Abstract
The Compressed Sensing (CS) camera can compress images in real time without consuming computing resources. Applying CS theory in the Laser Communication (LC) system can minimize the assumed transmission bandwidth (normally from a satellite to a ground station) and minimize the storage costs of beacon light-spot images; this can save more than ten times the typical bandwidth or storage space. However, the CS compressive process affects the light-spot tracking and key parameters in the images. In this study, we quantitatively explored the feasibility of the CS technique to capture light-spots in LC systems. We redesigned the measurement matrix to adapt to the requirement of light-tracking. We established a succinct structured deep network, the Compressed Sensing Denoising Center Net (CSD-Center Net) for denoising tracking computation from compressed image information. A series of simulations was made to test the performance of information preservation in beacon light spot image storage. With the consideration of CS ratio and application scenarios, coupled with CSD-Center Net and standard centroid, CS can achieve the tracking function well. The information preserved in compressed information correlates with the CS ratio; higher CS ratio can preserve more details. In fact, when the data rate is up than 10%, the accuracy could meet the requirements what we need in most application scenarios.
Highlights
The use of laser beams to carry information through free space is a popular technique due to its directivity, power dissipation, high bandwidth and high data rate [1,2,3,4,5]
A series of simulations was made to test the performance of information preservation in beacon light spot image storage
The information preserved in compressed information correlates with the Compressed Sensing (CS) ratio; higher CS ratio can preserve more details
Summary
The use of laser beams to carry information through free space is a popular technique due to its directivity, power dissipation, high bandwidth and high data rate [1,2,3,4,5]. Other researchers [28] placed a random phase mask on a lens to randomly project the object on the array of sensors with fewer pixels This captured compressed images in a single shot. The CS structured performs two necessary functions: light coarse tracking and light-spot image storage This requires deep network, the CSD-Center Net, which can compute light-plot centers directly and swiftly while obtaining the plot center directly from compressed information and maintaining sufficient atmospheric requiring relatively little computation, as discussed in greater detail below. Approached problem byindex redesigning the measurement matrix a succinct structured fluctuation deep network, [9,43] It is example, the this refractive structure constant (Cand) building and angle-of-arrival the CSD-Center Net, which can compute light-plot centers directly and swiftly while requiring relatively critical to quantify the effects that compression processes with different ratios exert upon various little computation, as discussed in greater detail below. It is easy to access the centroid (Ch , Cv ), Pn i,j=0 xi,j wi
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