Synthetic aperture laser radar (inverse) combines the technology of laser radar with synthetic aperture, which has high imaging resolution, strong anti-interference, and good concealment. Due to the short laser wavelength and fast imaging time, the tiny vibrations of the moving target may achieve the target inverse synthetic aperture (range -Doppler) imaging in a very short time, which increases identification characteristics compared to the traditional optical remote point target detection and recognition; it reduces the complexity of data processing compared to radar, and optical imaging is easier to understand. Therefore, synthetic aperture laser radar has the advantages of both optics and radar, and has attracted more and more attention in long-distance target detection and recognition. Since 1960's, MIT Lincoln Laboratory has conducted research on the long-range target tracking and identification using laser radar. In this paper, the micro-motion feature extraction and recognition method for inverse synthetic aperture laser radar after target imaging is studied. The target images of different micro-motion form are analyzed by range-Doppler imaging model, and the geometric features of the target are extracted by the optical target segmentation algorithm. The Hough transform theory is used to extract the characteristics of the micro-motion period, and the micro-motion angle is inversed through the change of the target geometric features. The simulation test in field shows that this method can effectively extract the micro-motion characteristics of the target and lay a foundation for the micro-motion target recognition of synthetic aperture laser radar.
At present, when the infrared sensor detects the targets remotely, the target appears as a spot in the image plane, the geometry information is difficult to obtain, and the surface brightness temperature becomes an effective feature for the target recognition. However, due to the long distance of the target, the weak signal and the complex transmission path, the temperature features are difficult to extract accurately, which brings great uncertainty to the target recognition. Based on the principle of multi-spectral infrared radiation temperature measurement, this paper establishes a BP network model to estimate the point target temperature. Experiments show that the accuracy of extracting the faint targets temperature characteristics can be effectively improved, which shows great support for target recognition.
A small target is a target which is far enough to a detector, and its image on FPA can’t be large enough to show its shape and size. In this situation, when a small target is detected by an infrared imaging single-band detector, we can only analyze it by the dispersion point or the subpixel image caused by it. The target discrimination can be impossible when it meets a smaller-sized target with higher temperature and a larger-sized target with lower temperature, because their image on FPA can be quite similar when they’re far enough. However, with the dual-band detection, we can figure out the temperature via dual-band ratio easily, without the information of distance and target size. Equivalent area can be also figure out during this calculation. The target discrimination can be achieved with the temperature and equivalent area known. And according to some priori knowledge, can we make those target recognized in a particular scene. This article briefly show the benefit of dual-band detection compared to single band detection.
When infrared detection scene simulation, Target signature quantitative measurement, a high confidence sensor model should be
developed. Generally, sensor model is developed by dividing an infrared sensor into three parts: optics, detector and electronic
circuits. Then several Mathematics models describing those parts effect are developed, and a sensor model is integrated. In this
way, the sensor model is based on strict mathematic theory. But this model needs a lot of parameters, some of which are very
difficult to achieve. So this model have an advantage to analyze sensor model effect, and when to simulating Infrared detection
scene or to analyzing quantitative measurement precision of faint target by actual infrared sensor, a sensor model which is based
on parameters that can be achieved should be developed. This article presents a new sensor model. The input parameters includes:
SiTF, MTF, Noise and so on, all of which can be achieved in laboratory or outfield. The sensor model is validated by point target
experiment and four-bar target experiment, and the error is within 5%. The SiTF parameter can be achieved through the relation
of blackbody radiation and sensor signal. The noise parameter can be achieved by nonuniform background sensor signal and SiTF.
The MTF parameter is very important, but it is difficult to be measured directly, especially outfield. This article presents a method
to inverse the MTF by point target observation experiment. This method can be used to inverse an actual sensor MTF outfield by
star observation experiment.
Target signature quantitative measurement is a key problem in target signature studies. There are many factors to affect the measurement precision, especially when the target is faint. This article studies two of the most important factors, infrared imagery SNR and infrared detector fill factor. The former is related to the detector energy acquisition parameters, such as NETD, caliber, integral time, and so on. The latter is related to the detector spatial sampling feature. The conclusion of this article could give reference to infrared target quantitative measurement and infrared detector design.
This article studies the near sea surface atmosphere visibility and aerosol scattering asymmetry factor with observed weather data. Based on muti-modal log normal distribution model, Mie theory and SCE-UA method, near sea surface atmosphere aerosol parameters, such as distribution parameters and relative refractive index, are inversed. A discussion about the relationship between the inversed parameters and weather data is present.
The spectra have been calculated with Line by Line method for high-temperature CO<sub>2</sub> and atmosphere. The characteristic of the wing of atmospheric 4.3μm absorption band has been analyzed. The variation of transmittance from different altitudes to space in 2370 to 2390cm<sup>-1</sup> was calculated and analyzed. The radiations of high-temperature CO<sub>2</sub> located in different altitudes in the same wave range were compared. According to the variation of atmosphere and the spectral characteristic of hot CO<sub>2</sub>, the way for choosing band of detection was discussed.
Along with the popular application of focal-plane array IR camera, the field of IR detection and measurement is
extended from qualitative to quantitative measurement. In the area of low-temperature radiation quantitative
measurement for small targets in low temperature background, one of the most important techniques for IR radiation in
low-temperature measurement is IR radiation calibration in the low-temperature. With the need of measuring small weak
target with IR camera in our research, we developed a special large aperture low-temperature blackbody which is different from vacuum blackbody for low-temperature calibration, and presented an effective method for
low-temperature calibration of IR camera to the lowest temperature at -100ºC. The key technology in the camera’s calibration includes anti-frosting in low-temperature, correcting calibration results. In the calibration, adopts end to end
way to make the infrared rays full of the FOV of camera. This method minimizes the influence of CO<sub>2</sub>, H<sub>2</sub>O in the air,
and improves the accuracy of calibration.