Angle-based positioning has been agreed by 3GPP to be one of NR positioning techniques, and is now in standardization in 3GPP Release 16. In this paper, the key techniques for angle-based positioning are addressed including the localization algorithm and angle estimation algorithms. Uplink angle-of-arrival (UL-AOA) and downlink angle-of-departure (DL-AOD) techniques for both FR1 (sub-6GHz) and FR2 (above-6GHz) bands are presented along with their impacts on the existing NR standards. Simulation results show that both UL-AOA and DL-AOD can reach meter level accuracy in indoor scenarios, and hence can well meet the positioning requirements of the commercial cases defined in 3GPP NR. Compared to traditional timing-based positioning, the angle-based positioning is robust to the synchronization error, which makes it competitive for NR positioning.
In this paper, we study the trade-off between data and localization services while dividing time and frequency resources in a multi-user millimeter-wave system. In this multi-service system, budgeting more resources for the data service than for localization would indeed imply higher data rates but also, adversely, higher position and orientation estimation errors. Based on theoretical localization performance bounds and on the expression of the average data rate per user, we herein investigate and compare various ways the two different services could be operated.
Accurate radio signal based geolocalization for Low Power Wide Area networks is a key-enabler to various Internet of Things applications. However, localization with narrowband signals remains challenging in multipath environments. Sequential coherent multi-channel ranging improves temporal resolution while being compatible with narrowband transmissions. New radio chipsets integrate proprietary ranging functions. This paper compares a proof-of-concept implementation for coherent multi-channel ranging with narrowband signals to the Time-of-Flight ranging function of the LoRa 2.4 GHz radio chip. Benchmarking results for different configurations and propagation scenarios are discussed, illustrating the precision scalability of multi-channel ranging.
Recent years have seen a rapid growth in research on elliptic localization, due to its widespread usage in systems such as multiple-input multiple-output radar and multistatic sonar. However, most of the algorithms are devised under line-of-sight propagation, while a number of those existing non-line-of-sight (NLOS) mitigation schemes for elliptic localization are highly case-dependent. This paper addresses the problem of elliptic localization in adverse environments without assumptions about distribution/statistics of errors and NLOS status. To achieve robustness towards NLOS bias, we resort to the worst-case least squares formulation which requires only a bound on the errors. We then apply certain approximations to the resultant intractable constrained minimax optimization problem, and finally relax it into a readily solvable convex optimization problem. Simulation results show that our method can outperform several existing non-robust approaches in terms of positioning accuracy, and achieve comparable performance to a robust estimator with a lower computational cost.
Since the availability of GNSS raw measurements with Google Nougat API in 2016, research has been assessing smartphone performances and GNSS data's quality. The objective is to achieve precise positioning and to assess its quality. With the growing Internet of Things (IoT), embedded sensors spread everywhere for acquiring, assessing and monitoring the environment, for which low-cost and precise positioning is essential. Low-cost does not only relate to the financial cost of the GNSS receiver but also to other costs like data consumption, battery consumption and computation cost. Using the Google API and the new generation of smartphones, these costs can be targeted to produce smarter, more efficient, and optimized positioning solutions for being implemented on other IoT devices. This paper presents research on Real-Time Precise Point Positioning (PPP-RTK) application development in Android. Analysis of needed RTCM (Radio Technical Commission for Maritimes Services) streams is given along with the assessment of their benefits for efficient positioning. Focus is made on evaluating the cost of precise products in terms of internet data consumption.
Future applications for connected and automated driving depend on high-precision, lane selective positioning especially in dense urban environments. Estimating global user position is oftentimes based on Global Navigation Satellite Systems (GNSS), but stand-alone GNSS positioning methods do not meet the necessary requirements in terms of accuracy and robustness. To achieve higher accuracies, additional sensor information, e.g. digital maps (DM), is usually incorporated. Baseline state of the art GNSS positioning is based on deterministic and probabilistic estimation methods which do not integrate a-priori map data in a tightly coupled manner but rather perform a map matching after the GNSS position was estimated. The work presented in this paper provides a proof of concept of a novel likelihood based snapshot Probability Grid Positioning (PGP) approach for low-cost GNSS receivers using a digital elevation model (DEM). The proposed method is described in detail and validated in a real word, dynamic measurement scenario. The presented approach is compared with a conventional positioning method.
Map-based localization is an essential challenge for the development of autonomous vehicles. Popular localization solutions depend on static, semantic objects, like road signs. In this paper, we introduce a novel approach to extract feature areas (FAs) within LiDAR point clouds enabling the detection of non-semantic map (MFAs) as well as on-board (KFAs) areas. KFAs compose a set of connected points with similar geometry-based descriptors which are extracted based on their benefit for the localization task. As opposed to other extraction methods based on LiDAR descriptors, our approach selects areas rather than detecting single key points. This input is used by our extraction approach in a two-stepped clustering and discarding process resulting in non-semantic segments. Our simple localization algorithm following the feature-based approach is more accurate than point-based localization on a real-world data set. We show that the feature extraction works persistently over data sets spanning one and a half year.
Robotic swarm or multi-agent systems attract increasing attention for extraterrestrial exploration missions, among others. Control of a swarm requires communication among agents and accurate knowledge about their position and orientation. Both can be provided by radio signals exchanged between the agents. For navigation, round-trip delay (RTD) and direction-of-arrival (DoA) are informative signal metrics, as they relate to the relative distances and angles between the agents. DoA estimation is usually performed using antenna arrays. Recently, multi-mode antennas (MMAs) have been suggested as an alternative, but so far only theoretical results exist. This paper presents measurement results for DoA estimation with a single MMA mounted on a rover. The median estimation error of 7.2° for a moving rover proves the potential of DoA estimation with an MMA.
In urban environments, cellular network-based positioning of user equipment (UE) is a challenging task, especially in frequently occurring non-line-of-sight (NLOS) conditions. This paper investigates the use of two machine learning methods - neural networks and random forests - to estimate the position of UE in NLOS using best received reference signal beam power measurements. We evaluated the suggested positioning methods using data collected from a fifth generation cellular network (5G) testbed provided by Ericsson. A statistical test to detect NLOS conditions with a probability of detection that is close to 90% is suggested. We show that knowledge of the antenna are crucial for accurate position estimation. In addition, our results show that even with a limited set of training data and one 5G transmission point, it is possible to position UE within 10 meters with 80% accuracy.
This paper examines the statistical behaviour of noise in triaxial sensors, particularly magnetometers. It is shown by statistical test that the sensor noise does not follow a Normal distribution as is commonly assumed. Beside noise, other sources of error in readings of magnetometers are discussed. The combined effects of errors are demonstrated by a mathematical model, which helps calibrating magnetometer readings under the Maximum Likelihood Estimator scheme. Calibrated data is aligned to the sensor frame in order to be applied later in navigation applications, where the North finding problem is solved by true geomagnetic readings.
Cooperation between vehicles and/or with static elements of the road infrastructure enables a wide number of applications and services, such as traffic monitoring and prediction, localization and mapping, or novel safety approaches for vulnerable road users. For instance, relying on on-board sensors, on conventional navigation systems, and on V2X wireless connectivity, vehicles can represent geo-tagged measurements (e.g., LiDAR) in the form of local occupancy grid maps accounting for the presence of obstacles. The latter maps can be subsequently shared, either directly (e.g., with other fellow vehicles around) or via a centralized entity (e.g., edge cloud...). Through cooperative fusion, equipped vehicles thus contribute to elaborate a global view of the physical environment.In this paper, we first describe a flexible end-to-end system simulator that can evaluate such cooperative mapping strategies in complex road driving environments. The overall simulation flow spans from sensor and V2X connectivity abstractions up to fusion algorithms running at the application level. We then present a few illustrating simulation results in a smart intersection scenario, confirming the importance of V2X-aided cooperation to enhance the physical perception of standalone vehicles in terms of both coverage and detection performances.
The use of fingerprinting localization techniques in outdoor IoT settings has started gaining popularity over the recent years. Communication signals of Low Power Wide Area Networks (LPWAN), such as LoRaWAN, are used to estimate the location of low power mobile devices. In this study, a publicly available dataset of LoRaWAN RSSI measurements is utilized to compare different machine learning methods and their accuracy in producing location estimates. The tested methods are: the k Nearest Neighbours method, the Extra Trees method and a neural network approach using a Multilayer Perceptron. To facilitate the reproducibility of tests and the comparability of results, the code and the train/validation/test split of the dataset used in this study have become available. The neural network approach was the method with the highest accuracy, achieving a mean error of 357 meters and a median error of 206 meters.
Wireless synchronization of industrial controllers is a challenging task in environments where wired solutions are not practical. The best solutions proposed so far to solve this problem require pretty expensive and highly specialized FPGA-based devices. With this work we counter the trend by introducing a straightforward approach to synchronize a fairly cheap IEEE 802.11 integrated wireless chip (IWC) with external devices. More specifically we demonstrate how we can reprogram the software running in the 802.11 IWC of the Raspberry Pi 3B and transform the receiver input potential of the wireless transceiver into a triggering signal for an external inexpensive FPGA. Experimental results show a mean-square synchronization error of less than 496 ns, while the absolute synchronization error does not exceed 6 μs. The jitter of the output signal that we obtain after synchronizing the clock of the external device did not exceed 5.2 μs throughout the whole measurement campaign. Even though we do not score new records in term of accuracy, we do in terms of complexity, cost, and availability of the required components: all these factors make the proposed technique a very promising of the deployment of large-scale low-cost automation solutions.
Release 9 specification of the Third Generation Partnership Project (3GPP) provided support to Observed Time Difference of Arrival (OTDOA) based positioning method for UE-Assisted mode using Long Term Evolution (LTE) Radio Access Technology (RAT). Evolving to release 16 specification it has been agreed by 3GPP consortium also to support UE-Based New Radio (NR), popularly known as 5G RAT dependent positioning method. UE-Based positioning methods will enable the device to calculate its location by using the Assistance Data (AD) that are transmitted by the Network. The AD may need to be broadcast and since the broadcast resource is constraint in terms of resources and physical size limit of broadcast channel, the AD should be selected carefully. The selection of data in the AD delivery would play a crucial role for the UE to calculate the location with high accuracy. In order to obtain high precision accuracy, the geometry in terms of UE location and the selection of beams would play an important role in NR. Further NR is expected to operate in high bandwidth spectrum where beam based transmission would be more common. Several beams may have to be used to get a focused coverage and it should be directed in various angles. Network should carefully select which beams to be used for the Position Reference Signal (PRS) transmission in order to mitigate interference and optimize the resources. The traditional Geometric Dilution of Precision (GDOP) metric which is based upon geometry between UE and the Transmission Points (TRPs) may not be sufficient as the beams may have different tilt/orientation. In this paper, we discuss the architecture and signaling of UE-Based DL RAT dependent OTDOA Positioning method. We exploit the NR Beam based angular information in selecting the cell sector or beams to be used for location calculation. We propose a new metric for the beam selection for the PRS transmission.
With the widespread use of location-based services and the development of localization systems, user's locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users' location privacy and provide location based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users' true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.
Accurate localization is required to enable location awareness of devices in many IoT applications. Current state-of-the-art ultra-wideband solutions make use of ranging measurements in a multi-anchor setup. In this paper, we propose a novel single-anchor scheme (E-SALDAT): an improvement of the Dual Wireless Radio Localization (DWRL). While four ranging measurements were previously required for semi-localization (usually, the first step towards definite localization/rigid-localization), E-SALDAT allows for semi-localization with only two ranging measurements. This results in a significant improvement of overall power efficiency and localization ratio. We present a comparison of the proposed localization scheme E-SALDAT to its predecessors I-DWRL and DWRL. The comparison was performed based on simulations. Our results suggest that E-SALDAT, on average, can achieve double the localization ratio of I-DWRL with just 23% more messages.
The paper introduces the periodic ambiguity function (PAF) of the preamble code as a tool in designing periodic synchronization preamble. It is shown that in the considered low-complexity receiver architecture characteristics of the PAF, namely, sidelobes peak level and main lobe phase, play an important role in the quality of the channel estimation. Furthermore, it is shown that these PAF characteristics vary considerably with the circular shift of the preamble code and care should be taken when selecting both preamble codes and corresponding circular shifts. This is done on an example of the preamble design for high pulse repetition frequency (HPRF) mode of the upcoming IEEE 802.15.4z standard amendment for enhanced ultra wideband (UWB) physical layers (PHYs) and associated ranging techniques. Similar analysis is carried-out on the preamble codes already specified in the high-rate pulse repetition frequency (HRP) UWB PHY of the IEEE 802.15.4-2015 standard.
Internet of Things (IoT) has been scaling up over the last few years in multiple applications and due to the need for geolocation and tracking capabilities, the usage of traditional Time Difference of Arrival (TDOA) arises. In this paper, a novel methodology for localizing using TDoA is presented, after the detailed and complete description of the TDoA has been provided. This proposed method depends on the hyperbolic functions to localize the node on a hyperbola, rather than locating it in a free position in the space potentially suffering from the influence of the timestamp imperfections. Thus, the proposed approach is finding this location on a hyperbola at a point which has the minimum Euclidean distance to all the other hyperbolas. A comparison is performed investigating the attainable accuracies for localizing based on this parametric TDoA and the classical TDoA method, on a well-defined simulation environment. The simulator is based on a Poisson distribution approach for defining the gateways and the node topology, as well as a noise model for emulating the oscillator drift at the gateways. In the given results, the feasibility of the proposed technique is asserted by a drastic improvement over a wide range of drift variances and the number of gateways. This manifests the robustness of the contributed method to the outlier timestamps and its optimum rendering, especially when the number of gateways is expected to be increased in the future.
Indoor positioning is gaining increasing attention in the industrial context to track vehicle, robot, or operator motions. The availability of low-cost ultra-wideband RF localization chips facilitates highly accurate 3D position estimation even in challenging metallic environments. In this work, we present an indoor positioning setup based on the Decawave DW1000 chip and 3D estimation results in a dynamic industrial robotic scenario. A performance analysis of achieved estimation accuracies from two different modes of operation is compared to the theoretical limits given by the Cramér-Rao lower bound.
