Adaptive NLOS Mitigation Location Algorithm in Wireless Cellular Network

Wu, Shixun; Xu, Dengyuan; Wang, Honggang

doi:10.1007/s11277-015-2791-6

Cited by 12 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following [2], in this paper we take a first look at the potential of using LIS for terminal-positioning, where we assume that a terminal to be positioned is equipped with a single-antenna and located in a three-dimensional space in front of the LIS. For analytical tractability, although we do not deal with more complicated geometries, our results are fundamental in the sense that positioning of objects in rich scattering environments [10], [11] can be done in two steps: i) estimating the positions of a number of reflecting objects in the environment with line-of-sight (LoS) propagation to the LIS, and ii) backward computation of the position of the object of interest. Thus, the results obtained in this work are instrumental for understanding the accuracy in the first step for positioning with scatters.…”

Section: Introductionmentioning

confidence: 99%

Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces

Rusek

Edfors

2018

IEEE Trans. Signal Process.

327

204

View full text Add to dashboard Cite

We consider the potential for positioning with a system where antenna arrays are deployed as a large intelligent surface (LIS), which is a newly proposed concept beyond massive-MIMO where future man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". In a first step, we derive Fisher-information and Cramér-Rao lower bounds (CRLBs) in closed-form for positioning a terminal located perpendicular to the center of the LIS, whose location we refer to as being on the central perpendicular line (CPL) of the LIS. For a terminal that is not on the CPL, closed-form expressions of the Fisher-information and CRLB seem out of reach, and we alternatively find approximations of them which are shown to be accurate. Under mild conditions, we show that the CRLB for all three Cartesian dimensions (x, y and z) decreases quadratically in the surface-area of the LIS, except for a terminal exactly on the CPL where the CRLB for the z-dimension (distance from the LIS) decreases linearly in the same. In a second step, we analyze the CRLB for positioning when there is an unknown phase ϕ presented in the analog circuits of the LIS. We then show that the CRLBs are dramatically increased for all three dimensions but decrease in the third-order of the surface-area. Moreover, with an infinitely large LIS the CRLB for the z-dimension with an unknown ϕ is 6 dB higher than the case without phase uncertainty, and the CRLB for estimating ϕ converges to a constant that is independent of the wavelength λ. At last, we extensively discuss the impact of centralized and distributed deployments of LIS, and show that a distributed deployment of LIS can enlarge the coverage for terminal-positioning and improve the overall positioning performance. Large intelligent surface (LIS), massive-MIMO, Fisher-information, Cramér-Rao lower bound (CRLB), terminal-positioning, central perpendicular line (CPL), arrive-of-angle (AoA), surface-area, phase-uncertainty. I. INTRODUCTION Wireless communication has evolved from few and geographically distant base stations to more recent concepts involving a high density of access points, possibly with many antenna elements on each. A Large Intelligent Surface (LIS) is a newly proposed concept in wireless communication that is envisioned in [2], where future man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". We foresee a practical implementation of LIS as a compact integration of a vast amount of tiny antenna-elements with reconfigurable processing networks. Antennas on the surface cooperate to transmit and sense signals, both for communication and other types of functionality. Machine learning [3] can bring intelligence in the systems both for autonomous operation of the system and for new functionality. One such application is depicted in Fig. 1, where three different terminals are communicating to LIS in an outdoor and indoor scenarios, respecti...

show abstract

Section: Introductionmentioning

confidence: 99%

Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces

Rusek

Edfors

2018

IEEE Trans. Signal Process.

327

204

View full text Add to dashboard Cite

show abstract

“…e broadcasting base station in digital broadcasting is the basic unit of terrestrial broadcasting supplementary system. It broadcasts mobile multimedia data and undertakes the task of transmitting spread spectrum positioning signal [7,8].…”

Section: Principle Of Digital Broadcasting Positioning Systemmentioning

confidence: 99%

Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm

Qiu

2021

Complexity

View full text Add to dashboard Cite

In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network (ANN) is proposed for NLOS positioning error caused by non-line-of-sight (NLOS) propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training algorithm based on extended Kalman (EKF). Combined with the statistical characteristics of NLOS error, the state component NLOS bias estimation is modified to realize TDOA data reconstruction. Simulation and experimental data analysis show that the algorithm can effectively weaken the influence of NLOS error. The localization method does not depend on the specific NLOS error distribution, nor does it need LOS and NLOS recognition. It can significantly improve the mobile positioning accuracy.

show abstract

“…From the viewpoint of location accuracy, the most crucial aspect is the correction mechanism used due to propagation conditions. It should be emphasized that the majority of solutions available in the literature for NLOS conditions are based on the theoretical error distributions of measured parameters, e.g., [38,39], instead of using more realistic methods of channel modeling. In the case of mobile sensors and emitters, the time-variant channel models should be used, e.g., [40,41].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions

Kelner

Ziółkowski

2020

Sensors

View full text Add to dashboard Cite

This paper focused on assessing the effectiveness of the signal Doppler frequency (SDF) method to locate a mobile emitter using a swarm of unmanned aerial vehicles (UAVs). Based on simulation results, we showed the impact of various factors such as the number of UAVs, the movement parameters of the emitter and the sensors on location effectiveness. The study results also showed the dependence of the accuracy and continuity of the emitter coordinate estimation on the type of propagation environment, which was determined by line-of-sight (LOS) or non-LOS (NLOS) conditions. The applied research methodology allowed the selection of parameters of the analyzed location system that would minimize the error and maximize the monitoring time of the emitter position.

show abstract

Adaptive NLOS Mitigation Location Algorithm in Wireless Cellular Network

Cited by 12 publications

References 11 publications

Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces

Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces

Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm

Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions

Contact Info

Product

Resources

About