Search

Mmillimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) system usually utilizes hybrid precoding to reduce complex hardware and high energy consumption. However, most hybrid precoding scheme consider the fully connected architecture, which may result in a significant loss of energy efficiency. In this paper, we propose a hybrid precoding scheme based on the overlapped subarray architecture (OSA), where the analog part of the MU-MIMO system is realized by a OSA network. Based on the OSA architecture, a modified generalized low rank approximation of matrices (GLRAM) based hybrid precoding scheme is designed to maximize the achievable sum-rate, where the analog precoding algorithm apply the phase alignment technology and GLRAM approach to obtain RF precoder/combiner with high array gain provided by massive MIMO system. Then, the block diagonalization (BD) technology is performed to obtain the digital precoding matrix for the multiplexing gain. Simulation results show that the modified GLRAM based hybrid precoding algorithm for OSA architecture can reduce the number of iterations. Compared with the existing hybrid precoding algorithm, it achieves higher spectral efficiency and lower complexity.

This letter proposes the use of neural networks to realize the passive localization by signal time difference of arrival (TDOA). In the face of multiple complex targets with radiation sources in a specific area, real-time localization is an urgent problem. In this letter, positions of the known targets from the prior data are obtained and their time difference is calculated, which will be connected as data pairs and input to the neural network trained to obtain a corresponding model. Subsequently, unknown targets can be localized by this network. It is verified that the localization accuracy of the algorithm is reliable and its robustness is higher than that of traditional algorithms. The proposed method also shows that great reduction of operation time depending on the previous network training can complete real-time goals.

A long-time coherent integration could effectively improve the detection ability of radar for maneuvering targets. Nevertheless, the Doppler ambiguity and frequency migration caused by the high speed and acceleration severely degrade the detection performance. In this regard, a novel coherent integration algorithm is proposed, particularly for maneuvering targets with Doppler ambiguity. Specifically, the acceleration is firstly estimated by scaled non-uniform fast Fourier transform (SNuFFT). Then, the scaled periodic discrete Fourier transform (SPDFT), which periodically extends the observable Doppler scope of discrete Fourier transform (DFT), is proposed to estimate the unambiguous Doppler frequency. Finally, the grating lobes are significantly suppressed via product operation, and coherent integration is achieved after phase compensation. To alleviate the computational burden and eliminate the brute force searching procedure, an efficient implementation based on chirp-z transform (CZT) is also derived. Analysis shows that the proposed algorithm achieves a good balance between computational complexity and anti-noise performance. Extensive simulations and real measured radar data are conducted to verify the proposed algorithm.

Long-term coherent integration can remarkably improve the ability of detection and motion parameter estimation of radar for maneuvering targets. However, the linear range migration, quadratic range migration (QRM), and Doppler frequency migration within the coherent processing interval seriously degrade the detection and estimation performance. Therefore, an efficient and noise-resistant coherent integration method based on location rotation transform (LRT) and non-uniform fast Fourier transform (NuFFT) is proposed. QRM is corrected by the second-order keystone transform. Using the relationship between the rotation angle and Doppler frequency, a novel phase compensation function is constructed. Motion parameters can be rapidly estimated by LRT and NuFFT. Compared with several representative algorithms, the proposed method achieves a nearly ideal detection performance with low computational cost. Finally, experiments based on measured radar data are conducted to verify the proposed algorithm.

This paper addresses the joint time difference of arrival (TDOA), frequency difference of arrival (FDOA) and differential Doppler rate estimation problem for high-speed maneuvering targets in passive location systems, involving linear range migration (LRM), quadratic range migration (QRM) and linear Doppler frequency migration (LDFM) within observation time. A noise-resistant estimation algorithm based on second-order keystone transform (SKT) and non-uniform fast Fourier transform (NUFFT) is proposed. After QRM correction via SKT, a phase compensation function is constructed to eliminate LRM and estimate the FDOA. Then, NUFFT is used to remove LDFM and realize the joint estimation of TDOA and differential Doppler rate. Comparisons with several relatively new algorithms indicate that the proposed algorithm can obtain a good trade-off between computational cost and estimation performance. Extensive numerical examples, analysis of computational complexity and estimation performance can validate the effectiveness of the proposed method.

Due to the superior anti-interception performance and inherent security features, the wide applications of frequency-hopping (FH) signals bring a great challenge to the reconnaissance and monitoring of FH emitters. This paper addresses the problem of positioning measurement estimation for unknown FH signals in passive localization, considering the range migration (RM) and Doppler frequency migration (DFM) of the maneuvering target within the observation time. A coherent range difference (RD), range rate difference (RRD) and acceleration difference (AD) estimation algorithm based on scaled Fourier transform and scaled non-uniform fast Fourier transform is proposed. This method can effectively remove RM and random DFM effects regardless of varied carrier frequency and achieve the coherent estimation of RD, RRD and AD. The whole estimation process can be easily implemented by complex multiplications combined with fast Fourier transform (FFT) and inverse FFT operations without any brute-force searching procedure. Numerical experiments demonstrate that the anti-noise performance of the proposed method is superior to several representative methods and comparable to the optimal maximum likelihood estimator with a much lower computational cost.

Long-time coherent integration can remarkably improve the detection and motion parameter estimation ability of radar for maneuvering targets. Unfortunately, the linear range migration, quadratic range migration (QRM), and Doppler frequency migration (DFM) within the coherent processing interval seriously degrade the target detection and parameter estimation performance. In this regard, a novel coherent integration approach based on frequency autocorrelation function (FAF) and Lv’s distribution (LVD), i.e., FAF–LVD, is proposed in this paper. The QRM is firstly removed by the second-order keystone transform. Then, the FAF with a variable delay is introduced to remove the DFM, followed by the scaled Fourier transform to achieve coherent integration. Finally, the acceleration and velocity are estimated simultaneously by LVD. Analysis demonstrates that FAF–LVD could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT) and inverse FFT. Detailed comparisons with several representative methods indicate that FAF–LVD achieves a better balance among computational complexity, detection ability, and parameter estimation performance. Extensive simulations are presented to validate the effectiveness of FAF–LVD method.

This study addresses the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation problem in passive emitter localisation, involving range migration during the observation time. A computational efficient estimation algorithm based on scaled Fourier transformation is proposed. This method can effectively remove the RM and accomplish the parameter estimation. Cross-terms suppression ability of the proposed method is also analysed and its characteristic indicates the applicability in the scenario of multi-targets. The whole estimation step can be fast implemented by complex multiplications, fast Fourier transformation (FFT) and inverse FFT without any searching process. Compared with the ideal maximum likelihood estimator via extensive numerical experiments, the proposed method can achieve comparable estimation performance and have gained more than ten thousand times reduction in the computational cost, which helps practical application.

In this study, a novel algorithm based on three-dimensional scaled Fourier transform (TDSFT) is proposed for parameter estimation of a quadratic frequency modulated (QFM) signal. To solve the problem of signal-to-noise ratio loss caused by constant delay, a variable-delay instantaneous autocorrelation function is first proposed. After that, two steps TDSFT and addition operation are performed to achieve three-dimensional decoupling and coherent integration of signal energy. The analysis shows that the proposed algorithm can be efficiently implemented by a fast Fourier transform without any searching procedure. Moreover, it can deal with multi-component QFM signals with satisfactory suppression of cross terms. Comparisons with three other popular methods, chirp rate-quadratic chirp rate distribution, generalized scaled Fourier transform and generalized decoupling technique demonstrate that the proposed algorithm has superior anti-noise performance due to the coherent integration of all signal energies. Extensive numerical simulations validate the effectiveness of the TDSFT-based algorithm.

Many robust adaptive wideband beamforming algorithms have been carried out to preserve the signal of interest, but their performance can be still degraded by the mismatches of steering vector and covariance matrix. Therefore, in this paper, a novel robust adaptive wideband beamformer based on the time-frequency distribution is proposed, which can achieve high output signal-to-interference-plus-noise ratio close to the optimal one even with perturbed array manifold and limited snapshots. It transforms the received signal into the time-frequency domain, and gives a method to select the single-source points of wideband signals. With the single-source points, it reconstructs the interference-plus-noise covariance matrix and estimates the steering vector without using the imprecise prior information about the array manifold, guaranteeing high estimation accuracy. Simulation results demonstrate that the proposed method outperforms other adaptive wideband beamformers, and achieves excellent output performance over a broad range of direction and sensor position errors even with small number of snapshots.

In this paper, the problem of moving target localization from Bistatic Range (BR) and Bistatic Range Rate (BRR) measurements in a Multiple-Input Multiple-Output (MIMO) radar system having widely separated antennas is investigated. We consider a practically motivated scenario, where the accurate knowledge of transmitter and receiver locations is not known and only the nominal values are available for processing. With the transmitter and receiver location uncertainties, which are usually neglected in MIMO radar systems by prior studies, taken into account in the measurement model, we develop a novel algebraic solution to reduce the estimation error for moving target localization. The proposed algorithm is based on the pseudolinear set of equations and two-step weighted least squares estimation. The Cramer-Rao Lower Bound (CRLB) is derived in the presence of transmitter and receiver location uncertainties. Theoretical accuracy analysis demonstrates that the proposed solution attains the CRLB, and numerical examples show that the proposed solution achieves significant performance improvement over the existing algorithms. Keywords: Bistatic range, Bistatic range rate, Location uncertainty, MIMO radar, Target localization, Two-step weighted least squares

Adaptive beamformers based on interference-plus-noise covariance matrix reconstruction can extract the signal of interest (SOI) effectively in the condition of precise array manifold, but suffer performance degradation in the presence of array calibration error. In this paper, a novel interference-plus-noise covariance matrix reconstruction and steering vector estimation method is proposed, which is robust against random mismatches. By transforming the received signal into the time-frequency (TF) domain, the proposed method reconstructs the interference-plus-noise covariance matrix and estimates the steering vector of the SOI using the spatial time-frequency distribution (STFD) matrices. Neither the imprecise prior information about the array manifold nor the sample covariance matrix is utilized in the whole procedure, guaranteeing high estimation accuracy and excellent output performance. Simulation results demonstrate that the proposed method outperforms existing adaptive beamformers, and achieves high output signal-to-interference-plus-noise ratio (SINR) close to the optimal one even in the case of large calibration error.

The presence of transmitter and receiver location uncertainties has been known to remarkably deteriorate the target localization accuracy in multi-static passive radar (MPR) system. This paper explores the use of calibration targets, the positions and velocities of which are known to the MPR system, to counter the loss in target localization accuracy arising from the transmitter/receiver location uncertainties. We firstly evaluate the Cramér-Rao lower bound (CRLB) for bistatic range (BR) and bistatic range rate (BRR)-based target localization in the presence of calibration targets, which analytically indicates the potential of calibration targets in enhancing localization accuracy. After that, a novel closed-form solution is proposed for target localization using the BR and BRR measurements from the unknown target as well as the additional BR and BRR measurements from the calibration targets. The proposed solution includes two processing steps, referred to as calibration step and localization step respectively. The calibration step is devoted to refine the inaccurate transmitter and receiver locations using the BR and BRR measurements from the calibration targets, and then the localization step is devoted to determine the target position and velocity based on the refined transmitter/receiver locations and the BR/BRR measurements from the unknown target. The accuracy of proposed solution is shown analytically to accomplish the CRLB under sufficiently small BR/BRR measurement noises and transmitter/receiver location errors. Simulation results verify the effectiveness and superiority of the proposed solution over existing algorithms.

Frequency hopping (FH) signal has been widely used in military and civilian fields because of its superior anti-jamming performance. Meanwhile, its natural characteristics, i.e., hopped carrier frequency, brings a great challenge to the reconnaissance of FH emitters. To locate a high-speed moving FH emitter, the estimation of range difference (RD) and range rate difference (RRD) is a key process in passive localization. This paper proposes a joint RD and RRD estimation algorithm with the consideration of range migration (RM) during observation time. In this method, a new operation named frequency reversing transform is first presented to remove the random phase of the FH signal. Then, RD and RRD can be coherently estimated after RM correction via the scaled Fourier transform. This method accomplishes the RD and RRD estimation without knowing the hopped carrier frequencies of received FH signals, and it can be fast implemented by complex multiplications, fast Fourier transform (FFT) and inverse FFT operations without any searching process. Numerical experiments indicate that compared with several representative methods, the proposed estimation method achieves a better anti-noise performance with lower computational cost.

By employing lens antenna arrays, the number of radio frequency (RF) chains in millimeter-wave (mmWave) communications can be significantly reduced. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which may result in a significant loss of energy efficiency (EE). In this paper, we propose a switch selecting network to solve this issue, where the analog part of the beamspace MIMO system is realized by a sub-connected switch selecting network rather than the PS network. Based on the proposed architecture and inspired by the cross-entropy (CE) optimization developed in machine learning, an optimal hybrid cross-entropy (HCE)-based hybrid precoding scheme is designed to maximize the achievable sum rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate, but also outperform the PSs schemes concerning energy efficiency.

We address the covariance matrix estimation problem for radar adaptive detection in a non-Gaussian clutter environment. We first propose an estimation method based on α log-determinant divergence, which estimates the true covariance accurately by solving the geometric mean of the sample covariance matrix (SCM). Since the estimation performance would be seriously degraded when the number of secondary data is insufficient, a knowledge-aided method is then proposed. Under the similarity constraint between the a priori covariance and the true one, a closed form expression is derived by minimizing the α log-determinant divergence between the real covariance and the SCM. Simulation results verify the accuracy of the proposed algorithms in covariance estimation and superiority in target adaptive detection.

The application of micro electro mechanical system (MEMS) is more and more extensive, involving military, medical, communication and other major fields. The progress of science and technology has brought cross era changes to human beings, but also brought troubles to human beings. Because machines can replace most people, which leads to a significant reduction in human exercise, many people have the symptoms of obesity. Therefore, how to effectively detect human exercise energy consumption is of great significance to improve obesity symptoms. The energy consumption detector takes stm32f103zet6 as the core processor and uses the inertial sensor mpu6050 to build a MEMS sensor system to monitor the daily motion state and gait of human body in real time. In the design of the big data algorithm, the adaptive peak detection and step, decision tree two-level classification of motion recognition big data algorithm are organically integrated, and then combined with the acceleration vector value of the motion energy detection big data algorithm, to process the collected motion data, including the acceleration signal, gyroscope and other data processing, and finally complete the feature extraction, get the final recognition and detection results. Through the data reference, we can know that the system can recognize different human motion states. Among them, it has 95% accuracy in the motion recognition of sitting, standing, walking, running, going up and down stairs and lying back, which is basically the same as the top detectors on the market. In the energy consumption detection, it also has 95% accuracy, which proves the correctness of the experimental big data algorithm design, and also improves the accuracy It is proved that the system has good performance and high practicability, and can provide a new idea for obese individual motion detection.

Decision trees are popular machine-learning classification models due to their simplicity and effectiveness. Tai et al. (ESORICS ’17) propose a privacy-preserving decision-tree evaluation protocol purely based on additive homomorphic encryption, without introducing dummy nodes for hiding the tree structure, but it runs a secure comparison for each decision node, resulting in linear complexity. Later protocols (DBSEC ’18, PETS ’19) achieve sublinear (client-side) complexity, yet the server-side path evaluation requires oblivious transfer among 2d real and dummy nodes even for a sparse tree of depth d to hide the tree structure. This paper aims for the best of both worlds and hence the most lightweight protocol to date. Our complete-tree protocol can be easily extended to the sparse-tree setting and the reusable outsourcing setting: a model owner (resp. client) can outsource the decision tree (resp. attributes) to two non-colluding servers for classifications. The outsourced extension supports multi-client joint evaluation, which is the first of its kind without using multikey fully-homomorphic encryption (TDSC ’19). We also extend our protocol for achieving privacy against malicious adversaries. Our experiments compare in various network settings our offline and online communication costs and the online computation time with the prior sublinear protocol of Tueno et al. (PETS ’19) and O(1)-round linear protocols of Kiss et al. (PETS ’19), which can be seen as garbled circuit variants of Tai et al.’s. Our protocols are shown to be desirable for IoT-like scenarios with weak clients and big-data scenarios with high-dimensional feature vectors. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) Published version

Fragments generated from the blast-fragmentation warhead after blasting are typically multiple, fast, small, and dense. In light of the epipolar multitarget feature of blasting fragments, this paper utilizes the movement characteristics of blasting fragments for modeling. Then, the modeling results are adopted in probabilistic data association (PDA) algorithm of multitarget tracking. A novel epipolar multitarget velocity PDA (VPDA) algorithm is proposed based on the movement characteristics of blasting fragments. This algorithm forms the movement characteristics with the finite element simulation results of warhead blasting fragments, utilizes the Doppler velocity probability to reassign the association probability, and updates the state and covariance of each target through the probability weighted fusion. Simulation results demonstrate that, the computational complexity of the proposed algorithm is close to that of PDA algorithm, and the association success rate and the state value update error approximates to the association effects of joint probabilistic data association (JPDA) algorithm, which can effectively track the fragments with identical velocity while reducing the complexity of the epipolar multitarget tracking algorithm, and can respond to the group target tracking scenario.

Reputation/trust-based blockchain systems have attracted considerable research interests for better integrating Internet of Things with blockchain in terms of throughput, scalability, energy efficiency, and incentive aspects. However, most existing works only consider static adversaries. Hence, they are vulnerable to slowly adaptive attackers, who can target validators with high reputation value to severely degrade the system performance. Therefore, we introduce zkRep, a privacy-preserving scheme tailored for reputation-based blockchains. Our basic idea is to hide both the identity and reputation of the validators by periodically changing the identity and reputation commitments (i.e., aliases), which makes it much more difficult for slowly adaptive attackers to identify validators with high reputation value. To realize this idea, we utilize privacy-preserving Pedersen-commitment-based reputation updating and leader election schemes that operate on concealed reputations within an epoch. We also introduce a privacy-preserving identity update protocol that changes the identity and time-window-based cumulative reputation commitments during each epoch transition. We have implemented and evaluated zkRep on the Amazon Web Service. The experimental results and analysis show that zkRep achieves great privacy-preserving features against slowly adaptive attacks with little overhead. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by RGC under Contract CERG 16204418, Contract 16203719, Contract 16204820, and Contract R8015; in part by the Guangdong Natural Science Foundation under Grant 2017A030312008; and in part by the National Research Foundation, Singapore, under its Strategic Capability Research Centres Funding Initiative

Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10−2 m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10−2 m and 1.69 × 10−2 m and the standard variances of 6.77 × 10−4 m and 4.08 × 10−4 m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.

Considering the low estimation accuracy of the traditional interpolation method, this paper, on the basis of second-order cone programming (SOCP), proposes a novel joint time difference of arrival ...

In recent years, target detection has drawn more and more attention in the field of radar signal processing. This paper addresses the coherent integration problem for detecting radar maneuvering targets involving range migration (RM) and Doppler frequency migration (DFM). A novel method based on keystone transform (KT) and coherent integration cubic phase function (CICPF) is proposed. In specific, the RM is firstly removed by KT and fold factor phase compensation. Then, the CICPF estimates velocity and acceleration of target. Finally, the coherent integration is achieved after compensating the RM and DFM. Both simulations and real data processing results validate the performance of proposed method.

Concerned with high pressure of data transmission and heavy computation load for the FDOA estimation for radar pulse signal, a novel fast estimation method based on data compression transmission (DCT) is proposed. Firstly, in order to add the max complex-value corresponding to every pulse's amplitude-frequency response to the pulse description word (PDW), receivers perform Fourier transform (FT) for every received pulse. Secondly, receivers transmit PDW to the data processing center. Thirdly, the data processing center sorts and matches radar pulse trains according to PDW. After that, we perform conjugate product for every pair of matched complex-values and obtain a new complex sequence. Finally, we apply discrete Fourier transform (DFT) on the new obtained sequence to estimate the FDOA. The proposed method can be easily implemented by performing complex product and fast Fourier transform (FFT) without complicated search process. Simulation results show that the proposed method has a better anti-noise capability with a much lower computational complexity.

This paper proposes a closed-form distributed passive source localisation method using time difference of arrival (TDOA) and gain ratios of arrival (GROA) measurements. After removing the unknown parameters in TDOA equations by using GROA measurements, the proposed method applies two-step weighted least-square (LS) minimisations. First, the TDOA and GROA equations are transformed into a set of pseudo-linear equations by introducing additional parameters, and a weighted LS estimation is used to obtain a rough estimate; secondl, this method exploits additional parameters to refine the estimate through another weighted LS estimation. The application of weighting matrix leads to an approximate maximum likelihood estimator and produces a substantial improvement in source localisation accuracy. Both the theoretical analysis and simulation results indicate that the proposed method can achieve the Cramer–Rao Lower Bound at a moderate noise level and outperform the existing methods in terms of localisation...

For wideband array signals, existing self-calibration methods use a first order approximation to simplify the estimation procedure under the assumption of small sensor position errors. However, the performance will degrade severely as the position errors increase. In this paper, a novel self-calibration method based on the time-frequency distributions (TFDs) is proposed to relieve the small errors constraint. We firstly develop an approach for wideband signals to select the TFDs of each source. Then the TFDs are utilized to obtain the propagation delays, which have a simple linear relationship with the sensor positions. The direction of arrival (DOA) and positions are finally estimated by a two-step iteration without any approximation. Simulation results demonstrate that the proposed method can achieve accurate DOA and position estimation over a broad range of position errors with lower computational complexity, and outperforms existing self-calibration algorithms.

The performance of the existing robust beamformers can be still degraded by the bias between the nominal steering vector and the actual one. In this paper, a novel robust wideband beamformer based on the time–frequency distributions is proposed, which can estimate the steering vector accurately even in the presence of direction and sensor location errors. Firstly, it develops an approach for wideband signals to select the single-source auto-source time–frequency (TF) points of the source signals. Then these TF points are utilized to obtain the steering vectors without using the perturbed array manifold and direction information. Finally, a higher output signal-to-interference-plus-noise ratio (SINR) is achieved for the minor bias between the estimated steering vectors and the actual ones. Simulation results demonstrate that the proposed algorithm outperforms other conventional robust beamforming approaches and can achieve high output SINR close to the ideal beamformer over a broad range of direction and sensor location errors.

A revised recurrent neural networks method for the passive source localization is proposed in this paper. The cost function has been designed which makes the transformation from transmitters’ localization into the outputs of the settled revised recurrent neural networks through spatial partition. The neural networks are chaotic and stable in convergence. The received signal model is constructed firstly. The parameters of the recurrent neural networks have been trained properly according to the scene. The experiments and analysis display that the revised recurrent neural networks solution not only obtains high precision location, but also has high convergence rate.

This study deals with the problem of joint delay-Doppler estimation in a practically motivated scenario of passive bistatic radar, where the surveillance channel is polluted by the direct-path signal residual. A new joint delay-Doppler maximum-likelihood estimator (MLE) based on Markov chain Monte Carlo (MCMC) is proposed. The MCMC method allows one to compute the MLE in a computationally efficient manner. The proposed estimator is based upon generating random variates using a Markov Chain whose stationary distribution approximates the likelihood function and guarantees convergence to the global maximum. In contrast to the recently proposed modified cross-correlation estimator, and the expectation-maximisation-based MLE, it avoids grid search which may lead to a straddle loss or initialisation-dependent iteration which may lead to convergence problems. Simulation results indicate that the proposed estimator achieves a significant performance improvement over existing methods.

Ultra-wideband frequency modulated continuous wave (FMCW) radar has the ability to achieve high-range resolution. Combined with the inverse synthetic aperture technique, high azimuth resolution can be realized under a large rotation angle. However, the range-azimuth coupling problem seriously restricts the inverse synthetic aperture radar (ISAR) imaging performance. Based on the turntable model, traditional match-filter-based, range Doppler algorithms (RDAs) and the back projection algorithm (BPA) are investigated. To eliminate the sidelobe effects of traditional algorithms, compressed sensing (CS) is preferred. Considering the block structure of a signal at high resolution, a block-sparsity adaptive matching pursuit algorithm (BSAMP) is proposed. By matching pursuit and backtracking, a signal with unknown sparsity can be recovered accurately by updating the support set iteratively. Finally, several experiments are conducted. In comparison with other algorithms, the results from processing the simulation data, some simple targets, and a complex target indicate the effectiveness and superiority of the proposed algorithm.

The COVID-19 pandemic has highlighted the need for generic reagents and flexible systems in diagnostic testing. Magnetic bead-based nucleic acid extraction protocols using 96-well plates on open liquid handlers are readily amenable to meet this need. Here, one such approach is rigorously optimized to minimize cross-well contamination while maintaining sensitivity.Article SummaryA scalable, non-proprietary, magnetic bead-based automated nucleic acid extraction protocol optimised for minimum cross-well contamination

Sharing information to others is common nowadays, but the question is with whom to share. To address this problem, we propose the notion of secret transfer with access structure (STAS). STAS is a two-party computation protocol that enables the server to transfer a secret to a client who satisfies the prescribed access structure. In this paper, we focus on the case of STAS for threshold access structure, i.e. threshold secret transfer (TST). We also discuss how to replace it with linear secret sharing to make the access structure more expressive. Our proposed TST scheme enables a number of applications including a simple construction of oblivious transfer with threshold access control, and (a variant of) threshold private set intersection (t-PSI), which are the first of their kinds in the literature to the best of our knowledge. Moreover, we show that TST is useful a number of applications such as privacy-preserving matchmaking with interesting features. The underlying primitive of STAS is a variant of oblivious transfer (OT) which we call OT for sparse array. We provide two constructions which are inspired from state-of-the-art PSI techniques including oblivious polynomial evaluation and garbled Bloom filter (GBF). We implemented the more efficient construction and provide its performance evaluation.

In the relay protection device, the HMI module generally directly drives the display through the CPU plus the liquid crystal driving chip. Due to the harsh EMC in the substation, the actual project found that this method is likely to cause abnormal screen display. To overcome this situation, we install the screen on a dedicated panel and connect to the HMI via the Ethernet port. At this time, the HMI needs to send the LCD dot matrix data to the panel to display. When the original LCD dot matrix data is directly transmitted, the amount of data transmitted is large, occupying a large amount of bandwidth. This paper proposes a FF0 lossless compression algorithm, which is specifically used for compression of such black and white lattice data. After compression by this algorithm, the average data transfer of black and white screens is reduced by 35% in actual engineering applications. In the best case, when using all black or all white screens, the amount of data transferred is only 0.78%.

This paper addresses the estimation problem of range difference and range rate difference for unknown frequency hopping signals in passive emitter localization, considering the range migration (RM) of the high-speed moving target within the observation time. A coherent estimation algorithm based on the scaled Fourier transform is proposed. This method can effectively remove the RM and random phase effects. The whole estimation process can be fast implemented without any searching operation. Numerical experiments demonstrate that the estimation performance of proposed algorithm is superior to existing algorithms, and comparable to the maximum likelihood estimator.

In recent years, target detection has drawn increasing attention in the field of radar signal processing. This paper addresses the radar coherent integration problem for detecting highly maneuvering targets with jerk motion. A novel method based on location rotation transform (LRT) and threedimensional scaled Fourier transform (TDSFT), namely LRT- TDSFT, is proposed in this paper. In this method, the linear range migration (LRM) is first eliminated by LRT. Then, the linear Doppler frequency migration (LDFM) and quadratic Doppler frequency migration (QDFM) are accurately estimated via the newly proposed TDSFT. Analysis shows that, the LRT- TDSFT achieves satisfactory detection performance while keeping acceptable computational complexity. Finally, both simulations and real data experiments are presented to demonstrate the effectiveness of the proposed method.

The beamspace MIMO can significantly reduce the number of power radio frequency (RF) chains in millimetre-wave (mmWave) communications by utilizing lens antenna array. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which result in a significant loss of energy efficiency. In the paper, we propose a switch selection network to solve this problem, where the analog component of beamspace MIMO systems is realized by a sub-connected switch network rather than the PS. Based on the proposed architecture, an optimal hybrid cross-entropy (HCE) based hybrid precoding scheme is designed to maximize the achieveable sum-rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate performance, but also outperform the PSs schemes with regard to energy efficiency.

The target localisation in a multistatic passive radar system using bistatic range measurements from multiple transmitter–receiver pairs is considered. A novel closed-form solution employing multidimensional scaling (MDS) analysis technique is proposed, which is based on the optimisation of a cost function related to the scalar product matrix in the classical MDS framework. The proposed solution is robust to large measurement noise. Simulation results demonstrate the proposed solution achieves a significant performance improvement over existing spherical interpolation, spherical intersection and two-step weighted least squares methods, and attains the Cramer–Rao lower bound at a relatively higher measurement noise level before the threshold effect occurs.

This paper addresses the joint estimation of time and frequency differences of arrival (TDOAs/FDOAs) between overlapped multipath echo signals and reference signal in passive bistatic radar (PBR). A novel method based on MUltiple SIgnal Classification (MUSIC) is proposed. Its main strategy is to divide the autocorrelation matrix into signal subspace and noise subspace, and use the orthogonality between these two subspaces to estimate the TDOA and FDOA parameters. The proposed MUSIC-based method does not require any prior knowledge of the number of multipath echo signals and offers robust and super-resolution performance. Simulation results demonstrate that the proposed MUSIC-based method dramatically outperforms existing methods.

By employing lens antenna array and selection network, the beamspace MIMO can significantly reduce the number of power consumption radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems. However, most existing studies ignored the high energy consumption problem in the beamspace MIMO systems, which resulted in an obvious loss of the achievable sum-rate. To solve this problem, in this paper, we propose a new overlapped subarray phase shifter (OSAPS) architecture with low complexity for mmWave massive MIMO systems, where the analog part is realized by a reduction of phase shifters and power combiners. Simulation results show that the proposed overlapped sub-array phase shifter selection network (OSAPSSN) based hybrid precoding can effectively achieve the satisfied sum-rate performance, and outperform the conventional schemes with regard to energy efficiency.

We study how to construct secure digital signature schemes in the presence of kleptographic attacks. Our work utilizes an offline watchdog to clip the power of subversions via only one-time black-box testing of the implementation. Previous results essentially rely on an online watchdog which requires the collection of all communicating transcripts (or active re-randomization of messages).

This paper considers the long-time coherent detection problem for maneuvering targets with jerk motion. A novel method based on product-scaled integrated cubic phase function (PSICPF) is proposed. The main strategy of PSICPF is to estimate target’s motion parameters along the slow time for each range frequency cell. In order to eliminate the coupling terms between range frequency and slow time, the scaled nonuniform fast Fourier transform (SNUFFT) is newly defined in the integrated cubic phase function (ICPF). Then, the product operation is employed to coherently synthesize the estimation results, improve the antinoise performance, and suppress the cross terms. Finally, coherent integration is achieved via keystone transform (KT) and fold factor searching. Analysis demonstrates that the SNUFFT has the same computational complexity with nonuniform fast Fourier transform (NUFFT), and thus the PSICPF could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT), and NUFFT. Detailed comparisons with other representative methods in computational cost, motion parameter estimation performance, and detection ability indicate that the PSICPF could achieve a good balance between the computational cost and detection ability. Simulations and real data processing results are presented to verify the effectiveness of the proposed method.

To avoid the use of specific conversion coefficients with high expense and unacceptable prediction accuracy, a probability-based prediction method is proposed by considering probabilistic feature parameters, to predict the service safety life (SSL) of aeroengine turbine blades. The direct correlation between laboratory remaining life (LRL) and SSL was firstly established by considering probabilistic feature parameters. By conducting Combined high and low Cycle Fatigue (CCF) tests of turbine blades, the effectiveness of the developed method was validated based on the failure event. The proposed method was further verified by predicting the SSL of treated blades with certain operation time. In respect of the studies, it is illustrated that (1) the SSL of turbine blade can be reasonably reflected by the LRL in respect of probabilistic feature parameters; (2) the prediction errors of the raw and treated blades are 2.2% and 12.7%, respectively, indicating that the developed probability-based prediction method has acceptable prediction precision and is an effective method in the SSL prediction of aeroengine turbine blades; (3) the developed method needs less samples than the specific conversion coefficients method, indicating that the SSL prediction of turbine blade needs fewer time and costs. The efforts of this study provide a promising approach for the SSL prediction of turbine blades, offer a useful guidance for the service life management of aeroengine turbine blades to reduce the cost of expense and time and enhance the safety of aeroengine operation.

Accurate multipath number estimation is the prerequisite for MUSIC algorithm to achieve excellent superresolution performance. To solve the problem that the existing multipath number estimation algorithms based on eigenvalue distribution usually require a large amount of computation, this paper proposed a fast multipath number estimation algorithm based on QR decomposition under the OFDM signal model. This algorithm only needs few times QR iteration decomposition to obtain the pseudo-eigenvalues of the matrix, and uses them to estimate the multipath number through two-step comparison. Due to the avoidance of accurate eigenvalues solution, compared with the traditional algorithms based on eigenvalue distribution, proposed algorithm’s computation is smaller. Simulation results showed that the proposed algorithm has better detection performance in low SNR and few snapshots than existing algorithms.

To enhance the computational performance of the structural dynamic reliability estimation, advanced extremum Kriging method (AEKM) is proposed fusing Kriging surrogate model, multi-population genetic algorithm and response surface method-based extremum thought (ERSM) in this paper. The applicability of the AEKM is verified via the blisk radial deformation dynamic reliability analysis of an aeroengine high-pressure compressor. The results show that the reliability level of the blisk is 0.9956 as the allowable value of radial running deformation 1.75×10−3 m. Moreover, the developed AEKM is superior to other three surrogate models, including ERSM, K-ERSM and IK-ERSM, in terms of computing precision and analytical efficiency. Besides, the analytical precision of the developed method is basically consistent with the direct simulation, and the analytical time is obviously superior to the direct simulation. Therefore, the AEKM is illustrated to be an effective way for structural dynamic reliability analysis, which enriches complex structural reliability theory.

The evaluation of flexible mechanism involving multi-body dynamics with high nonlinearity and transients urgently requires an efficient evaluation method to enhance its reliability and safety. In this work, an enhanced network learning method (ENLM) is proposed to improve the modeling precision and simulation efficiency in flexible mechanism reliability evaluation, by introducing generalized regression neural network (GRNN) and multi-population genetic algorithm (MPGA) into extremum response surface method (ERSM). In the ENLM modeling, the ERSM is adopted to reasonably handle transients (time-varying) problem in motion reliability analysis by considering one extreme value in whole response process; the GRNN is applied to address high-nonlinearity in surrogate modeling; the MPGA is utilized to find the optimal model parameters in ENLM modeling. In respect of the developed ENLM, the motion reliability of two-link flexible robot manipulator (TFRM) was evaluated, with regard to the related input random parameters to material density, elastic modulus, section sizes, and deformations of components. In term of this study, it is illustrated that (i) the comprehensive reliability of flexible robot manipulator is 0.951 when the allowable deformation is 1.8×10−2 m; (ii) the maximum deformations of member-1 and member-2 obey normal distributions with the means of 1.45×10−2 m and 1.69×10−2 m as well as the standard variances of 6.77×10−4 m and 4.08×10−4 m, respectively. The comparison of methods demonstrates that the ENLM improves the modeling precision by 3.29% and reduces the simulation efficiency by 1.19 s under 10 000 simulations, and the strengths of the ENLM with high modeling precision and high simulation efficiency become more obvious with the increase of simulations. The efforts of this study provide a learning-based reliability analysis way (i.e., ENLM) for the motion reliability design optimization of flexible mechanism and enrich mechanical reliability theory.

This paper considers the coherent integration problem for Doppler-ambiguous maneuvering target. A novel method based on the product scaled periodic Lv's distribution (PSPLVD) is proposed. To be specific, the acceleration could be accurately estimated via Lv's distribution (LVD). Then, the periodic discrete Fourier transform (DFT), which periodically extends the frequency scope of DFT, is introduced to modify the LVD to estimate the unambiguous centroid frequency. Thereafter, scaling and product operations are respectively employed to eliminate the coupling and suppress the grating lobes. Finally, coherent integration is achieved after compensating off the range migration and Doppler frequency migration. Cross term analysis indicates the applicability in the scenario of multiple targets. Efficient implementation based on chirp-z transform is also presented to alleviate the computational burden and eliminate the brute force searching procedure. Detailed comparisons with several representative methods lead us to conclude that the proposed method could strike a good balance between computational cost and detection probability. Extensive simulations and real data processing results are provided to verify the proposed method.