Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments

Khodjaev, Jasurbek; Park, Yongwan; Malik, Aamir Saeed

doi:10.1007/s12243-009-0124-z

Cited by 180 publications

(107 citation statements)

References 23 publications

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“…The NLOS challenge is divided mainly into two parts [8], e.g., channel identification and error mitigation. In the first category, and the received signal is identified and, if corrupted by NLOS error, is discarded.…”

Section: State-of-the-art In Nlos Indoor Localisationmentioning

confidence: 99%

“…Otherwise, it is used for position calculations. The latter analyses error mitigation caused by NLOS signal propagation, and numerous research works have been published in this area [8]. The NLOS error mitigation techniques are further subdivided into subcategories.…”

Section: State-of-the-art In Nlos Indoor Localisationmentioning

confidence: 99%

“…The extra distance is the main cause of NLOS error and is difficult to quantify due to multipath effects. NLOS error mitigation is one of the most discussed topics in localisation systems and a significant number of research works have been carried out to address it [3,8]. As NLOS error mitigation is a fundamental localisation problem, it still represents an open challenge.…”

Section: Introductionmentioning

confidence: 99%

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Improved Nlos Error Mitigation Based on LTS Algorithm

Khodjaev¹,

Tedesco²,

O’Flynn³

2016

PIER Letters

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Abstract-A new improved Least Trimmed Squares (LTS) based algorithm for Non-line-of-sight (NLOS) error mitigation is proposed for indoor localisation systems. The conventional LTS algorithm has hard threshold to decide the final set of base stations (BSs) to be used in position calculations. When the number of Line of Sight (LOS) BSs is more than the number of NLOS BSs the conventional LTS algorithm does not include some of them in position estimation due to principle of LTS algorithm or under heavy NLOS environments it cannot separate least biased BSs to use. To improve the performance of the conventional LTS algorithm in dynamic environments we have proposed a method that selects BSs for position calculation based on ordered residuals without discarding half of the BSs. By choosing a set of BSs which have least residual errors among all combinations as a final set for position calculation, we were able to decrease the localisation error of the system in dynamic environments. We demonstrate the robustness of the new improved method based on computer simulations under realistic channel environments.

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Section: State-of-the-art In Nlos Indoor Localisationmentioning

confidence: 99%

Section: State-of-the-art In Nlos Indoor Localisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Nlos Error Mitigation Based on LTS Algorithm

Khodjaev¹,

Tedesco²,

O’Flynn³

2016

PIER Letters

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“…Generally, the result of median and LULU are comparable for different impulse noise densities. In future, we plan to test LULU filters for various applications like 3D recovery [10], microscopic applications [11], communication [12], medical imaging [13], [14], etc.…”

Section: =[I(ij-1) I(ij) I(i+1j-1) I(i+1j)]mentioning

confidence: 99%

Comparison of LULU and Median Filter for Image Denoising

Rahmat¹,

Malik²,

Kamel³

2013

IJCEE

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Abstract-This paper presents a comparison between LULU and Median filters for impulse noise in images. Noise removal from images is always a challenging area of research. Different methods are being used for different image noises such as Wiener filter for Gaussian noise, Frost filter for speckle noise and median filter for impulse noise. LULU filters are widely being used for impulse noise too. LULU filters are nonlinear rank selector operators which are computationally more competent and the performance of the operator is straightforward to describe. The comparison between Median and LULU filters, in this paper, is performed using two different image quality metrics which are RMSE and PSNR.Index Terms-LULU filters, impulse noise, RMSE, PSNR. I. INTRODUCTIONIn the field of image processing, there are so many issues and complexities for the recovery of original data from the noise corrupted data. In general, image noise is the variation of color or brightness intensity which can be caused by different sources. In general, image sensors degrade the quality of the images. Faulty devices, troubles with the data collection procedure, and interfering natural cause can all corrupt the data. Moreover, noise can be caused by compression and transmission errors. Thus, noise removal is required and is the first step before images are analyzed. It is necessary to apply an efficient noise reduction method to compensate for such data corruption [1], [2]. Even though there are so many different methodologies in this field for noise removal, image denoising is still a big issue. Some noise filters cause blurring of the images, because in reality the nature of the noise is unknown or only known to some extent.Impulse noise results due to the malfunctioning of the image sensors and the transmission channels. It can be identified by black and white points in the image. So far, there are so many different algorithms that had been proposed to filter this type of noise such as median, switching median, weighted median, adaptive median, rank conditioned median, separable median and contra harmonic mean filter. There are different names for impulse noise which are salt and pepper as well as shot noise.In this work, we address impulse noise. Generally, median filter is used for reducing impulse noise. We compared four different LULU filters with median filter. This comparison Manuscript received April 20, 2013; revised June 28, 2013 It is extensively applied in digital image processing due to its property of protecting edges as well as at the same time eliminating noise but it causes the image to blur. But still Median filter causes significantly a smaller amount of blurring than linear smooth filters of the same size. In general, all Median filters produce reasonable results for both bipolar and unipolar impulse noise. They are nonlinear, non-idempotent smoother which is computationally less efficient than LULU. A. 1D MedianIn one dimensional arrays, median filter is given by:The Fig. 2 illustrates the median operator in filtering a s...

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“…To mitigate the influence of NLOS and hence improve positioning accuracy, different methods have been proposed in the literature for NLOS identification and error mitigation. In [1] the main NLOS identification techniques are classified into three categories, namely range estimation-based methods, channel statistics-based methods and position estimation-based methods. Meanwhile, NLOS error mitigation techniques are divided into statistics-based methods and direct path estimation-based methods.…”

Section: Introductionmentioning

confidence: 99%

NLOS identification and compensation for UWB ranging based on obstruction classification

Wen

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Non-line-of-sight (NLOS) propagation is one of the major barriers to accurate ranging and positioning based on time of arrival (TOA) in the application of an ultra wideband (UWB) system. This paper proposes a new method for NLOS identification and mitigation based on signal characteristic analysis and fuzzy theory. This method neither requires to build a statistical model nor to create and update a training database, so that it can be used conveniently for different application scenarios. Extensive experiments were conducted and the results show that the cumulative distribution function of the ranging error below 0.5 meter is over 90% when using the proposed mitigation method, while that without using the mitigation method is below 70%. Also, by using the proposed method, the root mean square error (RMSE) of the range measurements is reduced from 0.77 to 0.33 meter. The results demonstrate that this method can effectively identify NLOS and mitigate the NLOS-induced ranging error.

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Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments

Cited by 180 publications

References 23 publications

Improved Nlos Error Mitigation Based on LTS Algorithm

Improved Nlos Error Mitigation Based on LTS Algorithm

Comparison of LULU and Median Filter for Image Denoising

NLOS identification and compensation for UWB ranging based on obstruction classification

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