LOS/NLOS detection for UWB signals: A comparative study using experimental data

Decarli, Nicolò; Dardari, Davide; Gezici, Sinan; D’Amico, Antonio A.

doi:10.1109/iswpc.2010.5483704

Cited by 41 publications

(25 citation statements)

References 19 publications

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“…To distinguish the direct and indirect signal paths, the existing approaches [36][37][38] are mainly based on the criterion that the direct signal path is corresponding to the smallest TOA measurement. However, these approaches are limited in practical use due to the problems of the large number of indirect signal paths in indoor environments, as well as the difficulty of guaranteeing the time synchronization between the Wi-Fi APs and receiver.…”

Section: Signal Path Identificationmentioning

confidence: 99%

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

et al. 2016

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Abstract:In recent decades, the proliferation of smart phones, tablets, and wireless networks has fostered a growing interest in indoor passive positioning. The Wi-Fi-based passive positioning systems can provide Location-Based Services (LBSs) to the third party such as market and security departments. Most of the existing systems are based on the Receive Signal Strength Indication (RSSI) information, which are generally time-consuming and susceptible to environmental change. To overcome this problem, we propose the Wi-Fi compass for indoor passive positioning system (WIPP), an Angle of Arrival (AOA) based passive positioning system using the existing commodity Wi-Fi network. In this paper, we first propose a new algorithm for the joint estimation of AOA and Time of Arrival (TOA) measurements based on the fine-grained Channel State Information (CSI), which is collected by an off-the-shelf Wi-Fi device equipped with only three antennas. Second, we use the affinity propagation clustering algorithm to identify the direct signal path from the target to each Wi-Fi Access Point (AP). Finally, we deploy the WIPP in an actual indoor environment to conduct the performance comparison with the well-known radio-frequency (RF) based system for locating and tracking users inside buildings (RADAR), as well as the conventional passive positioning system using the AOA solely. The experimental results show that the WIPP is able to achieve the median positioning error 0.7 m, which is much lower than the ones by the RADAR system and the conventional system using the AOA solely.

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Section: Signal Path Identificationmentioning

confidence: 99%

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

et al. 2016

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“…2 shows the simulation layout: 12 anchors are located evenly on a circle with a radius of The range measurement error definitions described in [18] (Type I to IV errors) are adopted here. When there is a severe NLOS offset (Type III and IV errors), we assume that NLOS detection and mitigation [1], [16]- [19] are applied, leaving small residual NLOS errors in the range measurements. This effectively results in Type I and II errors [see the model described by (2)].…”

Section: Simulationmentioning

confidence: 99%

“…During the range measurement estimation phase, NLOS links may be detected [16], [17] and severe NLOS links that do not contain much useful range information are discarded. Otherwise, NLOS mitigation methods [1], [18], [19] can be applied to improve the performance. After NLOS detection and mitigation, it is reasonable to assume that the residual NLOS offset is sufficiently small, allowing it to be merged with the Gaussian range error term.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Expectation Maximization Estimator for TDOA Localization

Qiao

Zhang

Liu

2014

IEEE Wireless Commun. Lett.

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Time-difference-of-arrival (TDOA) techniques are widely used in high-accuracy positioning systems. The weighted nonlinear least squares (NLLS) algorithm can be used in such systems to estimate the target position. If the range measurement errors can be modeled as additive white Gaussian noise (AWGN), the performance of the weighted NLLS algorithm approaches the Cramér-Rao lower bound (CRLB). However, TDOA with weighted NLLS is complex to implement because the covariance matrix of the range measurements is non-diagonal, which requires matrix multiplication and inversion. In this paper, we develop a low-complexity nonlinear expectation maximization localization algorithm. The proposed algorithm is much simpler to implement than the weighted NLLS method since no matrix manipulation is required, while their performances are similar, both approaching the CRLB.

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“…Most of the previous research in NLOS detection/mitigation for UWB use the simulated channel model to validate algorithms; although there are some exceptions that use real data [11], [12], [16].…”

Section: Introductionmentioning

confidence: 99%

A robust UWB indoor positioning system for highly complex environments

García

Poudereux

Hernández

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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Ultra-Wideband (UWB) has a high interest in research and industry for accurate indoor positioning. This technology comprises signals with a bandwidth of at least 500 MHz at a power decay of -10dB. This large bandwidth conferes the capability of resolving multipath, penetrating through obstacles and accurate ranging. However, NLOS (Non-Line-Of-Sight) conditions produced by obstacles in indoor environments severely degrade performance, as they introduce a positive bias in ranging estimation. In this paper we present a robust UWB indoor positioning which is able to accurately operate in a highly complex indoor scenario, where NLOS condition is predominant. For that purpose, the system uses a NLOS detection algorithm based on the skewness of the estimated channel impulse response and it mitigates NLOS by using an Extended Kalman Filter. The use of this detection/mitigation algorithm shows an improvement of the RMSE in positioning about 67%.

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LOS/NLOS detection for UWB signals: A comparative study using experimental data

Cited by 41 publications

References 19 publications

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy

Nonlinear Expectation Maximization Estimator for TDOA Localization

A robust UWB indoor positioning system for highly complex environments

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