différents capteurs émanent d’une seule et même source. Dans cette thèse, nous proposons une modélisation parcimonieuse des signaux reçus. Cette modélisation nous permet, en supposant les formes d’onde connues mais les canaux multi-trajets totalement inconnus, de développer une méthode de localisation directe de l’ensemble des cibles. Cette approche exploite certaines propriétés des canaux, qui permettent de séparer les trajets directs des trajets indirects. Un algorithme d’optimisation conique de second ordre est développé afin d’obtenir une décomposition dite atomique optimale, qui permet d’obtenir une localisation de très bonne précision dans des conditions de propagation difficiles, présentant un phénomène de multi-trajet important et/ou une absence de trajets directs. Nous montrons alors que la technique de localisation directe ainsi proposée présente de meilleures performances de localisation que les méthodes indirectes développées pour un environnement multi-trajet, mais aussi que la méthode de localisation directe la plus efficace proposée dans la littérature, qui n’est adaptée qu’au cas d’une transmission sans multi-trajet. ABSTRACT : Active and passive localization employing widely distributed sensors is a problem of interest in various fields. In active localization, such as in MIMO radar, transmitters emit signals that are reflected by the targets and collected by the receive sensors, whereas, in passive localization the sensors collect the signals emitted by the sources themselves. This dissertation studies optimization methods for high precision active and passive localization. In the case of active localization, multiple transmit elements illuminate the targets from different directions. The signals emitted by the transmitters may differ in power and bandwidth. Such resources are often limited and distributed uniformly among the transmitters. However, previous studies based on the well known Crámer-Rao lower bound have shown that the localization accuracy depends on the locations of the transmitters as well as the individual channel gains between different transmitters, targets and receivers. Thus, it is natural to ask whether localization accuracy may be improved by judiciously allocating such limited resources among the transmitters. Using the Crámer-Rao lower bound for target localization of multiple targets as a figure of merit, approximate solutions are proposed to the problems of optimal power, optimal bandwidth and optimal joint power and bandwidth allocation. These solutions are computed by minimizing a sequence of convex problems. The quality of these solutions is assessed through extensive numerical simulations and with the help of a lower-bound that certifies their optimality. Simulation results reveal that bandwidth allocation policies have a stronger impact on performance than power. Passive localization of radio frequency sources over multipath channels is a difficult problem arising in applications such as outdoor or indoor geolocation. Common approaches that combine ad-hoc methods for multipath mitigation with indirect localization relying on intermediary parameters such as time-of-arrivals, time difference of arrivals or received signal strengths, are unsatisfactory. This dissertation models the localization of known waveforms over unknown multipath channels in a sparse framework, and develops a direct approach in which multiple sources are localized jointly, directly from observations obtained at distributed sources. The proposed approach exploits channel properties that enable to distinguish line-of-sight (LOS) from non-LOS signal paths. Theoretical guarantees are established for correct recovery of the sources’ locations by atomic norm minimization. A second-order-cone-based algorithm is developed to produce the optimal atomic decomposition, and it is shown to produce high accuracy location estimates over complex scenes, in which sources are subject to diverse multipath conditions, including lack of LOS

Optimization Methods for Active and Passive Localization

Active and passive localization employing widely distributed sensors is a problem of interest in various fields. In active localization, such as in MIMO radar, transmitters emit signals that are reflected by the targets and collected by the receive sensors, whereas, in passive localization the sensors collect the signals emitted by the sources themselves. This dissertation studies optimization methods for high precision active and passive localization. In the case of active localization, multiple transmit elements illuminate the targets from different directions. The signals emitted by the transmitters may differ in power and bandwidth. Such resources are often limited and distributed uniformly among the transmitters. However, previous studies based on the well known Crámer-Rao lower bound have shown that the localization accuracy depends on the locations of the transmitters as well as the individual channel gains between different transmitters, targets and receivers. Thus, it is natural to ask whether localization accuracy may be improved by judiciously allocating such limited resources among the transmitters. Using the Crámer-Rao lower bound for target localization of multiple targets as a figure of merit, approximate solutions are proposed to the problems of optimal power, optimal bandwidth and optimal joint power and bandwidth allocation. These solutions are computed by minimizing a sequence of convex problems. The quality of these solutions is assessed through extensive numerical simulations and with the help of a lower-bound that certifies their optimality. Simulation results reveal that bandwidth allocation policies have a stronger impact on performance than power. Passive localization of radio frequency sources over multipath channels is a difficult problem arising in applications such as outdoor or indoor geolocation. Common approaches that combine ad-hoc methods for multipath mitigation with indirect localization relying on intermediary parameters such as time-of-arrivals, time difference of arrivals or received signal strengths, are unsatisfactory. This dissertation models the localization of known waveforms over unknown multipath channels in a sparse framework, and develops a direct approach in which multiple sources are localized jointly, directly from observations obtained at distributed sources. The proposed approach exploits channel properties that enable to distinguish line-of-sight (LOS) from non-LOS signal paths. Theoretical guarantees are established for correct recovery of the sources’ locations by atomic norm minimization. A second-order-cone-based algorithm is developed to produce the optimal atomic decomposition, and it is shown to produce high accuracy location estimates over complex scenes, in which sources are subject to diverse multipath conditions, including lack of LOS

Location-aided mm-wave channel estimation for vehicular communication

Millimeter-wave (mm-wave) communication is a promising technology for next-generation wireless systems. One challenging application lies in the vehicular domain, where mm-wave should support ultra-fast and high-rate data exchanges among vehicles and between vehicles and infrastructure. To achieve ultra-fast initial access between nodes, we propose a location-aided beamforming strategy and analyze the resulting performance in terms of antenna gain and latency. We find that location information can significantly speed up initial access

Allocation de ressources pour la localisation non-cohérente par radar MIMO

National audienceOn considère un réseau de radars MIMO dont on cherche à déterminer la meilleure répartition de largeurs de bande et de puissance entre les différentes antennes émettrices en vue d’obtenir une certaine précision de la localisation d’une cible unique. Plus précisément, on s’intéresse ici à l’allocation optimale de bande seule, ainsi qu’à l’allocation optimale conjointement de bande et de puissance. Cette allocation s’effectue par la minimisation de la borne de Cramér-Rao. Le problème d’optimisation non-convexe obtenu est résolu par un algorithme de programmation par différence de fonctions convexes. Les résultats numériques montrent que l’allocation conjointe fournit les meilleures performances, et que d’autre part l’allocation de bande joue un rôle prépondérant dans ces performances. De plus, une borne inférieure sur la borne de Cramér-Rao optimale théorique, difficilement calculable, a également été définie, qui montre la qualité de la solution quasi-optimale

Tensor Decomposition Based Beamspace ESPRIT for Millimeter Wave MIMO Channel Estimation

We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method by exploiting the multiple invariance structure of the measurements. Geometry-based channel model is considered to contain the channel sparsity feature. In our framework, an alternating least squares problem is solved for low rank tensor decomposition and the multidimensional parameters are automatically associated. The performance of the proposed algorithm is evaluated by considering different transformation schemes