Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Linear frequency-modulated (LFM) continuous-wave (CW) radar is usually the first choice in synthetic aperture radar (SAR) imaging missions due to its relatively low cost and hardware simplicity. However, the use of continuous-wave unnecessarily introduces the problem of intrapulse motion. Furthermore, a large amount of data generated by the CW imaging process may overburden the onboard communication system with its high streaming data rate. On the other hand, a large amount of data may, indeed, not be necessary for high-resolution SAR imaging. In this article, we took full consideration of the intrapulse motion in LFMCW radar systems and modeled the phase preserving extended frequency scaling algorithm (EFSA) reconstruction process with far fewer data samples as compressive sensing problem and used subgradient descent algorithm with optimal step size to realize compressive sensing reconstruction. Our compressive sensing problem is different from the traditional direct inversion-based problem in that our reconstructed results contain both mainlobe and sidelobes. Comparisons were made with mainstream iterative soft thresholding-based reconstruction algorithm to demonstrate its capability to reconstruct large imaging scenes with high resolution. Both simulations and experiments with measured data have verified our proposed algorithm.

Conventional single antenna synthetic aperture radar (SAR) has a conflict between wide imaging swath width and fine cross-range resolution. Single input multiple output (SIMO) and multiple input multiple output (MIMO) radars are the two methods to overcome this limitation. In order to separate the signals of MIMO radar, the codes are expected to be orthogonal for all delays. This is impossible in SAR. Due to the special imaging geometry of SAR, short-term shift orthogonal (STSO) codes were proposed to be used in MIMO SAR. This letter compares the SIMO SAR structure and MIMO SAR structure using STSO codes. We draw a conclusion that the number of receivers of MIMO structure is less than that of SIMO structure.

In this paper, we present an algorithm for curvilinear Video-SAR (CViSAR) persistent imaging with distortion correction. Firstly, we derived the mapping between the actual and reconstructed location of any target in any frame image formed by curvilinear persistent polar format algorithm, named as curvilinear persistent mapping (CPM). Then, through a combination of the non-uniform input wavenumber spectrum with the non-uniform output imaging grids calculated by CPM, distortion-free frame images can be formed directly via a single non-uniform fast Fourier transform of type 3 (NuFFT-3). Simulation experiments are provided to demonstrate the effectiveness of the proposed algorithm.

Conventional single-platform spaceborne synthetic aperture radar has a conflict between wide imaging swath width and fine-cross-range resolution. Displaced phase center antennas is one method to overcome this limitation. For spectrum recovery of nonuniform sampling, a detailed analysis on the relationship between coefficient matrix singularity and the number of effective phase centers (EPCs) passed by the platform during one pulse repetition time (PRT) is given. When the distance passed by the platform during one PRT is multiple of the distance between two EPCs plus a small value, the coefficient matrix is singularity. An explicit expression of the coefficient matrix is also derived.

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between 1 and 0 norms, is proposed to measure the sparsity. Gradient projection is used to solve a constrained nonconvex SOONE function minimization problem and recover the sparse signal. However, while the gradient projection method is computationally simple, it is not robust when a matrix in the algorithm is ill conditioned. We thus further propose using diagonal loading and singular value decomposition methods to improve the robustness of the algorithm. In order to handle targets with large flat surfaces, a combined amplitude and total-variation objective function is also proposed to regularize the shapes of the flat surfaces. Simulation results show that the proposed gradient projection of SOONE function method is better than orthogonal matching pursuit, CoSaMp, l1-magic, Bayesian method with Laplace prior, smoothed l0 method, and l1-ls in high SNR cases for recovery of ± 1 random spikes sparse signal. The quality of the simulated 3D images and real data ISAR images obtained using the new method is better than that of the conventional correlation method and minimum l2 norm method, and competitive to the aforementioned sparse signal recovery algorithms.

Bistatic inverse synthetic aperture radar (BiISAR) imaging provides complementary information to monostatic ISAR imaging. A suggestion that the conventional bistatic ISAR can be replaced by an equivalent monostatic radar is shown to be inadequate in this paper. We prove that the BiISAR image is a sheared version of the projection of the target on the range-Doppler plane. A bistatic interferometric radar configuration is proposed to correct this distortion and to form 3-D image. Simulation results have validated our analysis and shown the viability of the proposed 3-D imaging algorithm.

A conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Three-dimensional imaging using an interferometric technique cannot separate scatterers that have been projected onto the same range-Doppler unit. Multiple-input–multiple-output (MIMO) radar, however, in addition to having a wide virtual aperture and a high cross-range resolution, could also obtain a target's 3-D image in one snapshot. We discuss in this paper the use of time-domain information to improve MIMO radar's imaging quality. A 3-D image alignment algorithm and a rotation vector estimation method are discussed. Simulation results show that image SNR is improved and cross-range sidelobes are mitigated.

Conventional inverse synthetic aperture radar image is a 2-D range-Doppler projection of a target and does not provide 3-D information. Its formation also requires complex motion compensation when the target is uncooperative and maneuvering. On the other hand, multiple-input and multiple-output (MIMO) radar, in addition to having a wide virtual aperture and high cross-range resolution, could also obtain a target's 3-D image in one snapshot and thus have avoided the complex motion compensation needed. In this paper, we propose a 3-D imaging algorithm using three MIMO configurations. The signal model is derived based on a modified zero correlation zone code. A strong scatterer selection criterion is also proposed for the construction of the target profile.

Large 2-D sparse array provides high angular resolution microwave images but artifacts are also induced by the high sidelobes of the beam pattern, thus, limiting its dynamic range. CLEAN technique has been used in the literature to extract strong scatterers for use in subsequent signal cancelation (artifacts removal). However, the performance of DFT parameters estimation based CLEAN algorithm for the estimation of the signal amplitudes is known to be poor, and this affects the signal cancelation. In this paper, DFT is used only to provide the initial estimates, and the maximum likelihood parameters estimation method with steepest descent implementation is then used to improve the precision of the calculated scatterers positions and amplitudes. Time domain information is also used to reduce the sidelobe levels. As a result, clear, artifact-free images could be obtained. The effects of multiple reflections and rotation speed estimation error are also discussed. The proposed method has been verified using numerical simulations and it has been shown to be effective.

Conventional antenna array 3-D inverse synthetic aperture radar (ISAR) imaging method does imaging by first carrying out ISAR imaging for all the different antennas and then using array direction of arrival (DOA) estimation on every scatterer to get the position of that scatterer. The aperture formed by the target's motion is used to separate the scatterers, and the antenna array aperture is used for position measurement. In this letter, a 3-D imaging method based on a combination of sparse array beamforming and ISAR imaging is proposed, where 3-D images are formed by space-time match filtering. The estimation of the target's rotation parameters is also given. Simulation results have shown the effectiveness of the proposed methods.

In this paper, a 3D inverse synthetic aperture radar (ISAR) imaging method based on an antenna array configuration is proposed. The performance of conventional interferometric ISAR imaging system using three antennas is poor, as the positions of scatterers, which have the same range-Doppler value and projected onto the ISAR plane as a synthesis scatterer, cannot be correctly estimated. However, by using two antenna arrays perpendicular to each other, the system's ability to separate these scatterers can be improved. The criterion for the selection of a range unit that contains an isolated scatterer in the 2-D array domain for doing motion compensation is discussed. If there is no range unit which contains only an isolated scatterer, then radial and cross-range motion compensation has to be carried out by motion parameter estimation. Two cross-range motion parameters measurement algorithms, one based on array processing of the range profile and another based on correlation of ISAR images of different antennas, are proposed. The coordinates registration problem for the scatterers of a synthesis scatterer is also discussed. Simulation results have shown the effectiveness of the proposed methods.

Conventional envelope alignment method of ISAR imaging assumes that the target is a rigid body, and therefore, the correlation between one-dimensional range profiles at neighboring time slots gives a larger value compared to correlation between time separated range profiles. But this is not the case for targets such as helicopter which have rotating body parts. In this paper, an envelope alignment algorithm for helicopter target ISAR imaging is proposed. For every range profile, correlation with other range profiles are done, and the range profiles with larger correlation value are used to provide an initial estimate of the target motion parameters by polynomial fitting. Then the estimated parameters are averaged to get a better estimation. The position of the rotor signals are obtained by taking the difference of the range profiles in the slow time domain. The rotor signals are then suppressed by zero-force windowing technique. Simulation results have shown the effectiveness of this algorithm.

In ISAR imaging, most works have assumed the target to be a rigid body, a body without any rotating, vibrating or moving parts. The rotation of structures in a target, such as the rotor of a helicopter or the turret of a tank target, may induce frequency modulation on the returned signals and generate sidebands about the center frequency of the target’s body doppler frequency, known as the micro-doppler phenomenon. In this paper we present a new method based on range grouping of the target’s range profile for the separation of the rotating or moving parts from the target’s body. The method is carried out on the Range Profile Image, different from other methods that do the separation at the signal level.