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Goppelt et al. chose the probability of ghost target detection as a figure of merit [12].
Hela-P4 supernatants containing equal amounts of infectious virus, standardized for equal amounts of p24 antigen, were used for the infection of GHOST target cells expressing human CD4 and the CXCR4 glycosylation mutants.
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One objective of the tracking step is to identify and eliminate these ghost targets.
These ghost targets can appear stronger than the original target thus leading to incorrect interpretations of the focussed image that can mask other targets of interest.
Multipath propagation produces ghost targets which populate the scene and not only create confusion with genuine targets but deteriorate the performance of compressive sensing (CS) algorithms.
The result of this effect will be the appearance of ghost targets in the cross-range direction on both sides of the original target.
Based on the radar signal processing performed for the analyzed scenario, approximately eight ghost targets could be generated due to the incorrect TOA association.
Violation of this requirement leads to the occurrence of azimuth image ambiguities also called grating lobes or ghost targets in the reconstructed image.
This figure illustrates a well known problem of LFMCW radar: under certain conditions, the frequency matching step may lead to target mismatching, and thus may result in the creation of ghost targets [28].
Due to the micro-Doppler effect, azimuth compression cannot focus the vibrating scatterer on the correct cross-range position, with some significant sidelobes nearby, which is commonly called ghost targets [20].
As a consequence, the association in the beat frequency measurement space is robust and dramatically reduces the probability of ghost targets under the circumstances where the probability of detection is low and different depending on radars in the network[7].
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