Analysis of Kurtosis-Based LOS/NLOS Identification Using Indoor MIMO Channel Measurement

Zhang, Jie; Salmi, Jussi; Lohan, Elena Simona

doi:10.1109/tvt.2013.2249121

Cited by 55 publications

(36 citation statements)

References 9 publications

(8 reference statements)

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“…We also note an increasing tendency with i, since the higher principal components include little information (i.e., have small eigenvalues). Therefore, it is reasonable to use the first M components as described in Section IV-C. 9 In Fig. 5b, we can see that using kPCA for NLOS identification, instead of the approach based on rise time, can significantly reduce the misclassification rate with only M ≥ 3 principal components (we choose M = 4 for further analyses).…”

Section: B Model Selectionmentioning

confidence: 99%

Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity

Savic

Larsson

Ferrer-Coll

et al. 2016

IEEE Trans. Wireless Commun.

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Abstract-Accurate and robust positioning in multipath environments can enable many applications, such as search-andrescue and asset tracking. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, UWB still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival (TOA) will typically be positively biased. There are many techniques that address this problem, mainly based on NLOS identification and NLOS error mitigation algorithms. However, these techniques do not exploit all available information in the UWB channel impulse response. Kernel-based machine learning methods, such as Gaussian Process Regression (GPR), are able to make use of all information, but they may be too complex in their original form. In this paper, we propose novel ranging methods based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto a nonlinear orthogonal high-dimensional space, and a subset of these projections is then used as an input for ranging. We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel, and found that one of the proposed methods is able to outperform state-of-the-art, even if little training samples are available.

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Section: B Model Selectionmentioning

confidence: 99%

Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity

Savic

Larsson

Ferrer-Coll

et al. 2016

IEEE Trans. Wireless Commun.

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show abstract

“…• DDM-1: The linear DDM of [20], where the distance depending factor is ; • DDM-2: The NLOS error satisfies (16) where and represent the distance depending factor and the jitter respectively. Obviously, a smaller results in a closer approximation to the linear DDM and thus a higher localization accuracy.…”

Section: Simulations and Analysismentioning

confidence: 99%

“…The latter indeed represents the NLOS severity in an averaged meaning. Second, suitable choices of and the random number may lead to a small NLOS error in (16) with high probabilities, i.e., and . Here and represent two rand numbers generated independently.…”

Section: B Performance For the Nlos Ddm-2mentioning

confidence: 99%

“…Thus the above algorithms cannot be expected to be effective in most situations. On the other hand, Marano et al [15] and Zhang et al [16] proposed to use the multipath channel information to identify the NLOS measurement, where the statistics of channel (such as Kurtosis) were analyzed according to the channel measurements. Unfortunately, these two position estimators may also be sensitive to scattering environments and require a great many real-world measurements to cover a wide range of wireless environments.…”

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confidence: 99%

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Accurate and Simple Wireless Localizations Based on Time Product of Arrival in the DDM-NLOS Propagation Environment

Hua

Meng

Zhou

et al. 2015

IEEE J. Sel. Top. Signal Process.

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Wireless localization has become an important issue in internet of things. However, its performance degrades significantly when there are only non-line-of-sight (NLOS) propagation paths instead of line-of-sight (LOS) propagation paths. Therefore, this paper proposes a novel localization algorithm to improve the localization accuracy, where we assume that the NLOS error satisfies the requirement of distance dependent model (DDM). In order to obtain a stronger NLOS tolerance than conventional methods, the proposed algorithm analyzes the localization geometry and employs a new localization parameter, namely the time product of arrival (TPOA), to construct the linear localization equation group (LLEQ). Meanwhile, a simple least-squares (LS) principle is applied to solve this LLEQ, resulting in a low complexity. We verify our algorithm by computer simulations, and the result demonstrates that the proposed algorithm yields a better performance than traditional algorithms in the NLOS-DDM environment.Index Terms-Distance dependent model, non-line-of-sight error, time product of arrival, wireless localization.

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“…They found that RMS delay spread is the most useful for this problem, but the combination of these three parameters can improve the probability of correct identification. In [7], the authors found that the kurtosis provides consistent information about NLOS condition, and that using multiple antennas can improve this information. In [8], multiple parameters are considered by using a nonparametric least-square support-vector-machine (LS-SVM) classifier.…”

Section: Related Workmentioning

confidence: 99%

Kernel principal component analysis for UWB-based ranging

Savic

Larsson

Ferrer-Coll

et al. 2014

2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Abstract-Accurate positioning in harsh environments can enable many application, such as search-and-rescue in emergency situations. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, it still faces a problem in non-line-of-sight (NLOS) environments, in which range estimates based on time-of-arrival (TOA) are positively biased. There are many techniques that try to address this problem, mainly based on NLOS identification and NLOS error mitigation. However, these techniques do not exploit all available information from the UWB channel impulse response. In this paper, we propose a novel ranging technique based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto nonlinear orthogonal highdimensional space, and a subset of these projections is then used for ranging. We tested this technique using UWB measurements obtained in a basement tunnel of Linköping university, and found that it provides much better ranging performance comparing with standard techniques based on PCA and TOA.

show abstract

Analysis of Kurtosis-Based LOS/NLOS Identification Using Indoor MIMO Channel Measurement

Cited by 55 publications

References 9 publications

Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity

Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity

Accurate and Simple Wireless Localizations Based on Time Product of Arrival in the DDM-NLOS Propagation Environment

Kernel principal component analysis for UWB-based ranging

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