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Block sparse Bayesian learning (BSBL) has been widely used in inverse synthetic aperture radar (ISAR) imaging, which significantly improves the imaging performance by exploiting the sparse pattern information of ISAR images. However, the conventional Bayesian learning algorithm has high computational complexity, which hinders its applications to real-time processing of radar imaging. The approximate message passing (AMP) can be used to obtain a low complexity implementation of sparse Bayesian learning (SBL). However, AMP suffers from performance losses and even diverges in the case of high-Doppler resolution ISAR imaging where the measurement matrix can be highly correlated. To solve this problem, we propose a fast pattern coupled SBL ISAR imaging algorithm based on approximate message passing with unitary transformation (UTAMP). First, the estimates of the hyperparameters of sparse vector are obtained through UTAMP based SBL, and then nearest neighbor hyperparameters are coupled and updated for next iteration. With low complexity, the proposed algorithm can effectively exploit the sparse pattern information of ISAR images, and exhibits excellent convergence and imaging performance. Both simulation and real data experiments are carried out to verify the effectiveness of the proposed algorithm.

Some new problems in distributed inverse synthetic aperture radar (ISAR) fusion imaging may be caused by constant frequency errors (CFE) due to the imperfect match of two independent oscillators. In this study, the effects of the CFE on distributed ISAR coherent fusion imaging are first analysed, which show that the CFE will produce false images (incorrect scatterer number and position) in distributed ISAR fusion imaging. To eliminate the effects of the CFE, a novel CFE calibration scheme is devised by exploiting the echo signal phase relationship between different sensors and making some reasonable approximations. The echo signal of the active radar (transmit and receive) is used as a reference signal. After some derivation, the phase relationship between the reference signal and other signals with CFE can be derived. Based on the derived phase relationship, the CFE of each radar will be estimated by multiple signal classification algorithm. Then, the estimated CFE value is further used to calibrate the CFE of each radar before signal coherent fusion. As a result, the false image is eliminated. Simulation results prove the authors theoretical analysis and the effectiveness of their method.

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition, this method uses enumeration algorithm to solve the optimization problem, which has a heavy computation burden. Therefore, a robust calibration method based on the contrast maximization principle is proposed. Compared with image entropy, image contrast has better anti-noise ability because it has better sensitivity property, namely, the change of image contrast is sharper than the change of image entropy. In the proposed method, the estimation of frequency error coefficient is modelled as an unconstrained optimization problem with image contrast as cost function, and the particle swarm optimization (PSO) algorithm is used to search the global optimal solution. Then, the LTFE can be calibrated by the estimated frequency error coefficient. The proposed method has better robustness, which can work well under low SNR conditions. Besides, it has higher computational efficiency. Simulations are carried out to verify the effectiveness and robustness of the proposed method.

Radar observations from different angles are often discontinuous in multistatic inverse synthetic aperture radar (ISAR) imaging. Based on Fourier transform, such as Polar Format Algorithm and Range Doppler Algorithm, the discontinuity of the angle will make the performance of traditional ISAR imaging algorithm worse. The sidelobe of the image will rise and the mainlobe may split. Generally, it is necessary to pre-process the gapped data and then the traditional ISAR imaging algorithm is used for imaging. The most commonly used pre-processing method is to interpolate the gap. However, the performance of this method is not satisfied, especially when the gap is large. The reason of sidelobe rising and mainlobe splitting is first analysed. Then, a sidelobe reduction method based on compressive sensing (CS) is proposed. This method establishes a relationship between the complete data and the gapped data, and the complete data can be solved from the gapped data by CS method. After that, the complete data will be used for imaging by utilising traditional ISAR imaging algorithm and the high sidelobe will be reduced effectively. The effectiveness of the proposed method is verified by the analysis and the simulation results.

In this paper, a robust target localization method based on iterative reweight least squares (IRWLS) is presented to against outliers in distributed MIMO radars. Unlike conventional weight least squares model which derived from the maximum likelihood estimator (MLE), the proposed robust global cost function is established based on least absolute model. Since the least absolute is nontrivial to solve directly, we convert it into a reweight least squares problem. After that, we linearize the relation between target location and bistatic range (BR) without introducing any nuisance parameters, which is different from existing linearization methods. The solution of proposed method can be obtained efficiently via iteration with closed-form expression. Simulation results validate the robust performance of proposed method under low SNR condition and outliers.

The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

Distributed small satellites flying in close formations are deployed in the low Earth orbit for Earth observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular, and temporal dimensions simultaneously. All these missions require inter-satellite links (ISLs) with guaranteed performance to track and maintain the satellites in a desired geometric configuration. The data exchanged should then be arrived timely in order to control the inter-satellite distance collaboratively. Although many protocols are developed to apply in different communication layer, there has been little work done in self-organizing time division multiple access (STDMA) in data link layer for the inter-satellite communication.This paper addresses an adaption of STDMA in order to reduce the overall development costs of novel space missions. Besides, STDMA has been simulated in a mesh formation scenario with periodic broadcast packets compared to Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Finally, simulation results show that STDMA has a higher packet reception probability in the scenario and thereby a better reliability.