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A periodic mirror-reflected circular-polarized (CP) leaky wave antenna (LWA) with dual beam-scanning in dual polarization types is reported in this paper. The unit cell of the proposed antenna consists of two oblique stubs that are orthogonally separated (90°) to create a circular polarization. In order to achieve better CP (axial ratio (AR) < 3dB) and beam-scanning characteristics for the periodic CP antenna, a new method of adding a groove at the junction of stubs within the unit cell is introduced. To realize dual beam-scanning in dual circular polarization, a metal via is added at the end of the antenna to generate a mirror reflection. A mirror-reflected structure is fabricated and measured. The measured results show that the right-handed CP (RHCP) scanning range of the incident beam is -31°~-8° and 5°~-14°, and the left-handed CP (LHCP) scanning range of the corresponding reflected beam is 31°~6° and -6°~-15°. The measured results are in good agreement with the simulated ones.

Extracting distinctive, robust, and general 3D local features is essential to downstream tasks such as point cloud registration. However, existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural architectures. It remains challenging to learn robust and general local feature descriptors for surface matching. In this paper, we propose a new, simple yet effective neural network, termed SpinNet, to extract local surface descriptors which are rotation-invariant whilst sufficiently distinctive and general. A Spatial Point Transformer is first introduced to embed the input local surface into an elaborate cylindrical representation (SO(2) rotation-equivariant), further enabling end-to-end optimization of the entire framework. A Neural Feature Extractor, composed of point-based and 3D cylindrical convolutional layers, is then presented to learn representative and general geometric patterns. An invariant layer is finally used to generate rotation-invariant feature descriptors. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities.

In this paper, a 1-bit wideband electronically reconfigurable reflectarray (RRA) in Ku-band is proposed. In order to achieve the wideband and reconfigurable characteristics, the stacked microstrip structure and a 1-bit “microstrip line-slot line-microstrip line” phase shifter (MSMPS) were introduced in this design. A novel “receiving-phase shift-transmitting” unit cell was gotten by connecting two stacked microstrip structures using MSMPS. Due to the receiving and transmitting parts are perpendicular to each other, the polarization rotation characteristic was demonstrated. To avoid the blockage of the feed horn, it was placed above the reflecting surface array with an offset angle of 25° to the normal direction. A $16\times16$ obliquely-fed RRA was designed and fabricated. The measured 1-dB gain bandwidth of the RRA is 15.4%. The measured results show that the fabricated prototype can achieve beam scanning from −20° to 50° in the elevation plane, and ±50° beam scanning in the azimuth plane.

In this paper, a phased array antenna with an integrated metasurface is designed to achieve wideband and wide-angle scanning. Generally, the coupling between adjacent elements changes significantly as the scanning angle increases, resulting in limited scanning coverage. Two mutually perpendicular metasurface slabs are designed to achieve wide-angle scanning. The mutual coupling is reduced by properly tuning the dimension of the metasurface. Two perpendicular linear arrays were fabricated to verify the design. The measured 3-dB beamwidth of the active element pattern (AEP, in the array environment) with the integrated metasurface is about 150° in the E-plane and 109° in the H-plane, which is suitable for wide-angle scanning. The simulated and measured results indicate that the beam can scan from -78° to 78° with a 3-dB gain fluctuation in the E-plane array and a wide frequency range from 10 GHz to 12.5 GHz (22.2%). In the H-plane array, the scan angle ranges from -69° to 69° with a 3-dB gain fluctuation and a relative bandwidth of 11%.

The multipath effect deteriorates the performance of low elevation target altitude estimation with a ubiquitous radar strongly, especially in complex terrain environments in which multiple reflection paths exist simultaneously. This work shows how the authors reconstructed a multipath model for ubiquitous radar with a known waveform and took advantage of it to improve the altitude measurement performance when locating low‐altitude small targets in complex terrain environments. The authors first present a multipath propagation model for target elevation angle estimation in which not only received multipaths but also transmitted multipaths are taken into consideration. Then, a reconstructed elevation angle estimation model in which the waveform is known and the target elevation information is as same as the original model is derived. Finally, an innovative low elevation angle estimation algorithm based on a maximum likelihood estimator and a sparse representation is proposed. Simulations and experiment results verify that the proposed algorithm has a much more robust low elevation angle estimation performance than existing methods in complex terrain environments. Index Terms—Target altitude measurement, complex multipath effect, known transmitted waveform, sparse representation, ubiquitous radar.

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.

In this letter, a 1-bit wideband reconfigurable unit cell for transmitarray applications in Ku-band is proposed. The unit cell consists of a Vivaldi structure (transmitting), a phase shifter, and a microstrip Yagi structure (receiving). Based on the current reversal mechanism, a pair of reverse mounted PIN diodes was introduced to obtain the required phase resolution (two phase states: 0° and 180°). The unit cell is fabricated and measured using a specific waveguide simulator. The measured minimum insertion losses are 1.58 and 1.61 dB for State I and II, respectively. The insertion loss is less than 2.58 dB (1 dB bandwidth) from 14.88 to 17.28 GHz for State I, and less than 2.61 dB (1 dB bandwidth) from 14.73 to 17.25 GHz for State II. The 1 dB bandwidth of the unit cell is about 15%. The measured phase difference remains 180°, and the maximum error is only 3.8°.

The natural environment is a complex system, and there is never only one kind of nanomaterial entering the environment. However, many studies only considered the plant toxicity of one kind of nanomaterial and do not consider the influence of two or more kinds of nanomaterials on plant toxicity. Multi-walled carbon nanotubes (MWCNTs) and zinc oxide nanoparticles (ZnO NPs) are two common and widely used nanomaterials in water environment, so these two kinds of nanomaterials were chosen to explore the effects of their combined toxicity on cabbage. This study investigated the toxicity of MWCNTs combined with ZnO NPs on cabbage by measuring the length of roots and stems, chlorophyll content, oxidative stress, antioxidant enzyme activity, metal element content, and root scanning electron microscopy. The toxicity of single MWCNTs toward cabbage was attributed to direct oxidative damage, while the toxicity of single ZnO NPs toward cabbage was due to the high level of zinc concentration. Moreover, ZnO NPs (10 mg/L) ameliorated MWCNTs toxicity toward cabbage by improving the activity of antioxidant enzymes. ZnO NPs (50 and 100 mg/L) because of the high content of zinc disrupted the balance of other metals in the plant and increased the toxicity of MWCNTs. In conclusion, the combined toxicity of different concentrations and types of nanomaterials should be considered for a more accurate assessment of environmental risks.

StochAstic Recursive grAdient algoritHm (SARAH), originally proposed for convex optimization and also proven to be effective for general nonconvex optimization, has received great attention because of its simple recursive framework for updating stochastic gradient estimates. The performance of SARAH significantly depends on the choice of the step size sequence. However, SARAH and its variants often manually select a best-tuned step size, which is time consuming in practice. Motivated by this gap, we propose a variant of the Barzilai-Borwein (BB) method, referred to as the Random Barzilai-Borwein (RBB) method, to determine the step size for SARAH in the mini-batch setting, leading to a new SARAH method: MB-SARAH-RBB. We prove that MB-SARAH-RBB converges linearly in expectation for strongly convex objective functions. Moreover, we analyze the gradient complexity of MB-SARAH-RBB and show that it is better than the original method. To further confirm the efficacy of the RBB method, we propose the MB-SARAH+-RBB method, by incorporating it into the MB-SARAH + method. Numerical experiments on standard data sets indicate that our proposed methods outperform or match state-of-the-art algorithms.

Parameter estimation of chirp signals plays an important role in the field of radar countermeasures. Compressed sensing (CS) based sub-Nyquist sampling and parameter estimation methods alleviates the pressure on hardware systems to acquire and process chirp signals with large time-bandwidths. In this paper, a framework based on the fractional Fourier transform (FrFT) and alternating direction method of multipliers network (ADMM-Net) is proposed to realize chirp signal parameter estimation under sub-Nyquist sampling. The whole framework is composed of multiple parallel ADMM-Nets, where each ADMM-Net is defined over a data flow graph, which is derived from the iterative procedures of the ADMM algorithm for optimizing a CS-based $p$ -order FrFT spectral estimation model. The chirp rate and central frequency of chirp signals are obtained through a two-dimensional search on the spectrum image output by the network group. Experiments demonstrate that the proposed ADMM-Net-based method can achieve higher estimation accuracy and computational efficiency at lower signal-to-noise ratios and sampling ratios than traditional CS methods. We also demonstrate that the proposed ADMM-Net-based framework has strong generalization ability for multi-component chirp signals. Furthermore, we further generalize ADMM-Net to GADMM-Net, in which the activation function is data-driven instead of model-driven. Experiments demonstrate that GADMM-Net significantly improves on the basic ADMM-Net and achieves higher spectral resolution with faster computation speed.

Near-infrared diffuse reflectance spectroscopy, with the characteristics of simplicity, speed, and nondestructive analysis, has been frequently used in tobacco blending design and maintenance. Its main function is to identify the appropriate combination from hundreds of types of tobacco leaves to match the target formula. Due to the large number of combinations of tobacco leaves, preparing so many blended samples of tobacco powder with different proportions and measuring their spectra are time-consuming, laborious, and costly. Therefore, a novel strategy of simulated spectra based upon calibration transfer is proposed to substitute for the measured spectra without preparing these tobacco powder blended samples. Five and nine single-grade tobacco powder blending experimental results showed that the substitutive spectra for a massive tobacco powder blended samples may be obtained in a short time according to the established calibration transfer models between measured and simulated spectra of a small number of blended samples. Moreover, with the help of calibration models built on the same spectrometer, the desired properties including reducing sugar, total sugar, potassium, starch, and total nitrogen of these tobacco mixtures are quickly predicted. In addition, transfer based on canonical correlation analysis and independent component analysis were superior to other approaches, respectively. The strategy of simulated spectra partially may replace powder blending and spectral analysis and may be considered to be a valuable and effective tool for computer-aided numerical tobacco blending.

Pyrite and engineering carbon materials have received increasing attention for their catalytic potential in Fenton reactions due to their extensive sources and low cost. However, effects of carbon materials on the degradation of pollutants by pyrite-catalyzed heterogeneous Fenton oxidation have not been fully understood. In this study, the performance of pyrite-catalyzed heterogeneous Fenton system on the degradation of ciprofloxacin (CIP) was investigated in the presence of activated carbon (AC), biochar (BC), and carbon nanotubes (CNTs). Synchronous and asynchronous experiments (adsorption and catalysis) were conducted to elucidate the roles of the carbon materials in pyrite-catalyzed Fenton reactions. The results demonstrated that all the three carbon materials accelerated the pyrite-catalyzed Fenton oxidation of CIP. Under the experimental conditions, the reaction rates, which were obtained by fitting the synchronous experimental results with the pseudo-first-order kinetic model, of pyrite/AC, pyrite/BC and pyrite/CNTs with H

The combined eco-risks of Sb (widely presented in soils, especially nearing mining areas) and the engineering nanomaterials (ENMs) (applied in agriculture and soil remediation) still remain uncovered. The current study investigated the impacts of single and combined exposure of CuO, CeO

Based on the metasurface, a large-angle beam-scanning phased array design is introduced in this paper. Due to the mutual coupling changes significantly with the increase of the scanning angle, the scanning coverage is generally limited. The metasurface design is an effective method to overcome this problem. Generally, the metasurface is designed as an impedance matching layer and placed in parallel at some distance above the array. Here, a metasurface composed of periodically arranged subwavelength cross-shaped metals is designed. The designed metasurface is used as the decoupling wall to broaden the scanning coverage.

Recently, the staring radar has been developed and used to detect and track micro-UAVs. It shows significant advantages in the surveillance of such slow-moving targets with low RCS for its sensitivities and high Doppler resolutions. Meanwhile, the feature of wide area transmission of staring radar results in multipath effect, especially for low altitude targets. A novel elevation angle estimation approach for staring radar is proposed in order to measure the height of targets. Field trials with micro-UAVs show that the proposed approach can help staring radar to obtain target height parameter accurately.

The interrupted-sampling repeater jamming (ISRJ) is coherent with an emitted signal, and significantly limits radar's ability to detect, track and recognise targets. This study focuses on the research of ISRJ suppression for linear frequency modulation radars. A new band pass filter design method based on time frequency (TF) analysis is proposed. A function named ‘max-TF’ is constructed from the TF energy distribution of the de-chirped signal, reflecting the changes of the maximum signal component amplitude with respect to time. Based on the ‘max-TF’ function, jamming-free signal segments are automatically and accurately extracted to generate the filter, which is smoothed subsequently. After filtering, jamming signal peaks in pulse compression results are suppressed while real targets are retained simultaneously. Comparing with the state-of-the-art filtering method, the proposed method has improved jamming suppression ability and extended the feasible scope of signal-to-noise ratio and jamming-to-signal ratio conditions. Simulations have validated the improvements and demonstrated how the parameters affect performance. The average signal to jamming improvement and average radar detection rate of the proposed method is about 7.4 dB and 23% higher than those of the state-of-the-art filtering method, respectively. The direction of further works is inferred.

The wideband radar intermediate frequency direct acquisition module is used to acquire, process and transmit data, which is an important part of radar system. Based on the current advanced Virtex-7 FPGA board and high-speed ADC card, this article designs the module from two aspects of hardware design and hardware development, including structure design, data flow design, and FPGA top-level design. Then the article researches on some key technologies involved, including JESD204B protocol configuration, data flow transmission, DDR3 buffer design and clock optimisation. Practice has proved that the design of this article can accomplish the task well in practical engineering.

Interrupted-sampling repeater jamming (ISRJ) is coherent jamming based on digital radio frequency memory (DRFM) device, which repeatedly samples, stores, modulates, and retransmits part of the radar emitted signal, and flexibly forms false targets in the victim radar with relatively low transmitting power. It significantly interferes the radar to detect, track, and recognize targets. There are many electronic counter-countermeasures against ISRJ, among which a series of filtering methods are promising. However, it is not fully addressed. This study proposes a filtering method based on stacked bidirectional gated recurrent unit network (SBiGRU) and infinite training to fulfill the ISRJ suppression for pulse compression (PC) radar with linear frequency modulation (LFM) waveform. SBiGRU method converts signal extraction into a temporal classification problem and accurately extracts the jamming-free signal segments to generate a band pass filter to suppress the ISRJ and retain the real target signal components simultaneously. Comparing with two most advanced filtering methods in the published literature, SBiGRU method has improved the jamming-free signal extraction accuracy, leading to better performances of ISRJ suppression and real targets detection, which are verified by Monte Carlo Simulations.

Recently, passive bistatic radar (PBR) has become an emerging technology because it allows target detection and localization with advantages of low cost, covert detection, low vulnerability to electronic jamming and high valuable counter to stealth technology. However, these researchers mainly focus on the illuminators of opportunity such as FM radio, television broadcast, mobile network basestation, wireless fidelity, long term evolution, global navigation satellite, etc. Different from the aforementioned illuminators, this paper introduces a new PBR using a non-cooperative frequency agile phased array radar illuminator. The types of illuminator of opportunity are enriched, whereas the difficulties in PBR signal processing also come along. On one hand, since PBR works in a non-cooperative mode, there is a problem that the instantaneous parameters of transmitting signal are unknown at the receiver. On the other hand, the frequency agility technology destroys the coherency between pulses and the ability of rapidly changing beam scanning makes it difficult for PBR to realize space synchronization. Under such scenarios, conventional radar signal processing methods cannot be conducted effectively. To address these problems, this paper then proposes a feasible target detection and localization method, which is tested by field experiments. Preliminary results validate the proposed method can be applied to PBR system successfully.

This paper addresses the problem of estimating the parameters of constant-amplitude chirp signals that have single or multiple components and are embedded in noise. Chirp signals are widely employed in applications such as radar and telecommunications, and it is a key task in countermeasure techniques to estimate their parameters without prior information. Hence, a parameter estimation processor based on a complex-valued deep neural network (CV DNN) is proposed to perform this task efficiently. The CV DNN, which is designed for regression, consists of a function fitter and a predictor. The function fitter acts like a eigenfunction mapping: it maps the one-dimensional input into a two-dimensional feature map suitable for subsequent network learning. As a special feature extraction tool, the predictor extracts local features from the feature map and estimates parameters. Simulation results indicate that the CV DNN outperforms conventional processors. Moreover, it is more accurate than the Wigner-Hough transform while being several orders of magnitude faster, which will enable real-time signal processing with fewer computational resources. Furthermore, we demonstrate that the CV DNN shows strong robustness to changes in modulation parameters and the number of components of a chirp signal. This study shows the advantages of deep learning systems for signal parameter estimation.

The ability of microplastics (MPs) to interact with environmental pollutants is of great concern. Riverine sediments, as sinks for multi-pollutants, have been rarely studied for MPs risk evaluation. Meanwhile, MPs generated from biodegradable plastics are questioning the safety of the promising materials. In this study, we investigated the effects of typical non-degradable polyethylene (PE) and biodegradable polylactic acid (PLA) MPs on sediment enzymes, arsenic (As) fractionation, and microbial community structures in As-contaminated riverine sediments. The results indicated that the presence of MPs (1% and 3%, w/w) led As transformed into more labile and bioavailable fractions in riverine sediments, especially under higher As and MPs levels. Analysis on microbial activities and community structures confirmed the strong potential of MPs in inhibiting microbial activities and shifting bacterial community succession patterns through enrichment of certain microbiota. Moreover, biodegradable PLA MPs presented stronger alterations in arsenic fractionation and microbial community structures than PE MPs did, which might be jointly attributed to adsorption behaviors, microbial alterations, and potential PLA degradation behaviors. The study indicated that MPs contamination increased As mobility and bioavailability, and shifted microbial communities in riverine sediments. Moreover, biodegradable MPs might lead to stronger microbial alterations and increases in As bioavailability, acting as a threat to ecological safety, which needed further exploration.

Nanoparticles (NPs) are widely used for their special physical properties and released into the natural environment. When two types of NPs exist in the same environment, the presence of one type of NP may affect the properties of the other type of NP. This study investigated the toxic effects of multi-walled carbon nanotubes (MWCNTs) and copper oxide nanoparticles (CuO NPs) on Tetradesmus obliquus. Both NPs had toxic effects on algae, and the toxic effects of MWCNTs were significantly stronger than CuO NPs which the 96-hr median effective concentration to algae were 33.8 and 169.2 mg/L, respectively. Oxidative stress and cell membrane damage were the main reasons for the toxicity of NPs to algae, and they were concentration-dependent, and the existence of CuO NPs in some groups reduced cell membrane damage caused by MWCNTs which may because that CuO NPs formed heteroaggregation with MWCNTs, reducing the contact of nanoparticles with cell membranes, then reducing physical damage. Scanning electron microscopy (SEM) and transmission electron microscope (TEM) results indicated cell damage, the heteroaggregation of MWCNTs-CuO NPs and obvious nanoparticles internalization. In some groups, the presence of CuO NPs significantly reduced reactive oxygen species (ROS) level induced by MWCNTs. However, for the highest concentration group, the ROS level was much higher than that of the two NPs alone treatment groups, which might be related to the high concentration of MWCNTs promoting the internalization of CuO NPs. MWCNTs and CuO NPs affected and interacted with each other, causing more complex toxic effects on aquatic organisms.

Synthetic aperture radar (SAR) imagery is widely applied to recognize target ships in marine surveillance since it can operate in all-weather day-and-night conditions. Deep learning and neural networks have also been used more frequently in recent years with SAR data and have achieved significant progress. However, it takes a lot of energy for deep neural networks to learn and hinders the further development of SAR technology. Compared with the traditional deep neural networks, Spiking Neural Networks (SNNs) have temporal information processing ability, low power consumption, and high biological plausibility, which are suitable for future radar intelligent sensing. In this paper, we take inspiration from SNNs and propose an energy-efficient method, namely SpikingSAR. The experimental results show that the proposed method outperforms the state-of-the-art deep learning methods as well as the conventional methods, while it also has fewer model parameters.

In the past few decades, the demand for reliable and robust systems capable of monitoring unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signature (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the UAVs and birds. After the separation, rotating parts will be obtained without the interference of the body components, and the m-DS features can also be revealed more clearly, which is conducive to feature extraction. What is more, there are some problems in using m-DS only for target classification. Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram. Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of L band staring radar. In this algorithm, to make full use of the information in the spectrogram and supplement the information in exceptional situations, m-DS, movement, and energy aggregation features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically, the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates the effectiveness of the proposed algorithm.

Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.

Plastic discharged into the environment would break down into microplastics (MPs). However, the possible impact of MPs on heavy metals in the aquatic sediment remains unknown. In order to evaluate the potential role of MPs as carriers of coexisting pollutants, the adsorption capacity of lead ions from sediment onto aged degradable and conventional MPs were investigated as a function of lead ions concentration, contact time, temperature, MPs dosage, aging time, and fulvic acid concentration. MPs were exposed to UV to obtain aged polyethylene (A-PE) and aged polylactic acid (A-PLA). The aging treatment increased the oxygen content, specific surface area and hydrophilicity of MPs. The adsorption capacity of A-PE for Pb(II) in sediment increased from 10.1525 to 10.4642 mg g−1 with the increasing aging time. However, the adsorption capacity of A-PLA for Pb(II) in sediment decreased from 9.3199 to 8.7231 mg g−1 with the increasing aging time. The adsorption capacity of MPs in sediment for Pb(II) was in the following order: A-PE > PLA > PE > A-PLA. Fulvic acid could promote the adsorption of Pb(II) by MPs in sediment. These results indicated that the aging process of the plastics in the environment would affect its role as a carrier of coexisting pollutants.

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 $\ell^{0}$ 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.

Synchronous Nyquist folding receiver (SNYFR) is an improved novel broadband receiver architecture that is possible to implement in the engineering. The output signal of SNYFR is usually a hybrid modulated signal, so corresponding signal detection methods need to be investigated, which is the premise of precise parameter estimation. In this paper, a signal detection method based on recursive autocorrelation and mn method for SNYFR is proposed. First, autocorrelation with a fixed length for SNYFR output signal is acquired. Then, in order to determine the existence of the signal, constant false alarm rate (CFAR) detector is introduced into the method to generate an adaptive threshold. Finally, the position where the signal exists is determined by mn method and the signal data are sent to back-end processor for parameter estimation. The proposed algorithm can not only adapt to low signal-to-noise ratio (SNR), but also has a high measurement accuracy with low computational complexity. The simulation demonstrates the viability and validity of the proposed method.

The problem of direction-of-arrival (DOA) estimation is investigated for co-prime linear array, where the co-prime linear array incorporates two uniform linear arrays (ULAs) with adjacent spacing larger than half wavelength. In this paper, we propose an ESPRIT-like DOA estimation method based on gradient descent for co-prime linear array. First, we decompose the entire co-prime array into several subarrays regarding each sensor as a subarray and derive the property of rotational invariance between manifold matrixs of any two subarrays. Then, this paper uses the gradient descent to iterative search the rotational invariance matrix and obtain direction information of the incident signals. The proposed method can achieve full DOFs, and there is no grid search in the range of directions. Simulation results corroborate the effectiveness and superiority of the proposed method, compared with the total spectral search method (TSS) and MUSIC for ULA with the same sensor number.

Ultrahigh frequency ultrawideband (UHF UWB) synthetic aperture radar (SAR) system can penetrate the foliage, get the high-resolution image, and add the scattered information, which can be used to detect the concealed area under the foliage. In fall, the monostatic and bistatic campaign with the airborne UHF UWB SAR system has been conducted. This experiment operates the airborne SAR system, in conjunction with a ground-based stationary receiver, which can simultaneously collect the monostatic and bistatic scattered data. The fast factorized back projection (FFBP) algorithm has been applied to process the monostatic and bistatic SAR scattered data, as well as the subaperture spectrum-equilibrium method. Focused monostatic and bistatic SAR images prove the feasibility and property of the developed airborne monostatic and bistatic UHF UWB SAR.

Low frequency ultrawideband (LF UWB) synthetic aperture radar (SAR) has lately become of a particular interest to SAR community. Monostatic and bistatic LF UWB SAR system has the well foliage penetrating capability, high-resolution imaging and providing the increased information. In 2015, a monostatic and bistatic LF UWB SAR imaging experiment was conducted. In this experiment, the monostatic and bistatic data were collected simultaneously by operating a moving vehicle-based radar in the SAR mode, in conjunction with a stationary ground-based receiver. The aim was to investigate the imaging property of the bistatic LF UWB SAR system. The one pulse per second (1 PPS) signal in combination with the global position system (GPS) disciplined 100 MHz oscillator from the GPS receivers was used to implement the time and frequency synchronization in this SAR system. The bistatic SAR image was obtained by the subaperture spectrum-equilibrium method integrated with the fast factorized back projection (FFBP) algorithm. Bistatic experiment results are show to prove the validity of the bistatic LF UWB SAR imaging experiment.

The treatment of dye pollutant and salts by nanofiltration membrane technology usually has the difficulty of low permeability and higher pressure in the case of maintaining a high rejection. In this study, high flux polyester membranes of β-cyclodextrin/trimesoyl chloride - multiwalled carbon nanotubes (β-CD/TMC-MWCNTs) were fabricated on a modified multiwalled carbon nanotubes (MWCNTs) substrate via interfacial polymerization (IP) process. The modified MWCNTs substrate was not only beneficial to modulating the permeation and distribution of aqueous phase monomer (β-cyclodextrin, β-CD), but also in favor of the formation of an ultrathin loose selective polyester active layer due to the porous network structure. The optimization experiments about the reaction time and monomer concentrations were conducted. The experimental results showed that the pure water flux of the as-prepared β-CD/TMC-MWCNTs membrane was 179.93 L m-2h-1 bar-1 (at the pressure of 0.1 MPa) when the reaction time of IP process was 5 min. The single dye removal efficiency of β-CD/TMC-MWCNTs membrane was 97.41% for Brilliant Green (BG) and 96.39% for Congo red (CR), respectively. The removal efficiency of dye/salt mixture by the membrane was above 84% for BG and over 98% for CR, respectively. The results indicated that the membrane had the efficient selective separation performance for the dye/salt mixture benefiting from the special molecular sieving architectures with the precisely intrinsic inner cavity of β-CD/TMC-MWCNTs membrane. Furthermore, the reusability, chlorine resistance, and antifouling ability of the β-CD/TMC-MWCNTs membrane was relatively excellent, and the membrane could effectively remove BG (>93%) and CR (>98%) simultaneously for mixed BG/CR dyes in real water body. These results suggested that the as-prepared β-CD/TMC-MWCNTs membrane had the potential of practical application in dye wastewater treatment.

Antimony (Sb) is the ubiquitous re-emerging contaminant greatly accumulated in sediments which has been revealed risky to ecological environment. However, the impacts of Sb (III/V) on microbes and plants in sediments, under different water management with presence of engineering materials are poorly understood. This study conducted sequential incubation of sediments (flooding, draining and planting) with presence of multiwall carbon nanotubes (MWCNTs) and Sb to explore the influence on microbial functional diversity, Sb accumulation and alfalfa traits. Results showed that water management and planting led to greater impacts of sediment enzyme activities and microbial community metabolic function and bioavailable Sb fractions (defined as sum of acid-soluble fraction and reducible fraction, F1 + F2). Available fractions of Sb (V) showed higher correlation to microbial metabolism (r = 0.933) than that of Sb (III) (r = −0.480) in planting stage. MWCNTs with increasing concentrations (0.01 1%, w/w) positively correlated to microbial community metabolic function in planting stage whereas resulted in decreasing of Sb (III/V) concentrations in alfalfa, although 0.01% MWCNT led to increase of Sb (V) and decrease of Sb (V) by 50.97% and 32.68% respectively. This study provided information for investigating combined ecological impacts of heavy metal and engineering materials under different water managing sediments.

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 most large field of view (FOV) observations, the distortion problem is inevitably and significantly more serious than in small FOV ones. In the circumstances, many traditional star ident...