Unmanned aerial vehicles (UAVs) may be subjected to unintentional radio frequency interference (RFI) or hostile jamming attack which will lead to fail to track global navigation satellite system (GNSS) signals. Therefore, the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem. In this paper, a distortionless phase digital beamforming (DBF) algorithm with self-calibration antenna arrays is proposed, which enables to obtain distortionless carrier phase while suppressing jamming. Additionally, architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed. Finally, the performance of the algorithm is evaluated, including antenna calibration accuracy, carrier phase distortionless accuracy, and carrier phase measurement accuracy without jamming. Meanwhile, the maximal jamming to signal ratio (JSR) and real time kinematic (RTK) positioning accuracy under wideband jamming are also investigated. The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.
A novel variable step-size modified super-exponential iteration (MSEI) decision feedback blind equalization (DFE) algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm. Based on the MSEI-DFE algorithm, it is first proposed to develop an error function as an improvement to the error function of MSEI, which effectively achieves faster convergence speed of the algorithm. Subsequently, a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero, so as to further improve the convergence speed of the algorithm. In the end, a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals. For the multipath underwater acoustic channel with mixed phase and phase rotation, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM) modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance. The results show that the proposed algorithm can considerably improve convergence speed and steady-state error, make effective compensation for phase rotation, and efficiently facilitate carrier recovery.
A generalized labeled multi-Bernoulli (GLMB) filter with motion mode label based on the track-before-detect (TBD) strategy for maneuvering targets in sea clutter with heavy tail, in which the transitions of the mode of target motions are modeled by using jump Markovian system (JMS), is presented in this paper. The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model. In update, we derive a tractable GLMB density, which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence (KLD), to enable the next recursive cycle. The relevant simulation results prove that the proposed multiple-model GLMB-TBD (MM-GLMB-TBD) algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background. Additionally, the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD (DP-TBD) algorithm. Meanwhile, the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD (JMS-MeMBer-TBD) filter in estimation error with the basically same computational cost. Finally, the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.
To reduce the negative impact of the power amplifier (PA) nonlinear distortion caused by the orthogonal frequency division multiplexing (OFDM) waveform with high peak-to-average power ratio (PAPR) in integrated radar and communication (RadCom) systems is studied, the channel estimation in passive sensing scenarios. Adaptive channel estimation methods are proposed based on different pilot patterns, considering nonlinear distortion and channel sparsity. The proposed methods achieve sparse channel results by manipulating the least squares (LS) frequency-domain channel estimation results to preserve the most significant taps. The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion. Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns. In addition, the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.
To analyze the influence of time synchronization error, phase synchronization error, frequency synchronization error, internal delay of the transceiver system, and range error and angle error between the unit radars on the target detection performance, firstly, a spatial detection model of distributed high-frequency surface wave radar (distributed-HFSWR) is established in this paper. In this model, a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio (SNR), and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error. The direct wave component is extracted from the spectrum, the range estimation error and Doppler estimation error are reduced by the method of curve fitting, and the fitting accuracy of the parameters is improved. Then, the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed. The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given. Finally, the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting, the experimental data is compensated to correct the shift of the target, and finally the correct target parameter information is obtained. Simulations and experimental results demonstrate the superiority and correctness of the proposed method, theoretical derivation and detection model proposed in this paper.
Imaging detection is an important means to obtain target information. The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classification of the target. In practical applications, due to the mixed scenario, it is difficult to meet the needs of target recognition. Compared with intensity detection, the method of polarization detection can effectively enhance the accuracy of ground object target recognition (such as the camouflage target). In this paper, the reflection mechanism of the target surface is studied from the microscopic point of view, and the polarization characteristic model is established to express the relationship between the polarization state of the reflected signal and the target surface parameters. The polarization characteristic test experiment is carried out, and the target surface parameters are retrieved using the experimental data. The results show that the degree of polarization (DOP) is closely related to the detection zenith angle and azimuth angle. The (DOP) of the target is the smallest in the direction of light source incidence and the largest in the direction of specular reflection. Different materials have different polarization characteristics. By comparing their DOP, target classification can be achieved.
Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver. To reduce the computational complexity and latency of code acquisition, this paper proposes an efficient scheme employing sparse Fourier transform (SFT) and the relevant hardware architecture for field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementation. Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure. Compared with the existing code acquisition approaches, it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.
The perception module of advanced driver assistance systems plays a vital role. Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer. This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme. A binocular stereo vision sensor composed of two cameras and a light deterction and ranging (LiDAR) sensor is used to jointly perceive the environment, and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map. This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors. Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.
Aiming at evaluating and predicting rapidly and accurately a high sensitivity receiver’s adaptability in complex electromagnetic environments, a novel testing and prediction method based on dual-channel multi-frequency is proposed to improve the traditional two-tone test. Firstly, two signal generators are used to generate signals at the radio frequency (RF) by frequency scanning, and then a rapid measurement at the intermediate frequency (IF) output port is carried out to obtain a huge amount of sample data for the subsequent analysis. Secondly, the IF output response data are modeled and analyzed to construct the linear and nonlinear response constraint equations in the frequency domain and prediction models in the power domain, which provide the theoretical criteria for interpreting and predicting electromagnetic susceptibility (EMS) of the receiver. An experiment performed on a radar receiver confirms the reliability of the method proposed in this paper. It shows that the interference of each harmonic frequency and each order to the receiver can be identified and predicted with the sensitivity model. Based on this, fast and comprehensive evaluation and prediction of the receiver’s EMS in complex environment can be efficiently realized.
In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.
Acoustic source localization (ASL) and sound event detection (SED) are two widely pursued independent research fields. In recent years, in order to achieve a more complete spatial and temporal representation of sound field, sound event localization and detection (SELD) has become a very active research topic. This paper presents a deep learning-based multi-overlapping sound event localization and detection algorithm in three-dimensional space. Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features. These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively. The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features. Finally, a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm. Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.
In order to solve the problem that the performance of traditional localization methods for mixed near-field sources (NFSs) and far-field sources (FFSs) degrades under impulsive noise, a robust and novel localization method is proposed. After eliminating the impacts of impulsive noise by the weighted outlier filter, the direction of arrivals (DOAs) of FFSs can be estimated by multiple signal classification (MUSIC) spectral peaks search. Based on the DOAs information of FFSs, the separation of mixed sources can be performed. Finally, the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors. The Cramer-Rao bounds (CRB) for the unbiased estimations of DOA and range under impulsive noise have been drawn. Simulation experiments verify that the proposed method has advantages in probability of successful estimation (PSE) and root mean square error (RMSE) compared with existing localization methods. It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio (GSNR), few snapshots, and strong impulse.
In electromagnetic countermeasures circumstances, synthetic aperture radar (SAR) imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming (MISRJ), which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns. This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning. In the algorithm, the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation. Online dictionary learning is followed to separate real signals from jamming slices. Under the learned representation, time-varying MISRJs are suppressed effectively. Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.
Large calculation error can be formed by directly employing the conventional Yee’s grid to curve surfaces. In order to alleviate such condition, unconditionally stable Crank-Nicolson Douglas-Gunn (CNDG) algorithm with is proposed for rotationally symmetric multi-scale problems in anisotropic magnetized plasma. Within the CNDG algorithm, an alternative scheme for the simulation of anisotropic plasma is proposed in body-of-revolution domains. Convolutional perfectly matched layer (CPML) formulation is proposed to efficiently solve the open region problems. Numerical example is carried out for the illustration of effectiveness including the efficiency, resources, and absorption. Through the results, it can be concluded that the proposed scheme shows considerable performance during the simulation.
For time-of-flight (TOF) light detection and ranging (LiDAR), a three-channel high-performance transimpedance amplifier (TIA) with high immunity to input load capacitance is presented. A regulated cascade (RGC) as the input stage is at the core of the complementary metal oxide semiconductor (CMOS) circuit chip, giving it more immunity to input photodiode detectors. A simple smart output interface acting as a feedback structure, which is rarely found in other designs, reduces the chip size and power consumption simultaneously. The circuit is designed using a 0.5 μm CMOS process technology to achieve low cost. The device delivers a 33.87 dB? transimpedance gain at 350 MHz. With a higher input load capacitance, it shows a ?3 dB bandwidth of 461 MHz, indicating a better detector tolerance at the front end of the system. Under a 3.3 V supply voltage, the device consumes 5.2 mW, and the total chip area with three channels is 402.8×597.0 μm2 (including the test pads).
Non-uniform linear array (NULA) configurations are well renowned due to their structural ability for providing increased degrees of freedom (DOF) and wider array aperture than uniform linear arrays (ULAs). These characteristics play a significant role in improving the direction-of-arrival (DOA) estimation accuracy. However, most of the existing NULA geometries are primarily applicable to circular sources (CSs), while they limitedly improve the DOF and continuous virtual aperture for non-circular sources (NCSs). Toward this purpose, we present a triad-displaced ULAs (Tdis-ULAs) configuration for NCS. The Tdis-ULAs structure generally consists of three ULAs, which are appropriately placed. The proposed antenna array approach fully exploits the non-circular characteristics of the sources. Given the same number of elements, the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors. Advantageously, the number of uniform DOF, optimal distribution of elements among the ULAs, and precise element positions are uniquely determined by the closed-form expressions. Moreover, the proposed array also produces a filled resulting co-array. Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.
Time synchronization is one of the base techniques in wireless sensor networks (WSNs). This paper proposes a novel time synchronization protocol which is a robust consensus-based algorithm in the existence of transmission delay and packet loss. It compensates for transmission delay and packet loss firstly, and then, estimates clock skew and clock offset in two steps. Simulation and experiment results show that the proposed protocol can keep synchronization error below 2 μs in the grid network of 10 nodes or the random network of 90 nodes. Moreover, the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.
The electric-controlled metasurface antenna array (ECMSAA) with ultra-wideband frequency reconfigurable reflection suppression is proposed and realized. Firstly, an electric- controlled metasurface with ultra-wideband frequency reconfigurable in-phase reflection characteristics is designed. The element of the ECMSAA is constructed by loading the single electric-controlled metasurface unit on the conventional patch antenna element. The radiation properties of the conventional patch antenna and the reflection performance of electric-controlled metasurface are maintained when the antenna and the metasurface are integrated. Thus, the ECMSAA elements have excellent radiation properties and ultra-wideband frequency reconfigurable in-phase reflection characteristics simultaneously. To take a further step, a 6×10 ECMSAA is realized based on the designed metasurface antenna element. Simulated and measured results prove that the reflection of the ECMSAA is dynamically suppressed in the P and L bands. Meanwhile, high-gain and multi-polarization radiation properties of the ECMSAA are achieved. This design method not only realizes the frequency reconfigurable reflection suppression of the antenna array in the ultra-wide frequency band but also provides a way to develop an intelligent low-scattering antenna.
In this paper, we propose an effective full array and sparse array adaptive beamforming scheme that can be applied for multiple desired signals based on the branch-and-bound algorithm. Adaptive beamforming for the multiple desired signals is realized by the improved Capon method. At the same time, the sidelobe constraint is added to reduce the sidelobe level. To reduce the pointing errors of multiple desired signals, the array response phase of the desired signal is firstly optimized by using auxilary variables while keeping the response amplitude unchanged. The whole design is formulated as a convex optimization problem solved by the branch-and-bound algorithm. In addition, the beamformer weight vector is penalized with the modified reweighted ${l_1}$-norm to achieve sparsity. Theoretical analysis and simulation results show that the proposed algorithm has lower sidelobe level, higher SINR, and less pointing error than the state-of-the-art methods in the case of a single expected signal and multiple desired signals.
The conformal array can make full use of the aperture, save space, meet the requirements of aerodynamics, and is sensitive to polarization information. It has broad application prospects in military, aerospace, and communication fields. The joint polarization and direction-of-arrival (DOA) estimation based on the conformal array and the theoretical analysis of its parameter estimation performance are the key factors to promote the engineering application of the conformal array. To solve these problems, this paper establishes the wave field signal model of the conformal array. Then, for the case of a single target, the cost function of the maximum likelihood (ML) estimator is rewritten with Rayleigh quotient from a problem of maximizing the ratio of quadratic forms into those of minimizing quadratic forms. On this basis, rapid parameter estimation is achieved with the idea of manifold separation technology (MST). Compared with the modified variable projection (MVP) algorithm, it reduces the computational complexity and improves the parameter estimation performance. Meanwhile, the MST is used to solve the partial derivative of the steering vector. Then, the theoretical performance of ML, the multiple signal classification (MUSIC) estimator and Cramer-Rao bound (CRB) based on the conformal array are derived respectively, which provides theoretical foundation for the engineering application of the conformal array. Finally, the simulation experiment verifies the effectiveness of the proposed method.
A terahertz (THz) wave transmitted through vegetation experiences both absorption and scattering caused by the air molecules and leaves. This paper presents the scattering attenuation characteristics of vegetation in a THz range. The theoretical path loss model near the vegetation yields the average attenuation of THz waves in a mixed channel composed of air and vegetation leaves. Furthermore, a simplified model of the vegetation structure is obtained for generic vegetation types based on a variety of parameters, such as leaf size, distribution, and moisture content. Finally, based on specific vegetation species and different levels of air humidity, the attenuation characteristics under different conditions are calculated, and the influence of different model parameters on the attenuation characteristics is obtained.
The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.
Least squares projection twin support vector machine (LSPTSVM) has faster computing speed than classical least squares support vector machine (LSSVM). However, LSPTSVM is sensitive to outliers and its solution lacks sparsity. Therefore, it is difficult for LSPTSVM to process large-scale datasets with outliers. In this paper, we propose a robust LSPTSVM model (called R-LSPTSVM) by applying truncated least squares loss function. The robustness of R-LSPTSVM is proved from a weighted perspective. Furthermore, we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space. Finally, the sparse R-LSPTSVM algorithm (SR-LSPTSVM) is proposed. Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.
Taking the real part and the imaginary part of complex sound pressure of the sound field as features, a transfer learning model is constructed. Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network (CNN), the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem. The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method, realize the range estimation for the shallow source in the experiment, and compare the range estimation performance of the underwater target sound source of four methods: matched field processing (MFP), generalized regression neural network (GRNN), traditional CNN, and transfer learning. Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes, and the estimation performance is remarkably better than that of other methods.
To achieve robust communication in high mobility scenarios, an iterative equalization algorithm based on alternating minimization (AM) is proposed for the orthogonal time frequency space (OTFS) system. The algorithm approximates the equalization problem to a convex function optimization problem in the real-valued domain and solves the problem iteratively using the AM algorithm. In the iterative process, the complexity of the proposed algorithm is reduced further based on the study of the cyclic structure and sparse property of the OTFS channel matrix in the delay-Doppler (DD) domain. The new method for OTFS is simulated and verified in a high-speed mobile scenario and the results show that the proposed equalization algorithm has excellent bit error rate performance with low complexity.
In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection, an improved detection algorithm of infrared small and dim target is proposed in this paper. Firstly, the original infrared images are changed into a new infrared patch tensor mode through data reconstruction. Then, the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics, and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness. Finally, the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image, and the final small target is worked out by a simple partitioning algorithm. The test results in various space-based downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate. It is a kind of infrared small and dim target detection method with good performance.
Aiming at the problem of detecting a distributed target when signal mismatch occurs, this paper proposes a tunable detector parameterized by an adjustable parameter. By adjusting the parameter, the tunable detector can achieve robust or selective detection of mismatched signals. Moreover, the proposed tunable detector, with a proper tunable parameter, can provide higher detection probability compared with existing detectors in the case of no signal mismatch. In addition, the proposed tunable detector possesses the constant false alarm rate property with the unknown noise covariance matrix.
Recognition of pulse repetition interval (PRI) modulation is a fundamental task in the interpretation of radar intentions. However, the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences. The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification, while the actual situation is that radar pulses reach the receiver continuously, and there is no completely reliable method to achieve this division in the case of non-cooperative reception. Based on the above actual needs, this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model, which does not need to divide the PRI sequence in advance. Compared with the sliding window method, it can more effectively realize the recognition of the dynamically varying PRI modulation.
Memristor with memory properties can be applied to connection points (synapses) between cells in a cellular neural network (CNN). This paper highlights memristor crossbar-based multilayer CNN (MCM-CNN) and its application to edge detection. An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors. MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation. Figure of merit (FOM) is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results. Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
A dimension decomposition (DIDE) method for multiple incoherent source localization using uniform circular array (UCA) is proposed. Due to the fact that the far-field signal can be considered as the state where the range parameter of the near-field signal is infinite, the algorithm for the near-field source localization is also suitable for estimating the direction of arrival (DOA) of far-field signals. By decomposing the first and second exponent term of the steering vector, the three-dimensional (3-D) parameter is transformed into two-dimensional (2-D) and one-dimensional (1-D) parameter estimation. First, by partitioning the received data, we exploit propagator to acquire the noise subspace. Next, the objective function is established and partial derivative is applied to acquire the spatial spectrum of 2-D DOA. At last, the estimated 2-D DOA is utilized to calculate the phase of the decomposed vector, and the least squares (LS) is performed to acquire the range parameters. In comparison to the existing algorithms, the proposed DIDE algorithm requires neither the eigendecomposition of covariance matrix nor the search process of range spatial spectrum, which can achieve satisfactory localization and reduce computational complexity. Simulations are implemented to illustrate the advantages of the proposed DIDE method. Moreover, simulations demonstrate that the proposed DIDE method can also classify the mixed far-field and near-field signals.
