Quantum illumination makes use of the strong correlation between entangled photons, making it possible to detect targets that break through physical limits. The Gaussian entangled state is a continuous variable entangled state with a high number of Hilbert spatial modes, which is currently a viable alternative to quantum lighting. In order to explore the target detection ability of Gaussian entangled state, this paper starts from the Gaussian entanglement model of Lloyd, establishes the target detection error probability boundary model under Gaussian entangled quantum illumination, and explores the optimal detection condition of Gaussian entangled state based on the detection signal-to-noise ratio and the number of measurements. The results show that when the Gaussian entangled state is optimal for the coherent state, the number of measurements is inversely proportional to the signal-to-noise ratio. Under the same error probability, the difference between the detection distances of the two states is determined by the atmospheric attenuation coefficient. The smaller the attenuation coefficient is, the smaller the attenuation coefficient is, the difference is greater.
Spectral variability is one of the most limiting factors in hyperspectral unmixing, so it is important to further study the characteristics of spectral variability to improve the accuracy of unmixing. After conducting simulations under varying irradiation conditions, a linear mixed model combining endmember and band is proposed by introducing a band scaling factor to the endmember scaled spectrum. The total variation constraint is used to smooth the spatial distribution of both endmember and band scaling factors and then alternating iterative optimization is applied to solve the optimization problem. Experiments conducted with both simulated and real hyperspectral data sets indicate that the proposed algorithm is effective in hyperspectral unmixing and is superior to other state-of-the-art algorithms based on spectral variability.
Linear mixing model is widely used in hyperspectral images unmixing for its simplicity while the real ground distribution does not satisfy the model. To achieve a high unmixing performance, the nonlinear spectral mixing model should be further discussed. This paper extends the spectral library by adding virtual endmembers based on the second scattering between endmembers to the original spectral library and transforms the nonlinear problem into a linear issue. By introducing the l1 - l2 sparse constraint and enhancing the virtual abundance weight, the optimization problem is solved by alternating direction method of multipliers. Experiments conducted with simulated data sets and real hyperspectral data show that the proposed algorithm is superior to other state-of-the-art methods.
The space-borne HgCdTe infrared detector is widely used in missile warning and interception because of its ability to detect the active tail flame of the missile. The performance of the infrared detector on satellite directly affects the missile's identification and threat assessment. In order to study the effect of temperature on the performance of satellite infrared detector, Noise-Equivalent Temperature Difference(NETD) of the HgCdTe detector is used as the performance reference, and the SBIRS near-earth orbit small satellite's cosmic environment is the analysis background. Radiant energy of the target is analyzed with distance changes. The performance of HgCdTe detector with temperature and distance changes was simulated by mathematics software MATLAB. Which is been used to simulate the change of Noise-Equivalent Temperature Difference due to the increased operating temperature and distance change of HgCdTe infrared detector. which provided calculations reference for the performance analysis of HgCdTe detectors on the satellite.
Based on the heat conduction theory, a theoretical model of HgCdTe detector irradiated by 10.6μm CW laser is constructed. The thermal effect of the detector is analyzed by finite element method when the laser spot velocity is 0m/s, 4mm/s and 10mm/s respectively with the peak power density of 50KW/cm2 . The results show that, the temperature of irradiation point rises rapidly when v=0 mm/s, but the rising speed will be slower and slower and finally reaches the equilibrium temperature under the combined effect of laser irradiation and heat conduction. In the case of relative motion, the position of the peak temperature gradually shifts along the velocity direction. The peak temperature gradually decreases with the increase of the moving speed, and the width of the temperature peak gradually widens with the increasing speed. Relative motion should be considered when studying laser radiation and laser protection.
Laser reflective tomography(LRT) imaging is a effective technique in high-resolution imaging of remote target. Since the mass distribution information of target is contained in echo, the barycenter of target could be located from echoes in different angle. We proposed a universal method to locate the distance barycenter of 2D planar target or shelly target applied LRT. Simulation results show the barycenter could be located with relevant uncertainty of 0.0226.
The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.