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.
