Kernel-based cooperative robust sequential hypothesis testing

Pambudi, Afief Dias; Fauß, Michael; Zoubu, Abdelhak M.

doi:10.1109/icsigsys.2018.8373565

Cited by 4 publications

(1 citation statement)

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“…However, in comparison to the parametric model, the robust detectors are preferable with fewer false alarms. This detection performance can be improved by carefully assigning the band interval which naturally aligns with the non-stationary behavior of the measurements [30], [31]. A different approach can also be applied by considering the LRT which takes into account statistical dependencies between measurements [32].…”

Section: Detection Results In High Surface Roughness Environmentsmentioning

confidence: 99%

Minimax Robust Landmine Detection Using Forward-Looking Ground-Penetrating Radar

Pambudi

Faub

Ahmad

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using a forwardlooking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The LRT is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst-case performance over all feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions. The proposed technique is evaluated using electromagnetic field simulation data of shallowburied targets. We show that, compared to detectors based on parametric models, robust detectors can lead to significantly reduced false alarm rates, particularly in cases where there is a mismatch between the assumed model and the true distributions.

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Section: Detection Results In High Surface Roughness Environmentsmentioning

confidence: 99%