(1) Develop decision fusion architectures for the target detection by hybrid sensor network when the target location is random.
The distribution of symbols in a predefined geometric order allowed a more clear definition of the gaze shift during sequencing, since the distance from the center and between the symbols required a real gaze shift avoiding the target detection by periphery [ 40, 41].
In addition, we want to improve the target detection process by implementing a communication module between neighboring sensor nodes.
The goal is to enhance the target detection performance by suppressing the clutter and jamming based on space time adaptive processing (STAP).
Hence, in this subsection, we compare the target detection performance after denoising by MWF, PARAFAC, and MWPT-MWF.
Figures 5, 6, and 7 are the target detection results after denoising by MWF, PARAFAC, and MWPT-MWF, respectively, in the noise environment SNRINPUT=15dB.
Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach.
Similar to [18], by involving target detection in the feedback process, we would like to ensure that the rate of sensing is optimized for the end goal of target detection.
Kunar, Rich, and Wolfe (2010) also attempted to increase the rare target detection by presenting half of the items in the display at one time, then adding the remaining half after 1000 ms.
