New virtual-anchor and sample-polish-select estimators for the diffraction path-length model achieve near-CRLB performance in mixed LOS/NLOS localization with substantially lower complexity and better initialization robustness than multistart Gauss-Newton.
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dsCEM replaces random sampling in CEM-MPC with deterministic samples from localized cumulative distributions to improve efficiency and smoothness in nonlinear optimal control.
A probabilistic DOA estimator for directional sensors that folds missed detections into the likelihood function using only signal-strength measurements and known thresholds.
citing papers explorer
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A New Location Estimator for Mixed LOS & NLOS scenarios
New virtual-anchor and sample-polish-select estimators for the diffraction path-length model achieve near-CRLB performance in mixed LOS/NLOS localization with substantially lower complexity and better initialization robustness than multistart Gauss-Newton.
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Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
dsCEM replaces random sampling in CEM-MPC with deterministic samples from localized cumulative distributions to improve efficiency and smoothness in nonlinear optimal control.
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Utilizing Missed Detections in Directional Sensitivity-Based DOA Estimation
A probabilistic DOA estimator for directional sensors that folds missed detections into the likelihood function using only signal-strength measurements and known thresholds.