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SCIENCE CHINA Information Sciences, Volume 62, Issue 10: 202301(2019) https://doi.org/10.1007/s11432-019-9909-5

Geometry-based non-line-of-sight error mitigation and localization in wireless communications

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  • ReceivedJan 9, 2019
  • AcceptedMay 31, 2019
  • PublishedAug 26, 2019

Abstract

Recently, positioning services have received considerable attention. The primary source of the positioning error is non-line-of-sight (NLOS) propagation. To address this problem, we propose a novel NLOS mitigation scheme, in which the geometric relationship between a base station and a mobile station is used. This makes it possible to identify range measurements corrupted by NLOS errors, and the mobilestation can then estimate its position through line-of-sight (LOS)measurements. Moreover, the threshold of the NLOS detector is derivedvia a hybrid method using both the analytical derivationand computer simulation, which significantly reduces the difficultyof identifying thresholds. After identifying the NLOS measurements, a two-step weighted-least-squares algorithm is usedto obtain the localization, in which both range and anglemeasurements are considered. The simulation results revealthat the proposed algorithm yields a high identification probabilityof NLOS measurements, which results in improved localizationperformance.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61471322) and Open Project of Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang, China.


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  • Figure 1

    Example of NLOS and LOS propagation.

  • Figure 2

    (Color online) Influence of $\lambda$ on the probability of correct identification. Comparison of different (a) LOS BS numbers and (b) MS locations.

  • Figure 3

    (Color online) Probability of correct detection with different $\lambda$, SDR, and SDA. (a) SDR = 5 m, SDA $~=0.5{}^\circ$; (b) SDR = 4 m, SDA $=1{}^\circ$; (c) SDR = 16 m, SDA $~=1{}^\circ$; (d) SDR $=~10$ m, SDA $~=2{}^\circ$.

  • Figure 4

    (Color online) RMSE performance comparison for different SDR: SDA $~=1{}^\circ$. (a) Case 5/2; (b) case 5/3; protectłinebreak (c) case 5/4.

  • Figure 5

    (Color online) RMSE performance comparison for different SDA: SDR = 10 m. (a) Case 5/2; (b) case 5/3; protectłinebreak (c) case 5/4.

  • Figure 6

    (Color online) Cumulated distribution function (CDF) of tested algorithms under harsh environments for two LOS BSs. (a) SDR = 40 m, SDA $~=1{}^\circ$; (b) SDR = 40 m, SDA $~=2{}^\circ$

  • Table 1   Detection probability of RT algorithm with SDA$~=1{}^\circ$ and SDR = 9 and18 m
    LOS BS number 7 6 5 4 3 2
    SDR = 9 m 0.940 0.947 0.944 0.943 0.847
    SDR = 18 m 0.932 0.941 0.949 0.927 0.721
  • Table 2   Performance of proposed algorithm with different SDA:SDR = 9 m
    LOS BS number 7 6 5 4 3 2
    SDA = 0.1 1 1 1 0.999 0.998 0.994
    SDA = 0.5 0.995 0.991 0.987 0.985 0.988 0.976
    SDA = 1 0.979 0.968 0.951 0.945 0.937 0.935
  • Table 3   Performance of proposed algorithm for different SDA: SDR = 18 m
    LOS BS number 7 6 5 4 3 2
    SDA = 0.1 1 1 0.999 0.997 0.996 0.994
    SDA = 0.5 0.994 0.990 0.989 0.988 0.982 0.983
    SDA = 1 0.985 0.967 0.956 0.938 0.937 0.941

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