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Numerical experiments and real clutter data are given to demonstrate the effectiveness of the proposed geometric detection methods.
This technique is a demonstration of clutter characterization using OTHR data, but the method can be applied to characterize other types of clutter data.
This experiment was performed at Fort Indiantown Gap, PA, collecting over 250 km2 of clutter data and over 250 views of targets in realistic deployments.
The method generates a 3D wireframe model combined with clutter data, which is then imported into a BIM tool for as-built modeling.
By systematically analyzing practical sea clutter data under different sea environments, we demonstrate that the proposed model achieves comparable fitting effect to some commonly used statistical models.
First of all, the radar received clutter data in each range cell in one coherent processing interval is modeled and mapped into an HPD matrix space, which can be described as a complex Riemannian manifold.
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We evaluated the method on a large, highly cluttered data set of a building with forty separate rooms.
Finally, backprojection was performed on the wall clutter mitigated data, and the corresponding image is shown in Figure 4a.
The false alarm probability is specified as 0.001 and the decision threshold is determined from the Monte-Carlo tests to clutter-only data.
When N < 8, the decision thresholds have to be determined by the histograms from a large number of Monte-Carlo tests to clutter-only data.
In this case, the decision thresholds have to be determined by Monte-Carlo tests to clutter-only data and the performance evaluations have to be performed by the experimental results to real data rather than rigorous theoretical analysis [27].
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