Classification of transmission environment in UWB communication using a support vector machine
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Impulse-radio(IR)-based Ultra-wide band (UWB) technology has great potential in the fields of positioning, target detection and data transfer due to its significant advantages. In this paper, the ability of UWB to perceive different transmission environment is discussed. Four scenarios are simulated using finite-difference time-domain (FDTD) method. There are three NLOS(obstacle are concrete wall, glass wall, wood wall respectively)scenarios and one LOS scenario. Representative parameters are extracted from the UWB's channel models, and are sent into a support vector machine(SVM) to classify those scenarios. The results show that these scenarios can be classified with proper SVM parameters.Keywords:
Non-line-of-sight propagation
Time of arrival
Non-Line of Sight (NLOS) error due to the blockage of direct paths has been considered as the major problem in the wireless localization. Therefore, this paper studies the influence on different algorithms caused by the same NLOS error in order to identify LOS base stations (BS) from NLOS BS. Specifically, Time of Arrival (TOA) based algorithm and Time Sum of arrival (TSOA) based algorithm are chosen to be the two different localization algorithms to detect the LOS BS in this paper. Simulations show that the proposed strategy presents superior performance in Dense NLOS environment.
Non-line-of-sight propagation
Time of arrival
Arrival time
Identification
Angle of arrival
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this study examined hybrid Time of Arrival/Angle of Arrival (TOA/AOA) localization technique in a cellular network. Based on the linearized equations from the TOA and AOA measurements, the weighted least square (WLS) method is proposed to obtain the location estimation of a mobile station (MS) by analyzing the statistical properties of the error vector in Line of Sight (LOS) and Non-line of Sight (NLOS) environments, respectively. Moreover, the precise expression of the Cramer-Rao lower bound (CRLB) for hybrid TOA/AOA measurements in different LOS/NLOS conditions was derived when the LOS error is a Gaussian variable and the NLOS error is an exponential variable. The idea of cooperative localization is proposed based on the additional information from short-range communication among the MSs in fourth generation (4G) cellular networks. Therefore, the proposed hybrid TOA/AOA WLS method can be improved further with the cooperative scheme. The simulation results show that the hybrid TOA/AOA method has better performance than the TOA only method, particularly when the AOA measurements are accurate. Moreover, the performance of the hybrid TOA/AOA method can be improved further by the cooperative scheme.
Non-line-of-sight propagation
Time of arrival
Angle of arrival
Cramér–Rao bound
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A single scatter based location method is introduced in the non-line-of-sight (NLOS) conditions. The proposed method requires the location parameters of time of arrival (TOA) and direction of arrival (DOA) of multipath signals and direction of departure (DOD) of source signal. Simulation results prove that the proposed method is effective.
Non-line-of-sight propagation
Time of arrival
Direction of arrival
Angle of arrival
SIGNAL (programming language)
Line-of-sight
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In this paper, we address the issue of mobile positioning and tracking after measurements have been made on the distances and possibly directions between an MS (mobile station) and its nearby base stations (BS's). The measurements can come from the time of arrival (TOA), the time sum of arrival (TSOA), the time difference of arrival (TDOA), and the angle of arrival (AOA). They are in general corrupted with measurement noise and NLOS (non-line-of-sight) error. The NLOS error is the dominant factor that degrades the accuracy of mobile positioning. Assuming specific statistic models for the NLOS error, however, we propose a scheme that significantly reduces its effect. Regardless of which of the first three measurement types (i.e. TOA, TSOA, or TDOA) is used, the proposed scheme computes the MS location in a mathematically unified way. We also propose a method to identify the TOA measurements that are not or only slightly corrupted with NLOS errors. We call them nearly NLOS-error-free TOA measurements. From the signals associated with TOA measurements, AOA information can be obtained and used to aid the MS positioning. Finally, by combining the proposed MS positioning method with Kalman filtering, we propose a scheme to track the movement of the MS.
Non-line-of-sight propagation
Time of arrival
Angle of arrival
Tracking (education)
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Non-line-of-sight propagation
Time of arrival
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Sensor localization is a basic and important task of wireless sensor networks, and abundant localization algorithms have been proposed based on various ranging techniques, including time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS), angle-of-arrival (AOA) and etc. The accuracy of these ranging techniques rely on the line-of-sight (LOS) propagation of signals, while in practical environments, signals may be propagated in non-line-of-sight (NLOS) channels, with the result that both ranging and localization accuracy can be seriously degraded. In this paper, we consider TOA-based sensor localization and propose a novel NLOS identification method using Cayley-Menger determinant (CMD). To be specific, the errors induced by NLOS in distance measurements are positive and much larger than errors from TOA systems, and as a result, the values of corresponding CMD behave differently under LOS/NLOS channels. Hence, we leverage this character to establish a statistical hypothesis testing model to identify NLOS channels. Finally, a simulation analysis is conducted to verify the effectiveness of the proposed method.
Non-line-of-sight propagation
Ranging
Time of arrival
Angle of arrival
RSS
Leverage (statistics)
Cramér–Rao bound
Identification
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A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Link\"{o}ping university, in which we obtained ultra-wideband (UWB) complex impulse responses for line-of-sight (LOS), and three non-LOS (NLOS) scenarios. This paper is focused on time-of-arrival (TOA) ranging since this technique can provide the most accurate range estimates, which are required for range-based positioning. We describe the measurement setup and procedure, select the threshold for TOA estimation, analyze the channel propagation parameters obtained from the power delay profile (PDP), and provide statistical model for ranging. According to our results, the rise-time should be used for NLOS identification, and the maximum excess delay should be used for NLOS error mitigation. However, the NLOS condition cannot be perfectly determined, so the distance likelihood has to be represented in a Gaussian mixture form. We also compared these results with measurements from a mine tunnel, and found a similar behavior.
Non-line-of-sight propagation
Ranging
Time of arrival
Power delay profile
Wideband
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In order to increase the time-of-arrival (TOA) estimation accuracy, it is often useful to exploit additional information, such as path attenuation or path loss, which can in principle be observed from the received signal strength (RSS). This chapter investigates the TOA estimation techniques for the hybrid RSS-TOA localization in mixed line-of-sight (LOS)/non-line-of-sight (NLOS) environments. It describes how the number of TOA measurements should be selected to reduce computation complexity while improving the localization performance. The chapter shows how to determine a mobile position from such sufficient TOAs while the error performance attains certain performance bounds. The results shared in the chapter help a system designer select the number of nodes that should be used in the process of localization and NLOS mitigation. The selection is based on a trade-off between accuracy and computational complexity.
Non-line-of-sight propagation
RSS
Time of arrival
Position (finance)
Sight
SIGNAL (programming language)
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Time-of-arrival (TOA) estimation and the inherent accuracy of the mobile position estimation have been considered for the wireless non-line-of-sight (NLOS) geolocation. To increase the TOA estimation accuracy, it is often useful to exploit additional information, such as path attenuation or path loss, which can in principle be observed from the received signal strength (RSS). This chapter investigates the TOA estimation techniques for the hybrid RSS-TOA localization in mixed line-of-sight (LOS)/NLOS environments. It explains how to choose a sufficient number of the TOAs so that the computation burden of the mobile position estimation can be reduced without any performance degradation. More importantly, the chapter shows how to determine a mobile position from such sufficient TOAs while the error performance attains certain performance bounds. The results shared in the chapter help a system designer to select the number of nodes that should be used in the process of localization and NLOS mitigation. Controlled Vocabulary Terms position measurement; time of arrival estimation
Non-line-of-sight propagation
Geolocation
RSS
Time of arrival
Position (finance)
Angle of arrival
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Maximum A Posteriori Approach to Time-of-Arrival-Based Localization in Non-Line-of-Sight Environment
A conventional approach to mobile positioning is to utilize the time-of-arrival (TOA) measurements between the mobile station (MS) and several receiving base stations (BSs). The TOA information defines a set of circular equations from which the MS position can be calculated with the known BS geometry. However, when the TOA measurements are obtained from the non-line-of-sight (NLOS) paths, the position estimation performance can be very unreliable. Assuming that the NLOS probability and distribution are known and the NLOS-induced error dominates the corresponding TOA measurement, two maximum a posteriori probability (MAP) algorithms for NLOS detection and MS localization are derived in this paper. The first provides a standard MAP solution, while the second is a simplified version based on geometric constraints. It is shown that the former achieves more accurate estimation performance at the expense of higher computational cost.
Non-line-of-sight propagation
Time of arrival
Position (finance)
Sight
Dilution of precision
Angle of arrival
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Citations (41)