TY - JOUR
T1 - CASTELO
T2 - Convex Approximation based Solution To Elliptic Localization with Outliers
AU - Xiong, Wenxin
AU - Shi, Zhang-Lei
AU - So, Hing Cheung
AU - Liang, Junli
AU - Wang, Zhi
PY - 2024/5
Y1 - 2024/5
N2 - This short communication considers mitigating the negative effects of possibly unreliable path delay measurements acquired in non-line-of-sight (NLOS) environments on the positioning performance, a problem deserving further investigation within the expanding research area of elliptic localization. We present CASTELO, a Convex Approximation based Solution To Elliptic Localization with Outliers, to achieve such a goal. Our proposal corresponds to a mixed semidefinite (SD)/second-order cone (SOC) programming formulation derived from an error-mitigated nonlinear least squares (LS) location estimator, presenting itself as a remedy for the neglect of positivity of NLOS biases suffered by the majority of currently fashionable outlier-handling approaches. In terms of analytical discussions, we provide rationales supporting the incorporation of the SOC constraints, which serve to tighten the problem obtained after SD relaxation, and conduct a complexity analysis for the ultimate mixed SD/SOC programming formulation. Simulations are carried out to confirm the strong ability of CASTELO to attain reliable elliptic localization in the presence of NLOS outliers. © 2024 Elsevier B.V.
AB - This short communication considers mitigating the negative effects of possibly unreliable path delay measurements acquired in non-line-of-sight (NLOS) environments on the positioning performance, a problem deserving further investigation within the expanding research area of elliptic localization. We present CASTELO, a Convex Approximation based Solution To Elliptic Localization with Outliers, to achieve such a goal. Our proposal corresponds to a mixed semidefinite (SD)/second-order cone (SOC) programming formulation derived from an error-mitigated nonlinear least squares (LS) location estimator, presenting itself as a remedy for the neglect of positivity of NLOS biases suffered by the majority of currently fashionable outlier-handling approaches. In terms of analytical discussions, we provide rationales supporting the incorporation of the SOC constraints, which serve to tighten the problem obtained after SD relaxation, and conduct a complexity analysis for the ultimate mixed SD/SOC programming formulation. Simulations are carried out to confirm the strong ability of CASTELO to attain reliable elliptic localization in the presence of NLOS outliers. © 2024 Elsevier B.V.
KW - Convex approximation
KW - Elliptic localization
KW - Non-line-of-sight
KW - Outlier
UR - http://www.scopus.com/inward/record.url?scp=85182259403&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85182259403&origin=recordpage
U2 - 10.1016/j.sigpro.2023.109380
DO - 10.1016/j.sigpro.2023.109380
M3 - RGC 21 - Publication in refereed journal
SN - 0165-1684
VL - 218
JO - Signal Processing
JF - Signal Processing
M1 - 109380
ER -