Showing papers by "National University of Defense Technology published in 2012"
TL;DR: This work provides an updated assembly version of the 2008 Asian genome using SOAPdenovo2, a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome.
Abstract: There is a rapidly increasing amount of de novo genome assembly using next-generation sequencing (NGS) short reads; however, several big challenges remain to be overcome in order for this to be efficient and accurate. SOAPdenovo has been successfully applied to assemble many published genomes, but it still needs improvement in continuity, accuracy and coverage, especially in repeat regions. To overcome these challenges, we have developed its successor, SOAPdenovo2, which has the advantage of a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome. Benchmark using the Assemblathon1 and GAGE datasets showed that SOAPdenovo2 greatly surpasses its predecessor SOAPdenovo and is competitive to other assemblers on both assembly length and accuracy. We also provide an updated assembly version of the 2008 Asian (YH) genome using SOAPdenovo2. Here, the contig and scaffold N50 of the YH genome were ~20.9 kbp and ~22 Mbp, respectively, which is 3-fold and 50-fold longer than the first published version. The genome coverage increased from 81.16% to 93.91%, and memory consumption was ~2/3 lower during the point of largest memory consumption.
4,284 citations
TL;DR: It is shown that MIR168a could bind to the human/mouse low-density lipoprotein receptor adapter protein 1 (LDLRAP1) mRNA, inhibit LDLRAP1 expression in liver, and consequently decrease LDL removal from mouse plasma, demonstrating that exogenous plant miRNAs in food can regulate the expression of target genes in mammals.
Abstract: Our previous studies have demonstrated that stable microRNAs (miRNAs) in mammalian serum and plasma are actively secreted from tissues and cells and can serve as a novel class of biomarkers for diseases, and act as signaling molecules in intercellular communication. Here, we report the surprising finding that exogenous plant miRNAs are present in the sera and tissues of various animals and that these exogenous plant miRNAs are primarily acquired orally, through food intake. MIR168a is abundant in rice and is one of the most highly enriched exogenous plant miRNAs in the sera of Chinese subjects. Functional studies in vitro and in vivo demonstrated that MIR168a could bind to the human/mouse low-density lipoprotein receptor adapter protein 1 (LDLRAP1) mRNA, inhibit LDLRAP1 expression in liver, and consequently decrease LDL removal from mouse plasma. These findings demonstrate that exogenous plant miRNAs in food can regulate the expression of target genes in mammals.
910 citations
TL;DR: The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression.
Abstract: Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% ( P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.
659 citations
TL;DR: This article investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents and found that hypermutability is a property of ASD genes and may also include nucleotide substitution hot spots.
505 citations
TL;DR: A new efficient NeNMF solver is presented that applies Nesterov's optimal gradient method to alternatively optimize one factor with another fixed and can be used to solve -norm, -norm and manifold regularized NMF with the optimal convergence rate.
Abstract: Nonnegative matrix factorization (NMF) is a powerful matrix decomposition technique that approximates a nonnegative matrix by the product of two low-rank nonnegative matrix factors. It has been widely applied to signal processing, computer vision, and data mining. Traditional NMF solvers include the multiplicative update rule (MUR), the projected gradient method (PG), the projected nonnegative least squares (PNLS), and the active set method (AS). However, they suffer from one or some of the following three problems: slow convergence rate, numerical instability and nonconvergence. In this paper, we present a new efficient NeNMF solver to simultaneously overcome the aforementioned problems. It applies Nesterov's optimal gradient method to alternatively optimize one factor with another fixed. In particular, at each iteration round, the matrix factor is updated by using the PG method performed on a smartly chosen search point, where the step size is determined by the Lipschitz constant. Since NeNMF does not use the time consuming line search and converges optimally at rate in optimizing each matrix factor, it is superior to MUR and PG in terms of efficiency as well as approximation accuracy. Compared to PNLS and AS that suffer from numerical instability problem in the worst case, NeNMF overcomes this deficiency. In addition, NeNMF can be used to solve -norm, -norm and manifold regularized NMF with the optimal convergence rate. Numerical experiments on both synthetic and real-world datasets show the efficiency of NeNMF for NMF and its variants comparing to representative NMF solvers. Extensive experiments on document clustering suggest the effectiveness of NeNMF.
465 citations
TL;DR: A superparamagnetic graphene oxide-Fe3O4 hybrid composite was proposed in this paper to remove organic dyes from polluted water. But the performance of the composite was limited.
Abstract: A superparamagnetic graphene oxide–Fe3O4 hybrid composite (GO–Fe3O4) was prepared via a simple and effective chemical method. Amino-functionalized Fe3O4 (NH2-Fe3O4) particles are firmly deposited on the graphene oxide sheets. The graphene oxide sheets could prevent NH2-Fe3O4 particles from agglomeration and enable a good dispersion of these oxide particles. The as-prepared GO–Fe3O4 hybrid composite had a much higher thermal stability than graphene oxide. The amount of NH2-Fe3O4 loaded on GO was estimated to be 23.6 wt% by atomic absorption spectrometry. The specific saturation magnetization (Ms) of the GO–Fe3O4 hybrid composite is 15 emu g−1. The magnetic GO–Fe3O4 composite has been employed as adsorbent for the magnetic separation of dye contaminants from water. The adsorption test of dyes (Methylene Blue (MB) and Neutral Red (NR)) demonstrates that it only takes 30 min for MB and 90 min for NR to attain equilibrium. The adsorption capacities for MB and NR in the concentration range studied are 167.2 and 171.3 mg g−1, respectively. The GO–Fe3O4 hybrid composite can be easily manipulated in magnetic field for desired separation, leading to the removal of dyes from polluted water. These GO–Fe3O4 hybrid composites have great potential applications in removing organic dyes from polluted water.
341 citations
TL;DR: In this paper, the authors study the propagation of flexural waves in a locally resonant (LR) thin plate made of a two-dimensional periodic array of spring-mass resonators attached on a thin homogeneous plate.
Abstract: The authors study the propagation of flexural waves in a locally resonant (LR) thin plate made of a two-dimensional periodic array of spring–mass resonators attached on a thin homogeneous plate. The well-known plane wave expansion method is extended to deal with such a plate system with a periodic array of lumped resonant elements. Explicit matrix formulations are developed for the calculation of complex band structures, in which the imaginary parts of Bloch wave vectors are displayed to quantify the wave attenuation performance of band gaps. It is found that resonance-type and Bragg-type band gaps coexist in the LR plate, and the bandwidth of these gaps can be dramatically affected by the resonant frequency of local resonators. In particular, a super-wide pseudo-directional gap can be formed by a combination of the resonance gap and Bragg gap; inside such a pseudo-gap, only a very narrow pass band exists. An explicit formula is further developed to facilitate the design of such a pseudo-gap. Finally, vibration transmission in finite LR plates is calculated using the finite element method. Vibration transmission gaps are observed, and the results are in good agreement with the band gap properties predicted by the complex band structures.
320 citations
TL;DR: In this article, the effect of the turbulence model and slot width on the transverse slot injection flow field has been investigated numerically based on the grid independency analysis, and the predicted results have been compared with the experimental data available in the open literature.
318 citations
TL;DR: A novel approach for texture classification, generalizing the well-known local binary pattern (LBP) approach, which produces the best classification results on KTHTIPS2b, and results comparable to the state of the art on CUReT.
311 citations
TL;DR: The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, the approach outperforms traditional feature extraction methods which involve careful design and complex steps and leads to significant improvements in classification accuracy and reductions in feature dimensionality.
Abstract: Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for texture classification based on random projection, suitable for large texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag--of-words model to perform texture classification; thus, learning and classification are carried out in a compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We have conducted extensive experiments on each of the CUReT, the Brodatz, and the MSRC databases, comparing the proposed approach to four state-of-the-art texture classification methods: Patch, Patch-MRF, MR8, and LBP. We show that our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality.
310 citations
TL;DR: In this article, the elastic constants and electronic structures of two-dimensional monolayer MoS 2 under elastic strain using the first-principles calculations were analyzed and the in-plane stiffness and Poisson ratio calculated in the harmonic elastic strain range were found to be 123 N/m and 0.25, indicating that monollayer MoS2 is much softer than graphene.
TL;DR: In this article, the properties and the deposition process of surface film on lithium metal with LiNO3 as lithium salt in electrolyte solution are investigated using X-ray photoelectron spectroscopy (XPS), scanning probe microscopy (SPM), scanning electron microscope (SEM) and electrochemical impedance spectroscope (EIS).
TL;DR: An efficient online RSA-NMF algorithm that learns NMF in an incremental fashion and outperforms the existing online NMF (ONMF) algorithms in terms of efficiency and proves that OR- NMF almost surely converges to a local optimal solution by using the quasi-martingale.
Abstract: Nonnegative matrix factorization (NMF) has become a popular dimension-reduction method and has been widely applied to image processing and pattern recognition problems. However, conventional NMF learning methods require the entire dataset to reside in the memory and thus cannot be applied to large-scale or streaming datasets. In this paper, we propose an efficient online RSA-NMF algorithm (OR-NMF) that learns NMF in an incremental fashion and thus solves this problem. In particular, OR-NMF receives one sample or a chunk of samples per step and updates the bases via robust stochastic approximation. Benefitting from the smartly chosen learning rate and averaging technique, OR-NMF converges at the rate of in each update of the bases. Furthermore, we prove that OR-NMF almost surely converges to a local optimal solution by using the quasi-martingale. By using a buffering strategy, we keep both the time and space complexities of one step of the OR-NMF constant and make OR-NMF suitable for large-scale or streaming datasets. Preliminary experimental results on real-world datasets show that OR-NMF outperforms the existing online NMF (ONMF) algorithms in terms of efficiency. Experimental results of face recognition and image annotation on public datasets confirm the effectiveness of OR-NMF compared with the existing ONMF algorithms.
18 Sep 2012
TL;DR: This paper provides a control-theoretic solution to the dynamic capacity provisioning problem that minimizes the total energy cost while meeting the performance objective in terms of task scheduling delay, and uses Model Predictive Control (MPC) to find the optimal control policy.
Abstract: Data centers have recently gained significant popularity as a cost-effective platform for hosting large-scale service applications. While large data centers enjoy economies of scale by amortizing initial capital investment over large number of machines, they also incur tremendous energy cost in terms of power distribution and cooling. An effective approach for saving energy in data centers is to adjust dynamically the data center capacity by turning off unused machines. However, this dynamic capacity provisioning problem is known to be challenging as it requires a careful understanding of the resource demand characteristics as well as considerations to various cost factors, including task scheduling delay, machine reconfiguration cost and electricity price fluctuation.In this paper, we provide a control-theoretic solution to the dynamic capacity provisioning problem that minimizes the total energy cost while meeting the performance objective in terms of task scheduling delay. Specifically, we model this problem as a constrained discrete-time optimal control problem, and use Model Predictive Control (MPC) to find the optimal control policy. Through extensive analysis and simulation using real workload traces from Google's compute clusters, we show that our proposed framework can achieve significant reduction in energy cost, while maintaining an acceptable average scheduling delay for individual tasks.
TL;DR: In this paper, the solid components deposited in sulfur cathode during cycling for Li-S battery are studied by Fourier transform infrared (FTIR), Raman spectra and X-ray photoelectron spectroscopy (XPS).
Abstract: The solid components deposited in sulfur cathode during cycling for Li-S battery is studied in this work. ROLi, HCO2Li, LixSOy and Li2S (or Li2S2) are proved to be the main components by the methods of Fourier transform infrared (FTIR), Raman spectra and X-ray photoelectron spectroscopy (XPS). ROLi and HCO2Li are solvent degradation products existed in electrolyte. The reversibility of Li2S and Li2S2 are not serious as in previous reports. ROLi, HCO2Li and LixSOy co-deposited with Li2S or Li2S2 in discharge process lead to the cathodes performance deterioration. Lithium salts such as LiNO3 and LiTFSI can oxidize sulfur compounds to higher oxidation states, and LixSOy species increased with cycling indicates the active mass irreversible oxidation that may be another important reason for the capacity fading of Li-S battery.
TL;DR: In this paper, sound transmission loss of metamaterial-based thin plates consisting of multiple subwavelength arrays of spring-mass resonators attached to an unbounded homogenous thin plate was investigated.
TL;DR: These monodisperse palladium (Pd) nanoparticles on reduced graphene oxide (RGO) surfaces were successfully prepared by a "wet" and "clean" method in aqueous solution and exhibited catalytic activity in hydrogen generation from the hydrolysis of ammonia borane.
Abstract: In this study, monodisperse palladium (Pd) nanoparticles on reduced graphene oxide (RGO) surfaces were successfully prepared by a “wet” and “clean” method in aqueous solution. Without any surface treatment, Pd nanoparticles are firmly attached to the RGO sheets. These RGO/Pd nanocomposites exhibited catalytic activity in hydrogen generation from the hydrolysis of ammonia borane (AB). Their hydrolysis completion time and activation energy were 12.5 min and 51 ± 1 kJ mol−1, respectively, which were comparable to the best Pd-based catalyst reported. The TOF values (mol of H2 × (mol of catalyst × min)−1) of RGO/Pd is 6.25, which appears to be one of the best catalysts reported so far. We also obtained a 11B NMR spectrum to investigate the mechanism of this catalytic hydrolysis process. This simple and straightforward method is of significance for the facile preparation of metal nanocatalysts with high catalytic activity on proper supporting materials.
TL;DR: In this article, a reduced graphene oxide/hydroxyapatite (RGO/HA) hybrid material was synthesized by an environmental-friendly route, where Graphene oxide (GO) was first simultaneously reduced and surface functionalized by one-step oxidative polymerization of dopamine (PDA), which enabled efficient interaction between the RGO surface and the mineral ions to improve the bioactivity, promoted the formation of the HA nanoparticles.
Abstract: In this study, we present the synthesis of reduced graphene oxide/hydroxyapatite (RGO/HA) hybrid materials by an environmental-friendly route. Graphene oxide (GO) was first simultaneously reduced and surface functionalized by one-step oxidative polymerization of dopamine (PDA). The bioinspired surface was further used for biomimetic mineralization of hydroxyapatite. When incubated in a simulated body fluid (SBF), the PDA layer enabled efficient interaction between the RGO surface and the mineral ions to improve the bioactivity, promoted the formation of the HA nanoparticles. A detailed structural and morphological characterization of the mineralized composite was performed. The HA-based hybrid materials exhibited no cytotoxic effect on L929 fibroblast cells, showing potential capacity of being a scaffold material for bone tissue regeneration and implantation. This facile strategy also can be a useful platform for other RGO-based nanocomposites.
TL;DR: An efficient ML DOA estimator based on a spatially overcomplete array output formulation that surpasses state-of-the-art methods largely in performance, especially in demanding scenarios such as low signal-to-noise ratio (SNR), limited snapshots and spatially adjacent signals.
Abstract: The computationally prohibitive multi-dimensional searching procedure greatly restricts the application of the maximum likelihood (ML) direction-of-arrival (DOA) estimation method in practical systems. In this paper, we propose an efficient ML DOA estimator based on a spatially overcomplete array output formulation. The new method first reconstructs the array output on a predefined spatial discrete grid under the sparsity constraint via sparse Bayesian learning (SBL), thus obtaining a spatial power spectrum estimate that also indicates the coarse locations of the sources. Then a refined 1-D searching procedure is introduced to estimate the signal directions one by one based on the reconstruction result. The new method is able to estimate the incident signal number simultaneously. Numerical results show that the proposed method surpasses state-of-the-art methods largely in performance, especially in demanding scenarios such as low signal-to-noise ratio (SNR), limited snapshots and spatially adjacent signals.
TL;DR: This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving.
Abstract: This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control. The main objective here is to clarify the goals and guidelines for future development in the area of advanced driver-assistance systems (ADASs). Two major research directions are investigated and discussed in detail: (1) stimuli-decisions-actions, which focuses on the driver side, and (2) perception enhancement-action-suggestion-function-delegation, which emphasizes the ADAS side. This paper addresses the important achievements and major difficulties of each direction and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving. Other related topics, including driver training and infrastructure design, are also studied.
TL;DR: In this article, the spectral element (SE) method and the Bloch theorem were combined with the spectral equation for complex band structure calculation in metamaterial-based elastic rods with periodically attached multi-degree-of-freedom spring mass resonators.
Abstract: Wave propagation and vibration transmission in metamaterial-based elastic rods containing periodically attached multi-degree-of-freedom spring–mass resonators are investigated. A methodology based on a combination of the spectral element (SE) method and the Bloch theorem is developed, yielding an explicit formulation for the complex band structure calculation. The effects of resonator parameters on the band gap behavior are investigated by employing the attenuation constant surface plots, which display information on the location, the width and the attenuation performance of all band gaps. It is found that Bragg-type and resonance-type gaps co-exist in these systems. In some special situations, exact coupling between Bragg and resonance gaps occurs, giving rise to super-wide coupled gaps. The advantage of multi-degree-of-freedom resonators in achieving multiband and/or broadband gaps in metamaterial-based rods is demonstrated. Band gap formation mechanisms are further examined by analytical and physical models, providing explicit formulae to locate the band edge frequencies of all the band gaps.
01 Jul 2012
TL;DR: This work introduces a novel part crossover operator which works at the finer-level part structures of the shapes, leading to significant variations and thus increased diversity in the evolved shape structures, and demonstrates the effectiveness of set evolution on man-made shapes.
Abstract: We introduce set evolution as a means for creative 3D shape modeling, where an initial population of 3D models is evolved to produce generations of novel shapes. Part of the evolving set is presented to a user as a shape gallery to offer modeling suggestions. User preferences define the fitness for the evolution so that over time, the shape population will mainly consist of individuals with good fitness. However, to inspire the user's creativity, we must also keep the evolving set diverse. Hence the evolution is "fit and diverse", drawing motivation from evolution theory. We introduce a novel part crossover operator which works at the finer-level part structures of the shapes, leading to significant variations and thus increased diversity in the evolved shape structures. Diversity is also achieved by explicitly compromising the fitness scores on a portion of the evolving population. We demonstrate the effectiveness of set evolution on man-made shapes. We show that selecting only models with high fitness leads to an elite population with low diversity. By keeping the population fit and diverse, the evolution can generate inspiring, and sometimes unexpected, shapes.
TL;DR: In this article, the authors extended the plane wave expansion method to study the flexural wave propagation in locally resonant beams with multiple periodic arrays of attached spring-mass resonators.
TL;DR: In this article, the potential of single-layered graphene sheet (SLGS) as a nanomechanical sensor is explored based on the nonlocal Kirchhoff theory of plates which incorporates size effects into the classical theory.
TL;DR: In this paper, the qualitative and semi-quantitative analysis of polysulfide species dissolved in electrolyte during dischargecharge process and cycling of Li-S batteries was reported.
Abstract: In this paper, the qualitative and semi-quantitative analysis of polysulfide species dissolved in electrolyte during the dischargecharge process and cycling of Li-S batteries was reported. ICP-OES (Inductively coupled plasmaOptical emission spectrometer) measurement was used to estimate the total sulfur content dissolved in electrolyte. Lithium polysulfide with different order were separated and confirmed by LC-MS (Liquid chromatography coupled with mass spectrometry). Li2S4 and Li2S6 were proved to be the most stable form of lithium polysulfide species. At the end of discharge process, total sulfur content in the form of Li2S4 and Li2S6 remained in electrolyte was about 20% of the active material in initial cathode. At the end of charge process, 45% total sulfur content was preserved in electrolyte mainly in the form of Li2S6. The partial transformation of active material from liquid phase to solid phase resulted in relatively low practical specific capacity than the theoretical. In cycles, active material transferred between liquid and solid phase kept a balance, and the content of total sulfur, Li2S4 and Li2S6 were changed slightly at the end. Consequently, polysulfide dissolved in electrolyte just took limited responsibility for the capacity fading with cycles of Li-S batteries. © 2012 The Electrochemical Society. [DOI: 10.1149/2.060204jes] All rights reserved.
TL;DR: In this article, the authors used high speed photography and Schlieren system to capture the spark ignition process in the scramjet combustor fueled by hydrogen equipped with multi-cavities at Mach 4 flight condition.
TL;DR: A novel approach for ensuring confidential wireless communication is proposed and analyzed from an information-theoretic standpoint and a new concept of outage secrecy region is proposed to evaluate the secrecy performance from a geometrical perspective.
Abstract: A novel approach for ensuring confidential wireless communication is proposed and analyzed from an information-theoretic standpoint. In this approach, the legitimate receiver generates artificial noise (AN) to impair the intruder's channel. This method is robust because it does not need the feedback of channel state information (CSI) to the transmitter and does not assume that the number of Eve's antennas should be smaller than that of Bob. Furthermore, we propose a new concept of outage secrecy region to evaluate the secrecy performance from a geometrical perspective. This should be useful if we need to know what zone should be protected (or militarized). Analysis and simulation results in practical environments show that the proposed method has a good performance.
TL;DR: A Radiating Gradient Vector Flow (RGVF) aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed, and is thus robust to contaminations and can effectively locate the obscure boundaries.
TL;DR: The overall focusing procedure of the ENLCS algorithm only involves fast Fourier transform and complex multiplication, which means easier implementation and higher efficiency, and the experimental results with simulated data prove the effectiveness of the proposed algorithm.
Abstract: In this paper, an extended nonlinear chirp scaling (ENLCS) algorithm for focusing synthetic aperture radar data acquired at high resolution and highly squint angle is proposed. The whole processing of the ENLCS consists of the following three steps. First, a linear range walk correction is used to remove the linear component of target range cell migration (RCM) and to mitigate the range-azimuth coupling of the 2-D spectrum. Second, a bulk second range compression (SRC) is performed in the 2-D frequency domain for compensating the residual RCM, SRC term, and higher order range-azimuth coupling terms. Third, a modified azimuth NLCS (ANLCS) operation is applied to equalize the azimuth frequency modulation rate for azimuth compression. By adopting higher order approximation processing and by properly selecting the scaling coefficients, the proposed modified ANLCS operation has better accuracy and little image misregistration. The overall focusing procedure of the ENLCS algorithm only involves fast Fourier transform and complex multiplication, which means easier implementation and higher efficiency. The experimental results with simulated data prove the effectiveness of the proposed algorithm.
TL;DR: Novel l1-regularized space-time adaptive processing algorithms with a generalized sidelobe canceler architecture for airborne radar applications with a sparse regularization to the minimum variance criterion are proposed.
Abstract: In this paper, we propose novel l1-regularized space-time adaptive processing (STAP) algorithms with a generalized sidelobe canceler architecture for airborne radar applications. The proposed methods suppose that a number of samples at the output of the blocking process are not needed for sidelobe canceling, which leads to the sparsity of the STAP filter weight vector. The core idea is to impose a sparse regularization (l1-norm type) to the minimum variance criterion. By solving this optimization problem, an l1-regularized recursive least squares (l1-based RLS) adaptive algorithm is developed. We also discuss the SINR steady-state performance and the penalty parameter setting of the proposed algorithm. To adaptively set the penalty parameter, two switched schemes are proposed for l1-based RLS algorithms. The computational complexity analysis shows that the proposed algorithms have the same complexity level as the conventional RLS algorithm (O((NM)2)), where NM is the filter weight vector length), but a significantly lower complexity level than the loaded sample covariance matrix inversion algorithm (O((NM)3)) and the compressive sensing STAP algorithm (O((NsNd)3), where N8Nd >; NM is the angle-Doppler plane size). The simulation results show that the proposed STAP algorithms converge rapidly and provide a SINR improvement using a small number of snapshots.