This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters which are time synchronized to the physical transmitter and static in their positions. Contrarily to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously, hence, the approach does not require any prior information such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, the paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.
Positioning is next to communication the most important field of applications for wireless radio transmissions. This paper considers indoor positioning using wireless signals. Especially in indoor scenarios, multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. Strategies to mitigate multipath effects on range estimates are in general based on the estimation of the channel impulse response (CIR). All these methods have in common that they determine the CIR in order to remove the influence on the estimate of the line-of-sight path delay. This paper focuses on multipath aided positioning by using the time difference of arrival between multipath components (TDoAbMC). Hence, the paper uses the multipath propagation of the wireless signal to allow positioning in cases of a insufficient number of transmitters or increase the accuracy otherwise. Measurements with a moving receive antenna showed, that multipath components are visible for several meters of receiver movement. To estimate and track the time-variant multipath components of the received signal, the paper uses a Kalman filter which utilizes maximum likelihood estimates as measurements. For positioning, the novel approach treats multipath components as signals from virtual transmitters which are time synchronized to the physical transmitter and fixed in their position. Additionally, using a time difference of arrival approach, the estimation of the user clock bias is not necessary. To use the information of the multipath components, the positioning algorithm has to estimate the user position and the position of the virtual transmitters simultaneously. Furthermore, the new approach does not rely on any prior information such as the room layout or a database for fingerprinting.
A multi-mode antenna (MMA) can be an interesting alternative to a conventional phased antenna array for directionof-arrival (DoA) estimation. By MMA we mean a single physical radiator with multiple ports, which excite different characteristic modes. In contrast to phased arrays, a closed-form mathematical model of the antenna response, like a steering vector, is not straightforward to define for MMAs. Instead one has to rely on calibration measurement or electromagnetic field (EMF) simulation data, which is discrete. To perform DoA estimation, an array interpolation technique (AIT) and wavefield modeling (WM) are suggested as methods with inherent interpolation capabilities, fully taking antenna nonidealities like mutual coupling into account. We present a non-coherent DoA estimator for low-cost receivers and show how coherent DoA estimation and joint DoA and polarization estimation can be performed with MMAs. Utilizing these methods, we assess the DoA estimation performance of an MMA prototype in simulations for both 2D and 3D cases. The results show that WM outperforms AIT for high SNR. The coherent estimation is superior to non-coherent, especially in 3D, because non-coherent suffers from estimation ambiguities. In conclusion, DoA estimation with a single MMA is feasible and accurate.
This paper extends an algorithm that exploits multipath propagation for position estimation of mobile receivers named Channel-SLAM. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) and estimates the positions of the VTs simultaneously with the mobile receiver positions. For Channel-SLAM it is essential to obtain angle of arrival (AoA) measurements for each MPC in order to estimate the VT positions. In this paper, we propose a novel Channel-SLAM implementation based on particle filtering which fuses heading information of an inertial measurement unit (IMU) to omit AoA measurements and to improve the position accuracy. Interpreting all MPCs as signals originated from VTs, Channel-SLAM enables positioning also in non-line-of-sight situations. Furthermore, we propose a method to dynamically adapt the number of particles which significantly reduces the computational complexity. A posterior Cramér-Rao lower bound for Channel-SLAM is derived which incorporates the heading information of the inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a single fixed transmitter and a moving pedestrian carrying the receiver and the IMU. The evaluations show that accurate position estimation is possible without the knowledge of the physical transmitter position by exploiting MPCs and the heading information of an IMU.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.