Airborne sonar target recognition involves two key technical issues: target feature extraction and classification.
Experiments are carried out for airborne sonar target feature classification using these algorithms.
This finding suggests that advance knowledge of the target feature guides singleton search.
This target feature differs from standard rotating X-ray targets in conventional X-ray systems.
When visual attention is set for a particular target feature, such as color or shape, neural responses to that feature are enhanced across the visual field.
These findings argue against the claim that singleton search relies exclusively on stimulus-driven factors and suggest that preknowledge of the target feature, when available, can guide attention.
Generally, the multilayered feed-forward neural network can be applied to the airborne sonar target feature classification to achieve the high performance requirement.
This global feature-based enhancement is hypothesized to underlie the contingent attentional capture effect, in which task-irrelevant items with the target feature capture spatial attention.
They based their argument on the finding that distractor interference is reduced when the singleton target feature repeats vs. switches from one trial to the next.
First, an adaptive feature extraction approach based on sparse representation is applied to extract the target features from the measured radar echo waveforms, the target feature set is constructed by sparse coefficients that contain most target information.
The target feature is conducted by the image processing.
