Distributed multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) provides a new paradigm for radar imaging, which utilizes multiple distributed sensors to improve imaging performance. However, synchronization errors have a significant impact on imaging quality in these systems. The transmitted and received echo signals exhibit reciprocity, which can be exploited to estimate synchronization errors. By comparing echoes between different sensors, the synchronization errors could be estimated and compensated. This work presents a synchronization error-resistant imaging algorithm for distributed MIMO-SAR systems. First, the synchronization errors are estimated in the range domain by comparing the reciprocal echo signal pairs. Then, the errors are compensated during a fast back-projection (BP) based SAR imaging process. The effectiveness of the proposed algorithm has been verified by experiments.

High frame-rate imaging of synthetic aperture radar (SAR), known as video SAR, has received much research interest these years. It usually operates at extremely high frequency and even THz band as a technical tradeoff between high frame rate and high resolution. As a result, video SAR system always suffers from limited functional range due to strong atmospheric attenuation of signals. This article attempts to present a new high frame-rate collaborative imaging regime in microwave frequency band based on swarm of unmanned aerial vehicles (UAVs). The spatial degrees of freedom are employed to shorten the synthetic time and thus improve the frame rate. More specifically, the long synthetic aperture is split into multiple short sub-apertures, and each UAV-borne radar implements short sub-aperture imaging in a short time. Then, the accelerated fast back-projection algorithm is employed to fuse multiple sub-images to produce an image with high azimuth resolution.To implement the collaborative working of swarm of UAV-borne radars, a suitable orthogonal waveform is selected and a useful spatial configuration of the swarm is designed to compensate for the effect of the orthogonal waveform on imaging. Simulation results have been presented to highlight the advantages of collaborative imaging using swarm of UAV-borne radars.

Compressive sensing (CS) enables high-resolution inverse synthetic aperture radar (ISAR) imaging with limited measurements. However, these methods reconstruct images via iterative optimization, resulting in a high computational load. Recently, convolutional neural networks (CNNs) have been used to perform super-resolution ISAR imaging in real time, where high-resolution images are necessarily used as ground truth. However, the desired high-resolution images are not reliable in practice. This letter presents an unsupervised CNN-based framework for super-resolution ISAR imaging. The well-trained CNN can directly produce high-resolution ISAR images in real time. Moreover, the network is trained in an unsupervised manner, which is suitable for practical applications. Furthermore, a pseudo$\ell _{0}$-norm has been used as the sparse constraint for the exact image reconstruction. The proposed approach has been used to process the real ISAR data, and the experimental results are convincing.

Track-Before-Detect (TBD) algorithm has been used to track weak moving target shadow in video synthetic aperture radar (SAR), where strong maneuverability may deteriorate tracking performance. Fortunately, the Doppler characteristic of target can additionally provide velocity guidance for tracking. This article presents ground moving target indication (GMTI) results of the multi-channel video SAR raw data, and a tracking approach is proposed based on the Doppler supervision in an improved dynamic programming-based TBD (DP-TBD) framework that uses a dual-domain merit function. Both the sequential high-resolution SAR images and low-resolution Range-Doppler (RD) spectra are used, and the clutter in RD spectrum is suppressed by using an adaptive displaced phase center antenna (ADPCA) technique. Fine states can be searched in dual-domain through resampling from random expansion in state initialization and transition. The inverse shadow amplitude and Doppler energy of the same potential target are simultaneously integrated to determine whether the target exists. The proposed Dual-DP-TBD can deal with the tracking of a time-varying number of targets in successive measurements. Compared to other TBD algorithms, this approach has fewer missing alarms and false alarms thanks to precise velocity estimation constrained from diverse features both in SAR image and RD spectrum.

Sea-surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multiframe detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential range–Doppler (RD) spectra. A two-step detection framework is proposed, where the intraframe and interframe detections are achieved using the differences in features and interframe correlations between the moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multiframe detection algorithms. Additionally, this approach exhibits acceptable generalization ability.

It has long been recognized that synthetic aperture radar (SAR) images suffer in many applications from the speckle noise. Video SAR has a high frame rate of imaging and contains redundant information among frames. The temporal redundancy in video SAR images has been found useful for suppressing the speckle noise. However, the motion and the local differences between frames make it difficult to employ the temporal redundancy to suppress speckle noise. This article presents a video SAR image despeckling framework based on a new unsupervised training strategy referred to as DualNoise2Noise. This developed framework consists of a registration network and a denoising network. The registration network first compensates for the motion between two adjacent frames in video SAR in real time. After image registration, two adjacent frames with random speckle noise can be considered as the observations of the same region with local differences. The denoising network adopts the DualNoise2Noise training strategy to suppress speckle noise by using the temporal redundancy and to remove the negative impact caused by the local differences. The proposed approach has been used to process the real video SAR data, and the experimental results are convincing.

This article presents a novel radar imaging approach through an electronically displaced-phase-center antenna (eDPCA). In this approach, a reflectarray is used to directly scan a beam across a parabolic reflector, where the antenna phase center is displaced electronically. The proposed eDPCA-based imaging radar can provide a very high frame rate and does not depend on the motion between the target and the radar platform. The feasibility of the eDPCA-based imaging radar is investigated and validated through a designed example eDPCA at 157 GHz and the simulation experiments for a point target and a complex target, respectively. The effects of practical impairments such as errors in phase centers and position-dependent fluctuations in antenna patterns on the imaging quality are examined. Furthermore, by applying the precompensation method suggested in this article, the imaging procedure could be performed by using classic synthetic aperture radar (SAR) algorithms.

MOTIVATION: Small non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), play key roles in many biological processes. However, only a few tools can be used to develop the optimal primer or probe design for the expression profile of small ncRNAs. Here, we developed sRNAPrimerDB, the first automated primer designing and query web service for small ncRNAs. RESULTS: The primer online designing module of sRNAPrimerDB is composed of primer design algorithms and quality evaluation of the polymerase chain reaction (PCR) primer. Five types of primers, namely, generic or specific reverse transcription primers, specific PCR primers pairs, TaqMan probe, double-hairpin probe and hybridization probe for different small ncRNA detection methods, can be designed and searched using this service. The quality of PCR primers is further evaluated using melting temperature, primer dimer, hairpin structure and specificity. Moreover, the sequence and size of each amplicon are also provided for the subsequent experiment verification. At present, 531 306 and 2 941 669 primer pairs exist across 223 species for miRNAs and piRNAs, respectively, according to sRNAPrimerDB. Several primers designed by sRNAPrimerDB are further successfully validated by subsequent experiments. AVAILABILITY AND IMPLEMENTATION: sRNAPrimerDB is a valuable platform that can be used to detect small ncRNAs. This module can be publicly accessible at http://www.srnaprimerdb.com or http://123.57.239.141. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimation of voltage sags has become increasingly crucial with higher penetration of sensitive devices in the system recently. Single-event indices for voltage sags are essential to form the whole system of estimation. However, it is still difficult to tell how serious a sag is even after the assessment value is got, for there's no reference value to compare with. This paper firstly discussed indices of single-event sags. Then the voltage sag energy was recommended as a typical index irrelevant to the sensitive curves. In order to more intuitively describe the severity degree, a novel classification of severity level was proposed by using the reference values. In detail, CP95, CP75, CP50, and CP25 of the index of numerous sag events were determined as the reference values. To make up for the shortage of the Evs, the events come from not only measured data in grids but also the standard sensitive curves. To determine more reasonable and generalized reference values, the analytic hierarchy process (AHP) was applied to synthesize the reference values by giving each value a weight according to their different significance. Numerical examples show the conciseness and the clarity of the classification method which can provide a more intuitive view of the severity of a single-event sag for customers, device manufactures and the utility.

Mobile ad-hoc networks (MANETs) are based on cooperative and trust characteristic of mobile nodes. Typically, nodes are both autonomous and self-organized without requiring a central administration or a fixed network infrastructure. Due to their distributed nature, MANETs are very vulnerable to various attacks. To enhance the security of MANETs, it is important to rate the trustworthiness of other nodes without central authorities to build up a trust environment. In this paper, we propose a fuzzy trust recommendation based on collaborative filtering, which stimulates collaboration among distributed computing and communicating nodes, facilitates the detection of untrustworthy nodes, and assists decision-making in various protocols for MANETs. Due to the uncertain interaction outcomes, we use fuzzy logic to model trust recommendation in a MANET environment. Our trust model combines direct trust and trust recommendation information based on collaborative filtering to allow nodes to represent and reason with uncertainty and imprecise information regarding other nodespsila trustworthiness. Simulation results show that the proposed model is flexible and valid.