Error Analysis of Localization Based on Minimum-Error Entropy With Fiducial Points

Error Analysis of Localization Based on Minimum-Error Entropy With Fiducial Points

R. Mitra, G. Kaddoum, G. Dahman and G. Poitau, “Error Analysis of Localization Based on Minimum-Error Entropy With Fiducial Points,” in IEEE Communications Letters, vol. 25, no. 4, pp. 1187-1191, April 2021, doi: 10.1109/LCOMM.2020.3043974.

Abstract or Summary

Localization has been a well-investigated problem in the past decade for several networks and is one of the most celebrated applications of adaptive signal processing. However, existing localization algorithms exhibit a significant degradation under non-line of sight (NLOS) conditions, especially in outdoor scenarios. Due to the inherent non-Gaussianity in the NLOS returns, generic mitigation of the degradations due to NLOS remains quite an open challenge. In this regard, information-theoretic learning (ITL) criteria are attractive due to their ability to adapt to arbitrary NLOS distributions and suppress NLOS-induced non-Gaussian processes. In this regard, this letter proposes the use of minimum error entropy with Fiducial points (MEE-FP) in the particular context of round-trip time of arrival (RTTOA) based localization. From the presented simulations, it is observed that the proposed MEE-FP based localization method delivers lower variance under severe NLOS conditions and is closer to the ideal maximum-likelihood solution than contemporary ITL based approaches. Lastly, analytical variance-expressions are derived for the proposed localization technique, which is validated by computer simulations.

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