An improved Blackman Harris apodization function is studied for rectify the shortcomings of large main lobe width of Blackman Harris apodization function in apodization processing of Fourier transform spectrometer. The basic principle of apodized interferogram is analyzed. The Chebyshev function with tunable parameters is introduced to improve Blackman Harris function. Compared with the original function through Matlab simulation, the improved apodization function shows not only the characteristics of main lobe width is enhanced, but also the side lobe attenuation can be adjusted, so the function is more flexible in engineering application.
Endmember extraction technology of ballistic missile is an important research content in spectral remote sensing, which can effectively solve the mix pixel problem. This paper starts with the background requirement of near space and research content of mixed spectral endmember extraction. The algorithm of spectral endmember extraction based on non-negative matrix factorization for ballistic missile in near space background is proposed and analyzed. The simulation results show that the proposed algorithm demonstrates the good performance in the condition of random mixing mode and correlation mixing mode.
The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. The operating range of the infrared system based on the near space platform is the key index for detecting ballistic missile. In order to analyze the operating range of infrared detection system based on near space platform, an improved range equation from investigating the characteristics of point source target is discussed and deduced. The simulation results show this method has scientific guiding significance for system overall scheme demonstration and design technically.
The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. To estimate the detection range of near space-based infrared system for boost-phase ballistic missile, the background infrared radiation as well as ballistic missiles is analyzed in detail. As for the lack of applicability and accuracy of the role distance algorithm which based on the performance contrast, the wave number to the radiation flux formula is introduced. The detection ranges of skin, plume and tail nozzle for boost-phase ballistic missile at 4.25 to 4.55μm are simulated under various conditions. The results show that the improved algorithm can provide the certain engineering application value for the design of near space-based infrared system.
The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. In terms of target detection capability, aiming at the problem that the formula of the action distance based on contrast performance ignores the background emissivity in the calculation process and the formula is only valid for the monochromatic light, an improved formula of the detecting range based on contrast performance is proposed. The near space infrared imaging system parameters are introduced, the expression of the contrastive action distance formula based on the target detection of the near space platform is deduced. The detection range of the near space infrared system for the booster stage ballistic missile skin, the tail nozzle and the tail flame is calculated. The simulation results show that the near-space infrared system has the best effect on the detection of tail-flame radiation.
The propagation characteristics of the orbital angular momentum in vortex waves have been studied. The representation of electric multipole radiation filed is derived from the Laugerre-Gaussian beams to electromagnetic vortex. Simulation results show the capability of using the orbital angular momentum for remote imaging.
The ballistic missile hyperspectral data of imaging spectrometer from the near-space platform are generated by
numerical method. The characteristic of the ballistic missile hyperspectral data is extracted and matched based on two
different kinds of algorithms, which called transverse counting and quantization coding, respectively. The simulation
results show that two algorithms extract the characteristic of ballistic missile adequately and accurately. The algorithm
based on the transverse counting has the low complexity and can be implemented easily compared to the algorithm based
on the quantization coding does. The transverse counting algorithm also shows the good immunity to the disturbance
signals and speed up the matching and recognition of subsequent targets.
An improved anomaly detection and classification algorithm based on high-order statistics is presented. In order to solve some challenging problems, such as initializing projection, quantifying of anomaly classes and evaluating the performances. Firstly, initialize the projection vectors used by the idea of global RX algorithm. It gives priority to the detection of the anomalies with powerful energy. Secondly, analyze the current data whether have anomaly information or not so that it determines the terminal conditions and the quantities of anomaly classes. Thirdly, use two methods to evaluate the classification performance quantitatively. One is to match the results in the condition of reference images to evaluate the effects of anomaly detection and background suppression, the other is to segment the resultant images to calculate some features such as the classification rate, the number of detected anomalies and the number of false alarms. Simulated and Experimental results show that the improved algorithm has the capability of robustness and better anomaly detection performances under complex unknown background than traditional algorithm does.
Accurate modeling of sea clutter and robust detection of low observable targets within sea clutter are the important
problems in the field of remote sensing and radar signal processing. In this paper, a multiplicative multifractal process is
introduced for the modeling sea clutter amplitude time series; a double parameter model which outperforms the double
exponential model and Gaussian model is proposed and applicable for distribution fitting for multiplier of multiplicative
multifractal modeling of real sea clutter. In addition, a short time generalized dimension spectrum based measurements
of matching goodness is proposed and analyzed carefully, new model for HF sea clutter is built and reasonability of
STGDs based measurements has been proven by simulation analysis. This modeling is computable rapidly and
applicable in the research of target detection in HF sea-clutter background.
This paper presents a texture segmentation approach based on Gauss-Markov random field(GMRF) model and multi-directional mosaics. Image texture is modeled by the second order GMRF model and the least error estimation is employed for the solution of model parameters. In order to improve the segmentation accuracy of uncertain area in boundary region between different textures, we introduced Laws energy masks and directional mosaics to obtain energy and orientation feature. And Euclidean distance approach is employed to classify different features. Experiments show that accuracy of texture segmentation can be improved.
In texture analysis, the selection of window size has great influence on effectiveness of extracted feature and computing speed. This paper employs Gauss-markov random field (GMRF) model to describe textures, the least square error approach is employed to estimate field parameters, and it has been proved to be non-bias. Because there may be no solution by the estimation expression, a modification to it is presented. Based on the non-bias characteristic of parameter estimation, we present a window size selection approach for texture primitives, and experiment shows that our approach is very effective.
In this paper, a new small target detection approach based on the analysis of image background texture is presented. Through multi-scale wavelet decomposition of background textures and local energy calculation, feature vectors of each pixel is obtained. According to these feature vectors, a relative distance image corresponding to the features can be derived, and target detection can be performed on this distance image. The histogram of distance image is employed for automatic selection of threshold value. Experiments show that this approach can achieve quite satisfactory results.
In this paper, a new small target detection approach which is based on the analysis of image background texture is presented, and Gaussian-Markov random field (GMRF) is chosen as texture model. Texture features can be obtained from the least square error estimation of GMRF parameters. Image is divided into non-overlapped blocks, and feature vector is obtained for each block. Using the normalized Euclidean distance of feature vectors as a measure of the difference of textures, target detection can be performed in a coarse to fine procedure according to the difference of features and approximation error.
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