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Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems.

Yin Z, Cui K, Wu Z, Yin L - Sensors (Basel) (2015)

Bottom Line: The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease.The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error.The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. yinzhendong@hit.edu.cn.

ABSTRACT
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

No MeSH data available.


Related in: MedlinePlus

Connection between threshold factor and ranging error.
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sensors-15-11701-f011: Connection between threshold factor and ranging error.

Mentions: There is no direct connection between threshold factor and ranging error, but, as seen in Figure 3, the ranging error is relatively low at some specific threshold factor values. In order to explain this phenomenon, first, the connection between the threshold factor and the ranging error is investigated, as shown in Figure A1.


Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems.

Yin Z, Cui K, Wu Z, Yin L - Sensors (Basel) (2015)

Connection between threshold factor and ranging error.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481902&req=5

sensors-15-11701-f011: Connection between threshold factor and ranging error.
Mentions: There is no direct connection between threshold factor and ranging error, but, as seen in Figure 3, the ranging error is relatively low at some specific threshold factor values. In order to explain this phenomenon, first, the connection between the threshold factor and the ranging error is investigated, as shown in Figure A1.

Bottom Line: The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease.The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error.The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. yinzhendong@hit.edu.cn.

ABSTRACT
The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

No MeSH data available.


Related in: MedlinePlus