X-ray diffraction is one of the most widely applied methodologies for the in situ analysis of kinetic processes involving crystalline solids. However, due to its relatively high detection limit, it has only limited application in the context of crystallizations from liquids. Methods that can improve the detection limit of X-ray diffraction are therefore highly desirable. Signal processing approaches such as Savitzky-Golay, maximum likelihood, stochastic resonance, and wavelet transforms have been used previously to preprocess X-ray diffraction data. Since all these methods only utilize the frequency information contained in the single X-ray diffraction profile being processed to discriminate between the signals and the noise, they may not successfully identify very weak but important peaks especially when these weak signals are masked by severe noise. Smoothed principal component analysis (SPCA), which takes advantage of both the frequency information and the common variation within a set of profiles, is proposed as a methodology for the preprocessing of the X-ray diffraction data. Two X-ray diffraction data sets are used to demonstrate the effectiveness of the proposed approach. The first was obtained from mannitol-methanol suspensions, and the second data set was generated from slurries of L-glutamic acid (GA) in methanol. The results showed that SPCA can significantly improve the signal-to-noise ratio and hence lower the detection limits (approximately 0.389% g/mL for mannitol-methanol suspensions and 0.4 wt % for beta-form GA in GA-methanol slurries comprising mixtures of both alpha- and beta-forms of GA) thereby providing an important contribution to crystallization process performance monitoring.
Abstract-In order to eliminate the negative influence of the rotational phase component (RPC) of target prominent scattering centres on the performance of Doppler centroid tracking (DCT) method, a coherent phase compensation method is proposed. The coherence of echo pulses sampled directly in intermediate frequency (IF) is firstly analyzed and proved.Based on the coherence property, the proposed approach improves the translational phase component (TPC) estimation accuracy of DCT. Compared to the modified Doppler centroid tracking (MDCT) algorithm, the proposed method achieves better phase compensation performance with simpler operations. Both the theoretical analysis and experimental results based on the real ISAR data prove the effectiveness and efficiency of the presented strategy.
Abstract-A novel rotational motion compensation algorithm for high-resolution inverse synthetic aperture radar (ISAR) imaging based on golden section search (GSS) method is presented. This paper focuses on the migration through cross-range resolution cells (MTCRRC) compensation, which requires rotation angle and center as priori information. The method performs in a nonparametric way and uses entropy criterion to estimate rotation angle and rotation center, which are used for rotational motion compensation. Experimental results show that the rotational motion in ISAR imaging can be effectively compensated. Moreover, the proposed method is robust and computationally more efficient compared to the parametric methods.
Abstract-Due to the inaccuracies in radar's measurement, autofocus including range alignment and phase compensation is always essential in inverse synthetic aperture radar (ISAR) imagery. Compressed sensing (CS) based ISAR imagery suggests that the image of target can be reconstructed from much fewer random pulses. Because the number of pulses is inadequate and the pulse intervals are nonuniform, conventional phase compensating algorithms can't work in CS imaging. In this paper, an iterative algorithm is proposed to compensate the phase errors and reconstruct high-resolution focused image from limited pulses. In each iteration, the image of target is reconstructed by CS method, and then the estimation of phase errors is updated based on the reconstructed image. By cycling these steps, well-focused image can be obtained. The smoothed 0 algorithm is used to reconstruct the image, and the idea of minimum entropy optimization is used to estimate the phase errors. Besides, a method of extracting range bins in range profile based on amplitude information is proposed, which can reduce the computational complexity and improve the speed of convergence considerably. Both simulation and experiment results from real radar data demonstrate the effectiveness and feasibility of our method.
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