Aerial target spatial–spectral discrimination for imaging spectrometer based on the acousto-optic tunable filter

Abstract

To solve the problem caused by jamming, an acousto-optic tunable filter (AOTF)-based imaging spectrometer and a corresponding spatial–spectral discrimination method are proposed for aerial targets. The system has the capability of staring imaging and is electronically tunable, which provides the spatial location and a distinguishable spectral feature in a few images. Since AOTF operates in a frame mode, the spectral brightness of the targets can be predicted by Kalman filtering, like with the motion model. The final target state is updated by using synthetic spatial–spectral information to realize fast decision-making. The results show that the proposed method is more targeted to solve the problem caused by jamming, compared with the traditional energy discrimination method.

Angular-Spectral Characteristics of Acousto-Optic Tunable Filters Based on Mercurous Halide Crystals

“…AOTFs operate with an AO crystal, wherein incident light undergoes diffraction due to acoustic waves generated by ultrasonic transducers. AOTF-based spectral imaging systems, known for their rapid tuning, reliability, repeatability, and flexible spectral channel switching, enable quicker acquisition of spectral data in scenes compared to conventional push-broom systems [ 7 ].…”

Optical spectrum selection for aerial target detection under complex backgrounds

“…The modeling and simulation of the radiation properties of the aerial target itself and the study of the radiation properties in different backgrounds and seasons further improve the understanding of the characteristics of the aerial target in the coupled state of the environment [5,6] . Based on this, numerous algorithms including local spatial contrast, non-downsampled contour wave transform, 2D characteristic histogram, IR differential resolution factor characterisation, correlated null spectrum discrimination, omnidirectional multiscale morphology, correlation filtering and deep learning are used to achieve continuous monitoring of airborne weak targets in complex backgrounds [7][8][9][10][11][12] . In addition, combined multi-spectral detection can increase the probability of composite detection while reducing the probability of false alarms [13] , and by fusing the differences in characteristics between real and fake targets across multiple spectral bands can also enable the classification of targets for identification [14] .…”