Device Free Localisation Techniques in Indoor Environments

The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised

Using context-aware sub sorting of received signal strength fingerprints for indoor localisation

Mobile indoor localisation has numerous uses for logistics, health, sport and social networking applications. Current wireless localisation systems experience reliability difficulties while operating within indoor environments due to interference caused by the presence of metallic infrastructure. Current position localisation use wireless channel propagation characteristics, such as RF receive signal strength to localise a user\u27s position, which is subject to interference. To overcome this, we developed a Fingerprint Context Aware Partitioning tracking model for tracking people within a building. The Fingerprint Context Aware Partitioning tracking model used received RF signal strength fingerprinting, combined with localised context aware information about the user\u27s immediate indoor environment surroundings. We also present an inexpensive and robust wireless localisation network that can track the location of users in an indoor environment, using the Zigbee/802.15.4 wireless communications protocol. The wireless localisation network used reference nodes placed at known positions in a building. The reference nodes are used by mobile nodes, carried by users to localise their position. We found that the Fingerprint Context Aware Partitioning model had improved performance than using only multilateration, in locations that were not in range of multiple reference nodes. Further work includes investigating how multiple mobile nodes can be used by Fingerprint Context Aware Partition model to improve position accuracy

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

Fingerprint-based localization in massive MIMO systems using machine learning and deep learning methods

À mesure que les réseaux de communication sans fil se développent vers la 5G, une énorme quantité de données sera produite et partagée sur la nouvelle plate-forme qui pourra être utilisée pour promouvoir de nouveaux services. Parmis ceux-ci, les informations de localisation des terminaux mobiles (MT) sont remarquablement utiles. Par exemple, les informations de localisation peuvent être utilisées dans différents cas de services d'enquête et d'information, de services communautaires, de suivi personnel, ainsi que de communications sensibles à la localisation. De nos jours, bien que le système de positionnement global (GPS) des MT offre la possibilité de localiser les MT, ses performances sont médiocres dans les zones urbaines où une ligne de vue directe (LoS) aux satellites est bloqué avec de nombreux immeubles de grande hauteur. En outre, le GPS a une consommation d'énergie élevée. Par conséquent, les techniques de localisation utilisant la télémétrie, qui sont basées sur les informations de signal radio reçues des MT tels que le temps d'arrivée (ToA), l'angle d'arrivée (AoA) et la réception de la force du signal (RSS), ne sont pas en mesure de fournir une localisation de précision satisfaisante. Par conséquent, il est particulièrement difficile de fournir des informations de localisation fiables des MT dans des environnements complexes avec diffusion et propagation par trajets multiples. Les méthodes d'apprentissage automatique basées sur les empreintes digitales (FP) sont largement utilisées pour la localisation dans des zones complexes en raison de leur haute fiabilité, rentabilité et précision et elles sont flexibles pour être utilisées dans de nombreux systèmes. Dans les réseaux 5G, en plus d'accueillir plus d'utilisateurs à des débits de données plus élevés avec une meilleure fiabilité tout en consommant moins d'énergie, une localisation de haute précision est également requise. Pour relever un tel défi, des systèmes massifs à entrées multiples et sorties multiples (MIMO) ont été introduits dans la 5G en tant que technologie puissante et potentielle pour non seulement améliorer l'efficacité spectrale et énergétique à l'aide d'un traitement relativement simple, mais également pour fournir les emplacements précis des MT à l'aide d'un très grand nombre d'antennes associées à des fréquences porteuses élevées. Il existe deux types de MIMO massifs (M-MIMO), soit distribué et colocalisé. Ici, nous visons à utiliser la méthode basée sur les FP dans les systèmes M-MIMO pour fournir un système de localisation précis et fiable dans un réseau sans fil 5G. Nous nous concentrons principalement sur les deux extrêmes du paradigme M-MIMO. Un grand réseau d'antennes colocalisé (c'est-à-dire un MIMO massif colocalisé) et un grand réseau d'antennes géographiquement distribué (c'est-à-dire un MIMO massif distribué). Ensuite, nous ex trayons les caractéristiques du signal et du canal à partir du signal reçu dans les systèmes M-MIMO sous forme d'empreintes digitales et proposons des modèles utilisant les FP basés sur le regroupement et la régression pour estimer l'emplacement des MT. Grâce à cette procédure, nous sommes en mesure d'améliorer les performances de localisation de manière significative et de réduire la complexité de calcul de la méthode basée sur les FP.As wireless communication networks are growing into 5G, an enormous amount of data will be produced and shared on the new platform, which can be employed in promoting new services. Location information of mobile terminals (MTs) is remarkably useful among them, which can be used in different use cases of inquiry and information services, community services, personal tracking, as well as location-aware communications. Nowadays, although the Global Positioning System (GPS) offers the possibility to localize MTs, it has poor performance in urban areas where a direct line-of-sight (LoS) to the satellites is blocked by many tall buildings. Besides, GPS has a high power consumption. Consequently, the ranging based localization techniques, which are based on radio signal information received from MTs such as time-of-arrival (ToA), angle-of-arrival (AoA), and received signal strength (RSS), are not able to provide satisfactory localization accuracy. Therefore, it is a notably challenging problem to provide precise and reliable location information of MTs in complex environments with rich scattering and multipath propagation. Fingerprinting (FP)-based machine learning methods are widely used for localization in complex areas due to their high reliability, cost-efficiency, and accuracy and they are flexible to be used in many systems. In 5G networks, besides accommodating more users at higher data rates with better reliability while consuming less power, high accuracy localization is also required in 5G networks. To meet such a challenge, massive multiple-input multiple-output (MIMO) systems have been introduced in 5G as a powerful and potential technology to not only improve spectral and energy efficiency using relatively simple processing but also provide an accurate locations of MTs using a very large number of antennas combined with high carrier frequencies. There are two types of massive MIMO (M-MIMO), distributed and collocated. Here, we aim to use the FP-based method in M-MIMO systems to provide an accurate and reliable localization system in a 5G wireless network. We mainly focus on the two extremes of the M-MIMO paradigm. A large collocated antenna array (i.e., collocated M-MIMO ) and a large geographically distributed antenna array (i.e., distributed M-MIMO). Then, we extract signal and channel features from the received signal in M-MIMO systems as fingerprints and propose FP-based models using clustering and regression to estimate MT's location. Through this procedure, we are able to improve localization performance significantly and reduce the computational complexity of the FP-based method

Improving the performance of a radio-frequency localization system in adverse outdoor applications

In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate's performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm