Robust MT Tracking Based on M-Estimation and Interacting Multiple Model Algorithm
An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments where NLOS measurements are modeled as positive outliers is proposed. Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. I...
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| Veröffentlicht in: | IEEE transactions on signal processing 2011-07, Vol.59 (7), p.3398-3409 |
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| creator | Hammes, Ulrich Zoubir, Abdelhak M |
| description | An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments where NLOS measurements are modeled as positive outliers is proposed. Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. It is shown via numerical studies that the proposed algorithm yields positioning accuracy similar to the EKF in LOS environments and even significantly outperforms the REKF in NLOS environments. |
| doi_str_mv | 10.1109/TSP.2011.2138702 |
| format | Article |
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Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. 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Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. 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Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. It is shown via numerical studies that the proposed algorithm yields positioning accuracy similar to the EKF in LOS environments and even significantly outperforms the REKF in NLOS environments.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2011.2138702</doi><tpages>12</tpages></addata></record> |
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| subjects | Algorithms Applied sciences Computational modeling Confidence Covariance matrix Detection, estimation, filtering, equalization, prediction Estimates Exact sciences and technology Information, signal and communications theory Interacting multiple model algorithm Kalman filters M-estimation Mathematical models mobile terminal tracking NLOS mitigation Noise Nonlinear optics Robustness Signal and communications theory Signal processing algorithms Signal, noise Studies Telecommunications and information theory Terminals Tracking |
| title | Robust MT Tracking Based on M-Estimation and Interacting Multiple Model Algorithm |
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