@InProceedings{8902949,
  author = {V. Barral and C. J. Escudero and J. A. García-Naya},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {NLOS Classification Based on RSS and Ranging Statistics Obtained from Low-Cost UWB Devices},
  year = {2019},
  pages = {1-5},
  abstract = {Ultra-wideband (UWB) devices have been largely considered for indoor location systems due to their high accuracy. However, as in other wireless systems, such accuracy is significantly degraded under non-line-of-sight (NLOS) propagation conditions. Therefore, the identification of NLOS conditions is essential to mitigate inaccuracies due to NLOS propagation. Nonetheless, most of the techniques considered to identify NLOS situations are based on the study of the channel impulse response (CIR), which is not practical and even becomes unfeasible when employing low-cost UWB hardware. This is precisely the main motivation of this work, to introduce a classification system based on the statistics of both the received signal strength (RSS) and range available from low-cost UWB devices. We analyze the effect of considering different statistic sets of both the RSS and range as features to feed a support vector machine (SVM) classifier, which is experimentally evaluated by means of measurements carried out in a real scenario where both line-of-sight (LOS) and NLOS conditions are present.},
  keywords = {statistical analysis;support vector machines;telecommunication computing;ultra wideband communication;wireless channels;LOS;SVM classifier;support vector machine classifier;received signal strength;CIR;channel impulse response;RSS;ultrawideband hardware devices;UWB hardware devices;statistic setting;NLOS classification system;NLOS propagation conditions;nonline-of-sight propagation conditions;wireless systems;indoor location systems;Support vector machines;Hardware;Receivers;Estimation;Distance measurement;Machine learning algorithms;Monitoring;Ultra-wideband;NLOS Classifi;cation;RSS;ranging;SVM},
  doi = {10.23919/EUSIPCO.2019.8902949},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532534.pdf},
}

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However, as in other wireless systems, such accuracy is significantly degraded under non-line-of-sight (NLOS) propagation conditions. Therefore, the identification of NLOS conditions is essential to mitigate inaccuracies due to NLOS propagation. Nonetheless, most of the techniques considered to identify NLOS situations are based on the study of the channel impulse response (CIR), which is not practical and even becomes unfeasible when employing low-cost UWB hardware. This is precisely the main motivation of this work, to introduce a classification system based on the statistics of both the received signal strength (RSS) and range available from low-cost UWB devices. We analyze the effect of considering different statistic sets of both the RSS and range as features to feed a support vector machine (SVM) classifier, which is experimentally evaluated by means of measurements carried out in a real scenario where both line-of-sight (LOS) and NLOS conditions are present.","keywords":"statistical analysis;support vector machines;telecommunication computing;ultra wideband communication;wireless channels;LOS;SVM classifier;support vector machine classifier;received signal strength;CIR;channel impulse response;RSS;ultrawideband hardware devices;UWB hardware devices;statistic setting;NLOS classification system;NLOS propagation conditions;nonline-of-sight propagation conditions;wireless systems;indoor location systems;Support vector machines;Hardware;Receivers;Estimation;Distance measurement;Machine learning algorithms;Monitoring;Ultra-wideband;NLOS Classifi;cation;RSS;ranging;SVM","doi":"10.23919/EUSIPCO.2019.8902949","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532534.pdf","bibtex":"@InProceedings{8902949,\n author = {V. Barral and C. J. Escudero and J. A. García-Naya},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {NLOS Classification Based on RSS and Ranging Statistics Obtained from Low-Cost UWB Devices},\n year = {2019},\n pages = {1-5},\n abstract = {Ultra-wideband (UWB) devices have been largely considered for indoor location systems due to their high accuracy. However, as in other wireless systems, such accuracy is significantly degraded under non-line-of-sight (NLOS) propagation conditions. Therefore, the identification of NLOS conditions is essential to mitigate inaccuracies due to NLOS propagation. Nonetheless, most of the techniques considered to identify NLOS situations are based on the study of the channel impulse response (CIR), which is not practical and even becomes unfeasible when employing low-cost UWB hardware. This is precisely the main motivation of this work, to introduce a classification system based on the statistics of both the received signal strength (RSS) and range available from low-cost UWB devices. We analyze the effect of considering different statistic sets of both the RSS and range as features to feed a support vector machine (SVM) classifier, which is experimentally evaluated by means of measurements carried out in a real scenario where both line-of-sight (LOS) and NLOS conditions are present.},\n keywords = {statistical analysis;support vector machines;telecommunication computing;ultra wideband communication;wireless channels;LOS;SVM classifier;support vector machine classifier;received signal strength;CIR;channel impulse response;RSS;ultrawideband hardware devices;UWB hardware devices;statistic setting;NLOS classification system;NLOS propagation conditions;nonline-of-sight propagation conditions;wireless systems;indoor location systems;Support vector machines;Hardware;Receivers;Estimation;Distance measurement;Machine learning algorithms;Monitoring;Ultra-wideband;NLOS Classifi;cation;RSS;ranging;SVM},\n doi = {10.23919/EUSIPCO.2019.8902949},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532534.pdf},\n}\n\n","author_short":["Barral, V.","Escudero, C. 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