This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed ba...This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed based on the classical Huber loss(HL)and its refined version.The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds.In practice,appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers.A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers.Simulations are included to examine the solution's performance and compare it with several benchmarks.The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks.The solution is shown to be even robust to multiple measurement outliers.Furthermore,the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.展开更多
基金supported by the National Natural Science Foundation of China(U20B2038)。
文摘This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed based on the classical Huber loss(HL)and its refined version.The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds.In practice,appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers.A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers.Simulations are included to examine the solution's performance and compare it with several benchmarks.The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks.The solution is shown to be even robust to multiple measurement outliers.Furthermore,the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.
