查看更多>>摘要：Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning tech-niques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of net-work parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient train-ing for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quan-tization,and entropy coding,we can ensemble them in an inte-gration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the norma-lized singular values,outperforms other conventional sparse measures such as the ℓ1 norm for network compression.The other is a spatial and temporal balance for tensorized neural net-works.For accelerating the training of tensorized neural net-works,it is crucial to leverage redundancy for both model com-pression and subspace training.
查看更多>>摘要：With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)esti-mation due to the computational complexity of algorithms.Tradi-tional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their esti-mation accuracy under large array elements,this paper pro-poses a DOA estimation method based on Krylov subspace and weighted l1-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization func-tion based on the residual sum of squares and weighted l1-norm to solve the target DOA.Simulation results show that the pro-posed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.
查看更多>>摘要：The warhead of a ballistic missile may precess due to lateral moments during release.The resulting micro-Doppler effect is determined by parameters such as the target's motion state and size.A three-dimensional reconstruction method for the precession warhead via the micro-Doppler analysis and inverse Radon transform(IRT)is proposed in this paper.The pre-cession parameters are extracted by the micro-Doppler analysis from three radars,and the IRT is used to estimate the size of targe.The scatterers of the target can be reconstructed based on the above parameters.Simulation experimental results illus-trate the effectiveness of the proposed method in this paper.
查看更多>>摘要：Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for mas-sive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a consider-able number of implicit and explicit approximate matrix inver-sion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detec-tion for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conven-tional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has signifi-cantly lower complexity than higher Newton iterations.Conver-gence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
查看更多>>摘要：Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing fea-ture information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural net-work,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies indivi-dual of emitters from received raw data in end-to-end,it accele-rates SEI implementation and simplifies procedures of identifica-tion.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identifi-cation accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,compu-tational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counter-parts on the basis of simulation dataset and real dataset col-lected in the anechoic chamber.
查看更多>>摘要：Classical localization methods use Cartesian or Polar coordinates,which require a priori range information to deter-mine whether to estimate position or to only find bearings.The modified polar representation(MPR)unifies near-field and far-field models,alleviating the thresholding effect.Current localiza-tion methods in MPR based on the angle of arrival(AOA)and time difference of arrival(TDOA)measurements resort to semidefinite relaxation(SDR)and Gauss-Newton iteration,which are computationally complex and face the possible diverge problem.This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem,namely hybrid constrained optimization(HCO).HCO attains Cramér-Rao bound(CRB)-level accuracy for mild Gaussian noise.Compared with the existing closed-form solutions for the hybrid TDOA-AOA case,HCO provides compa-rable performance to the hybrid generalized trust region sub-problem(HGTRS)solution and is better than the hybrid succes-sive unconstrained minimization(HSUM)solution in large noise region.Its computational complexity is lower than that of HGTRS.Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator(MLE)attains if the noise is small,but the MLE deviates from CRB earlier.
查看更多>>摘要：Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention,especially the beamspace maximum likelihood(BML)algorithm.However,the difference beam is rarely used in super-resolution methods,especially in low elevation estimation.The target airspace information in the difference beam is different from the target airspace information in the sum beam.And the use of difference beams does not significantly increase the com-plexity of the system and algorithms.Thus,this paper applies the difference beam to the beamformer to improve the elevation estimation performance of BML algorithm.And the direction and number of beams can be adjusted according to the actual needs.The theoretical target elevation angle root means square error(RMSE)and the computational complexity of the proposed algorithms are analyzed.Finally,computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.
查看更多>>摘要：In order to extract the richer feature information of ship targets from sea clutter,and address the high dimensional data problem,a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP)based on the maxi-mum margin criterion(MMC)is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP).Multi-scale fusion is introduced to capture the local and detailed information in small-scale features,and the global and contour information in large-scale features,offering help to extract the edge information from sea clutter and further improv-ing the target recognition accuracy.The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space.Experimental results on the measured radar data show that the proposed method can effec-tively extract the features of ship target from sea clutter,further reduce the feature dimensionality,and improve target recogni-tion performance.
查看更多>>摘要：Three-dimensional(3D)synthetic aperture radar(SAR)extends the conventional 2D images into 3D features by several acquisitions in different aspects.Compared with 3D techniques via multiple observations in elevation,e.g.SAR interferometry(InSAR)and SAR tomography(TomoSAR),holographic SAR can retrieve 3D structure by observations in azimuth.This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation(AAO)for embedded targets detection and holographic 3D reconstruction.The ground tracks of the AAO orbit separate the earth surface into grids.Target in these grids can be accessed with an azimuth angle span of 360°,which is similar to the flight path of airborne circular SAR(CSAR).Inspired from the successive coverage orbits of optical sensors,several optimizations are made in the proposed method to ensure favorable grazing angles,the performance of 3D reconstruction,and long-term supervision for SAR sensors.Sim-ulation experiments show the regional AAO can be completed within five hours.In addition,a second AAO of the same area can be duplicated in two days.Finally,an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.
查看更多>>摘要：This paper systematically introduces and reviews a scientific exploration of reliability called the belief reliability.Beginning with the origin of reliability engineering,the problems of present theories for reliability engineering are summarized as a query,a dilemma,and a puzzle.Then,through philosophical reflection,we introduce the theoretical solutions given by belief reliability theory,including scientific principles,basic equations,reliability science experiments,and mathematical measures.The basic methods and technologies of belief reliability,namely,belief reliability analysis,function-oriented belief reliability design,belief reliability evaluation,and several newly developed methods and technologies are sequentially elaborated and overviewed.Based on the above investigations,we summarize the significance of belief reliability theory and make some prospects about future research,aiming to promote the develop-ment of reliability science and engineering.
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