DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection

Recent advancements in Internet of Things (IoTs) have brought about a surge of interest in indoor positioning for the purpose of providing reliable, accurate, and energy-efficient indoor navigation/localization systems. Ultra Wide Band (UWB) technology has been emerged as a potential candidate to satisfy the aforementioned requirements. Although UWB technology can enhance the accuracy of indoor positioning due to the use of a wide-frequency spectrum, there are key challenges ahead for its efficient implementation. On the one hand, achieving high precision in positioning relies on the identification/mitigation Non Line of Sight (NLoS) links, leading to a significant increase in the complexity of the localization framework. On the other hand, UWB beacons have a limited battery life, which is especially problematic in practical circumstances with certain beacons located in strategic positions. To address these challenges, we introduce an efficient node selection framework to enhance the location accuracy without using complex NLoS mitigation methods, while maintaining a balance between the remaining battery life of UWB beacons. Referred to as the Deep Q-Learning Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user is autonomously trained to determine the optimal set of UWB beacons to be localized based on the 2-D Time Difference of Arrival (TDoA) framework. The effectiveness of the proposed DQLEL framework is evaluated in terms of the link condition, the deviation of the remaining battery life of UWB beacons, location error, and cumulative rewards. Based on the simulation results, the proposed DQLEL framework significantly outperformed its counterparts across the aforementioned aspects

Position estimation in indoor localization with trilateration

Triangulateration uses geometric distance to estimate the location of user by employing techniques like received signal strength (RSS), time-of-arrival (TOA), time-difference-of-arrival (TDOA) and image processing. Radio frequency (RF) signal positioning using TOA or TDOA techniques generally requires timing synchronization of the anchors and/or the anchors and targets. If the desired position accuracy is high and coverage area is large, timing synchronization will be an extremely challenging issue. The first part of this dissertation focuses on improving the performance or deployability of indoor localization systems. Specifically, we propose a synchronization-free positioning network architecture that eliminates the need of timing synchronization. Another problem that remains unsolved in RF based localization is the non-line-of-sight (NLOS) problem, which greatly degrades the positioning performance. We propose a semidefinite programing (SDP) with a soft-minimal method and an NLOS link identification method with bias deduction to mitigate the NLOS error TOA systems. For TDOA systems, NLOS mitigation is more difficult since a reference should be fixed first. To overcome this problem, we propose a method to transform the TDOA architecture into a TOA one, and then form a SDP problem with new constraints. To avoid the special problems and difficulties in RF signal positioning, such as the synchronization and NLOS problems, in the second part of the dissertation, we propose an image-tag based localization using image processing and convolutional neural network (CNN). In the proposed method, after the segmentation of the tag from the image, information such as the tag ID, the distance, and the angle with reference to the camera are retrieved through CNNs. The camera position is finally reliably and accurately estimated from such retrieved information. The proposed method simplifies the system and provide good accuracy compare to RF based system. In addition, the proposed method effectively resolve those issues that exist in the traditional image-based localization, like the high cost, blind spot problems and unreliable and not scalable for in changing environments.Keywords: Convolutional neural network (CNN), Wi-Fi, Image, UWB, Algorithm, Localizatio

Localisation of sensor nodes with hybrid measurements in wireless sensor networks

Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution

On the Positioning of Sensors with Simultaneous Bearing and Range Measurement in Wireless Sensor Networks

Hybrid range and bearing based approach towards active localization of beacons will be widely celebrated in the near future, due to the protocols used for data transmission through targeted beam of radiation in 5G networks. This technique, which is one of the building blocks of 5G infrastructure does not only allow extremely high data rates but will also allow the estimation of direction of arrival/departure of the signal. Thus, in this paper a hybrid angle/range based approach towards positioning is under focus. A linear least squares approach will be applied to the unbiased version of hybrid direction of arrival-time of flight (DoA-ToF) measurement model. Thus, the unbiasing constant is first calculated followed by the theoretical mean squares expression calculation, to be utilized for selecting only those reference beacons that guarantee an improvement in the accuracy of the least squares approach. A critical distance expression is also derived that determines the relationship between the noise variance of angle and range estimates in terms of the distance between nodes. Furthermore, a weighted least squares solution is presented which exploits the noise covariance matrix of the hybrid measurement model. Finally, the weighted solution is bounded by the linear Cramér-Rao bound (LCRB) for the hybrid signal model

A survey on 5G massive MIMO Localization

Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field

Robust, Energy-Efficient, and Scalable Indoor Localization with Ultra-Wideband Technology

Ultra-wideband (UWB) technology has been rediscovered in recent years for its potential to provide centimeter-level accuracy in GNSS-denied environments. The large-scale adoption of UWB chipsets in smartphones brings demanding needs on the energy-efficiency, robustness, scalability, and crossdevice compatibility of UWB localization systems. This thesis investigates, characterizes, and proposes several solutions for these pressing concerns. First, we investigate the impact of different UWB device architectures on the energy efficiency, accuracy, and cross-platform compatibility of UWB localization systems. The thesis provides the first comprehensive comparison between the two types of physical interfaces (PHYs) defined in the IEEE 802.15.4 standard: with low and high pulse repetition frequency (LRP and HRP, respectively). In the comparison, we focus not only on the ranging/localization accuracy but also on the energy efficiency of the PHYs. We found that the LRP PHY consumes between 6.4–100 times less energy than the HRP PHY in the evaluated devices. On the other hand, distance measurements acquired with the HRP devices had 1.23–2 times lower standard deviation than those acquired with the LRP devices. Therefore, the HRP PHY might be more suitable for applications with high-accuracy constraints than the LRP PHY. The impact of different UWB PHYs also extends to the application layer. We found that ranging or localization error-mitigation techniques are frequently trained and tested on only one device and would likely not generalize to different platforms. To this end, we identified four challenges in developing platform-independent error-mitigation techniques in UWB localization, which can guide future research in this direction. Besides the cross-platform compatibility, localization error-mitigation techniques raise another concern: most of them rely on extensive data sets for training and testing. Such data sets are difficult and expensive to collect and often representative only of the precise environment they were collected in. We propose a method to detect and mitigate non-line-of-sight (NLOS) measurements that does not require any manually-collected data sets. Instead, the proposed method automatically labels incoming distance measurements based on their distance residuals during the localization process. The proposed detection and mitigation method reduces, on average, the mean and standard deviation of localization errors by 2.2 and 5.8 times, respectively. UWB and Bluetooth Low Energy (BLE) are frequently integrated in localization solutions since they can provide complementary functionalities: BLE is more energy-efficient than UWB but it can provide location estimates with only meter-level accuracy. On the other hand, UWB can localize targets with centimeter-level accuracy albeit with higher energy consumption than BLE. In this thesis, we provide a comprehensive study of the sources of instabilities in received signal strength (RSS) measurements acquired with BLE devices. The study can be used as a starting point for future research into BLE-based ranging techniques, as well as a benchmark for hybrid UWB–BLE localization systems. Finally, we propose a flexible scheduling scheme for time-difference of arrival (TDOA) localization with UWB devices. Unlike in previous approaches, the reference anchor and the order of the responding anchors changes every time slot. The flexible anchor allocation makes the system more robust to NLOS propagation than traditional approaches. In the proposed setup, the user device is a passive listener which localizes itself using messages received from the anchors. Therefore, the system can scale with an unlimited number of devices and can preserve the location privacy of the user. The proposed method is implemented on custom hardware using a commercial UWB chipset. We evaluated the proposed method against the standard TDOA algorithm and range-based localization. In line of sight (LOS), the proposed TDOA method has a localization accuracy similar to the standard TDOA algorithm, down to a 95% localization error of 15.9 cm. In NLOS, the proposed TDOA method outperforms the classic TDOA method in all scenarios, with a reduction of up to 16.4 cm in the localization error.Cotutelle -yhteisväitöskirj