A quadrant detector (QD) is a widely used position sensor. By employing ghost imaging, the detector's four-channel output can be multiplexed to simultaneously locate and image a target. However, flaws in the detection system distort the detected laser intensity, which will increase positioning errors and degrade the reconstructed image quality. In this study, based on the spot distribution and detector characteristics, a detection model was established and analyzed. A neural network-based fitting method is proposed to directly predict the spot location and correct the total light intensity with the detector output. The network is constructed and trained with simulated data. The prediction accuracy performance is verified by numerical simulations. This scheme has the potential to improve the localization accuracy and imaging quality of quadrant detector-based detection systems, such as radar and guidance applications.
Conventional semi-active laser guidance (SAL) systems take advantage of the laser designator to illuminate precisely and form a laser spot on the target. The seeker collects reflected light by a quadrant detector and converts to relative position information to guide the missile to the target accurately. Computational ghost imaging (CGI) provides new promising scheme for semi-active laser guidance in virtue of the similarity in the system composition. As with traditional optical imaging processes, scattering and turbulence in atmospheric medium interfere with the final imaging result. In this work, a mathematical model of entire imaging process is established basing on principles of Mie scattering of colloidal particles and scintillation of turbulence. Analysis of the SNR of the reconstructed image to different environment parameters or system parameters indicates that both scattering and turbulence lead to a decrease in imaging quality. In the mechanism of action, turbulence introduces a random multiplicative noise while scattering highlights the interference of the inherent additive noise to the imaging system.
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