Attitude estimation is a significant task for monitoring noncooperative spacecraft in orbit. Inverse synthetic aperture radar (ISAR) is an effective method to obtain the structure, size, and attitude ...of spacecraft. Most existing methods of attitude estimation based on ISAR images generally rely on characteristic point matching or rectangular feature matching among ISAR images. They often need high quality ISAR images, so the robustness is usually poor. A novel method for estimating spacecraft's axial attitude in ISAR image is proposed in this article. This method does not need to utilize a simulation database, or to match sequence of specific features, so it has good robustness. Our method bridges Range-Doppler images and attitude parameters with the accommodation of spacecraft's orbit parameters and the ISAR geometric projection model. Based on the ISAR imaging turntable model, the accurate position of the imaging plane of the spacecraft in orbit is derived. Then, attitude parameters are estimated by an optimization of the projection of spacecraft's three-dimensional model to the imaging plane. Experimental results demonstrate the effectiveness and robustness of the proposed method to estimate attitude of spacecraft in orbit by the simulated and measured ISAR images.
The Fourier transform-based range Doppler method is commonly used in an inverse synthetic aperture radar. Although it has achieved good success in most scenarios, its performance is determined by the ...rotational angle, and the cross-range resolution is extremely low in the case of a small rotational angle. In this letter, to improve the cross-range resolution, a novel cross-range compression scheme based on the iterative adaptive approach (IAA) is proposed. In addition to the standard IAA to achieve high resolution, the efficient IAA is introduced to suppress the sidelobes due to noise. Both the simulation and experimental results demonstrate that the proposed method has the advantages of parameter-free, high accuracy, and high efficiency.
In the present work, the air-steam biomass gasification model with tar has been developed based on the equilibrium constants. The simulation results based on two different models (with and without ...tar) have been validated by the experimental data. The model with tar can well predict the tar content in gasification; meanwhile, the predicted gas yield (GY), based on the model with tar, is much closer to the experimental data. The energy exchange between the gasifier and the surrounding has been studied based on the dimensionless heat transfer ratio (DHTR), and the relationship between DHTR and the process parameters is given by a formula. The influence of process parameters on the syngas composition, tar content, GY, lower heating value (LHV), and exergy efficiency have been researched.
Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level ...accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.
It is crucial for a ballistic missile defense system to discriminate the true warhead from decoys. Although a decoy has a similar shape to the warhead, it is believed that the true warhead can be ...separated by its micro-Doppler features introduced by the precession and nutation. As is well known, the accuracy of the phase-derived range method, to extract micro-Doppler curves, can reach sub-wavelength. However, it suffers from an inefficiency of energy integration and high computational costs. In this paper, a novel phase-derived range method, using high-order multi-frame track-before-detect is proposed for micro-Doppler curve extraction under a low signal-to-noise ratio (SNR). First, the sinusoidal micro-Doppler range sequence is treated as the state, and the dynamic model is described as a Markov chain to obtain the envelopes and then the ambiguous phases. Instead of processing the whole frames, the proposed method only processes the latest frame at an arbitrary given time, which reduces the computational costs. Then, the correlation of all pairs of adjacent pulses is calculated along the slow time dimension to find the number of cells that the point scatterer crosses, which can be further used in phase unwrapping. Finally, the phase-derived range method is employed to get the micro-Doppler curves. Simulation results show that the proposed method is capable of extracting the micro-Doppler curves with sub-wavelength accuracy, even if SNR = −15 dB, with a lower computational cost.
In the inverse synthetic aperture radar imaging field, the increasing demand of characterizing small targets drives researchers to seek a higher resolution. One attractive way to improve the ...resolution is sparse subband imaging. In this paper, we propose a novel framework for sparse subband imaging. First, the fine imaging of each subband is performed, through which the high-order phase errors between subband data are compensated. Then, the Keystone transform is performed to unify the image scales of subbands, and sparse subband returns are coherently processed. Finally, the cross-range compressed subband data are interpolated based on the autoregressive model and then used to reconstruct the high-resolution range profile (HRRP) via the smoothed ℓ 0 algorithm. In the proposed framework, the cross-range compression, which is performed before HRRP reconstruction, enhances the sparsity of the returns and achieves a considerable SNR gain. Consequently, it paves the way for HRRP reconstruction. Furthermore, the proposed framework needs no filling of the band gap between subbands, and it is robust against the band gap. The real data experimental results demonstrate this framework's ability to achieve a high-resolution fusion image accurately, even under low SNR conditions.
Focusing on the inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, this paper proposes a modified version of the Fourier transform, called non-uniform rotation transform, ...to achieve cross-range compression. After translational motion compensation, the target’s complex motion is converted into non-uniform rotation. We define two variables-relative angular acceleration (RAA) and relative angular jerk (RAJ)-to describe the rotational nonuniformity. With the estimated RAA and RAJ, rotational nonuniformity compensation is carried out in the non-uniform rotation transform matrix, after which, a focused ISAR image can be obtained. Moreover, considering the possible deviation of RAA and RAJ, we design an optimization scheme to obtain the optimal RAA and RAJ according to the optimal quality of the ISAR image. Consequently, the ISAR imaging of targets with complex motion is converted into a parameter optimization problem in which a stable and clear ISAR image is guaranteed. Compared to precedent imaging methods, the new method achieves better imaging results with a reasonable computational cost. Experimental results verify the effectiveness and advantages of the proposed algorithm.
Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk ...mechanisms between GC and T2D.BackgroundGastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.MethodsThe Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.ResultsA total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes."ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.Conclusion"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.
Mussels can form tough and long-lasting adhesions to organic and inorganic surfaces in saline and impactive severe aquatic environments. Similar to mussel adhesion, dentin bonding occurs in a wet ...environment. However, unlike mussels, it is difficult to achieve long-lasting bonds with dentin. Moreover, water is considered a major hindrance in dentin bonding. Inspired by the synergistic effect of cationic lysine (Lys) and catechol on the elimination of the hydration layer during mussel adhesion, a catechol- and Lys-functionalized polymerizable polymer (catechol–Lys–methacrylate CLM) was synthesized to replicate the complex synergy between amino acids and catechol. The bond-promoting potential of 5 mg/mL CLM primer was confirmed using an in vitro wet dentin-bonding model, which was characterized by an improvement in bond strength and durability. CLM can adhere to wet demineralized dentin, with Lys acting as a molecular vanguard to expel water. Subsequently, a myriad of interfacial interactions can be obtained by introducing the catechol group into the interface. Additionally, tough and long-lasting adhesion, similar to that formed by mussels, can be achieved by grafting CLM onto type I collagen via covalent bonds, hydrogen bonds, Van der Waals interactions, and cation–π interactions, which can enhance the mechanical and chemical stability of collagen, increase the enzymatic resistance of collagen, and provide additional physical/chemical adhesion to dentin bonds. Catechol- and cationic Lys-functionalized polymers can improve the stability of the resin–dentin interface under wet conditions.
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The interrupted-sampling repeater jamming (ISRJ) is a kind of intrapulse coherent deception jamming, it produces false-target peaks, which obfuscate the real target detection and tracking. In this ...article, a novel recognition method is proposed to identify the false-target peaks caused by ISRJ. The proposed method is realized via the integration decomposition of pulse compression, and the intermediate data in pulse compression are extracted to identify the false-target peaks. Due to the time-sharing transmit–receive antenna of the jammer, the jamming signal is short or discontinuous pieces compared with the real echo. This intrinsic property provides important feature for jamming identification. In the proposed method, a variance value is generated from the intermediate data, to evaluate the temporal energy distribution evenness of the signal component corresponding to each target peak. Real target echo corresponds to small variance whereas the jamming signal corresponds to large variance. In this way, false-target peaks caused by jamming signal can be identified efficiently. Besides, the computational cost of the proposed method is low, thus, it is suitable for real-time applications in practical radar systems. Numerical experiments under different jamming parameters demonstrate the promising performance of the proposed false-target recognition method. Moreover, Monte Carlo simulations under different SNR levels verify the reliable classification capability of the proposed method. In summary, this article provides a new perspective for ISRJ identification, and it is also the proof-of-concept example for other potential applications concerning integration decomposition.
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