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Hyperparameter Free MEE-FP Based Localization
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AI
DOI：10.1109/LSP.2021.3111596.png)
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Abstract
En 中文
In the context of outdoor localization over systems impaired by non-line of sight (NLoS), the minimum error entropy with fiducial points (MEE-FP) based methods have emerged as promising due to their excellent generalization and independence to statistics of NLoS. However, the performance of these approaches are well-known to depend on hyperparameters, such as, the spread parameter of the MEE-FP criterion. To enable MEE-FP based hyperparameter-free localization, we propose a modified Gauss-Newton based localization algorithm based on sampled kernel-widths. Next, analytical results are derived to demonstrate the asymptotic equivalence of the proposed kernel-width sampling based localization algorithm to its ideal fixed kernel width based counterpart. This equivalence is validated through computer simulations assuming typical non-Gaussian NLoS distributions, which motivates the hyperparameter-independence of the proposed localization algorithm and its generalization to different NLoS statistics.
Keywords:
Location awareness
Kernel
Signal processing algorithms
Probabilistic logic
Newton method
Entropy
Steady-state
Localization
information theoretic learning
minimum error entropy
hyperparameter-free learning
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W