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Sparsity-driven synthetic aperture radar (SAR) imaging technique for frequency modulation continuous wave (FMCW) has already shown the superiority in terms of performance improvement in imaging and recovery from down-sampled data. However, restricted by the computational cost, conventional FMCW SAR sparse imaging method based on observation matrix is not able to achieve the large-scale scene reconstruction, not to mention real-time processing. To solve this problem, the FMCW SAR sparse imaging theory based on approximated observation is proposed by using an echo simulation operator to replace typical observation matrix, and recovering the scene via 2-D regularization operation. This new technology can achieve high-resolution sparse imaging of the scene with a computational cost close to that of traditional matched filtering algorithms, which makes several applications, such as early-warning and battlefield monitoring, possible by using FMCW SAR sparse imaging system. In this article, we present the recent research progress on approximated observationbased FMCW SAR sparse imaging to deal with a few key issues for practical radar systems. In particular, we describe: first, Lq-norm (0 2,q-norm regularization-based imaging technique that minimizes the azimuth ambiguities in high-resolution sparse imaging; and third, a sparse imaging technique that supports real-time applications of FMCW SAR imaging.

This paper addresses the target localization problem in complex multipath propagation environment for three-dimensional (3-D) radar systems. Firstly, an approach based on the singular value decomposition (SVD) technique is developed to reduce the data dimension and formulate the joint multiple snapshot sparse representation problem in the signal subspace domain. Subsequently, a novel sparse representation based DOA estimation algorithm, combined with alternatingly iterative and dictionary refinement techniques, is proposed. The Cramér-Rao bounds (CRB) for the target DOA and attenuation coefficient estimations of multipath model are derived in closed forms. Experimental results based on both simulated data and measured data indicate that the target localization accuracy can be effectively enhanced by utilizing the proposed algorithm in complex terrain and/or limited snapshot scenarios.

In this paper, a novel direction-of-arrival (DOA) estimation algorithm is proposed for noncircular signals with nonuniform noise by using the unitary matrix completion (UMC) technique. First, the proposed method utilizes the noncircular property of signals to design a virtual array for approximately doubling the array aperture. Then, the virtual complex-valued covariance matrix with the unknown nonuniform noise is transformed into the real-valued one by utilizing the unitary transformation to improve the computational efficiency. Next, a novel UMC method is formulated for the DOA estimation to remove the influence of nonuniform noise. Finally, the DOA without the influence of the unknown noncircularity phase is obtained by using the modified estimation of signal parameters via rotational invariance technique (ESPRIT). Especially, for handling the coherent sources, the forward-backward spatial smoothing technique is utilized to reconstruct a full-rank covariance matrix so that the signal subspace and the noise subspace can be correctly separated. Due to utilizing the extended array aperture and the unitary transformation, the proposed method can identify more sources than the number of physical sensors and provides higher angular resolution and better estimation performance. Compared with the existing DOA estimation algorithms for noncircular signals, the proposed one can effectively suppress the influence of the nonuniform noise. The simulation results are provided to verify the effectiveness and superiority of the proposed method.

A novel multiple azimuth beams (MAB) inverse synthetic aperture radar (ISAR) system, as well as, the corresponding processing algorithms are proposed in this paper, which is a promising technology for wide area surveillance (WAS). This system concept is intended to obtain wide area ISAR images of multiple moving targets, which would greatly increase the surveillance velocity range compared with the single beam system. The antenna transmits a sequence of high-gain and narrow radar beams to quickly scan a wide area, and receives the echoes from all the subswaths simultaneously. Two processing methods based on digital beamforming (DBF) are presented to separate the overlapped signals for achieving the wide area ISAR images. The performance of the proposed system is analyzed from the perspectives of the surveillance area, surveillance velocity range, and steering angle error. Finally, an exemplary ISAR system is presented and verified by the simulation experiments.

Airborne circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems are attractive tools for air-to-ground surveillance and monitoring, because of their advantages of short revisit time and large coverage. This paper proposes an accurate imaging and Doppler chirp rate estimation algorithm for the airborne CSSAR-GMTI system with high range resolution. A key step of this algorithm is to utilize an alternation strategy to implement the correction of the target's range cell migration and the estimation of the range equation's quadratic coefficient alternatively. This step enables the proposed algorithm to achieve an accurate imaging and Doppler chirp rate estimation of a ground moving target. In addition, this paper presents an investigation on the accuracy of the quadratic-approximated range equation for ground moving target imaging. Numerical experiments are conducted to validate the proposed algorithm.

The growing popularity of unmanned aerial vehicle (UAV) attracts significant research interests and applications including low-altitude and airborne vehicles. Since there is no declared spectrum allocated to UAV communications, opportunistic transmission has been commonly considered as an important way for supporting UAV communications. When sharing the same spectrum with other users such as satellites and mobile base stations, accurate spectrum sensing and allocation are of critical importance for UAV communications to avoid serious interference. As the UAVs can constantly move to different locations with various spectrum environments, the spectrum decision may be invalid only in a short period, leading to require fast spectrum sensing. Furthermore, an UAV needs to predict possible temporal and spatial vacations of the spectrum. In this case, the spectrum prediction has a high dimensional state space which is notoriously difficult to solve. In this paper, some other issues such as how to determine the spectrum processing time and how to detect the primary signals with high priority to avoid interference, are also discussed. Finally, a fast spectrum sensing algorithm is proposed to improve the energy detection performance by optimizing the error estimation and a constant ratio of missed detection. Our proposed algorithm does not require high computational capability and can achieve relatively accurate sensing in low signal-to-noise ratio scenarios.

Cognitive radio (CR) is a novel technology for improving the utilization of radio electromagnetic spectrum. Recently, a new model called cognitive radio networks with cloud (CRNC) has been proposed to eliminate the constraints (pertinent to traditional CR) on power, memory space, and computational capacity. To enable multiple access of CRNC system, orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) has been investigated. Traditional OFDM-IDMA may not be applied to CRNC in a straightforward manner. This is because the spectral nulling in CR and the subsequent spectral sensing mismatch (between CR transmitter and receiver) could lead to unacceptable data error performance. This paper presents a new multiple access communication system, called transform domain communication system IDMA communication system, to deal with spectral nulling problems. Two improved schemes based on BPSK and cyclic code shift keying modulations are proposed. The proposed system structure integrates the advantages of both OFDM-IDMA and CRNC, which can make full use of other users' information while satisfying CR constraints. Simulation results demonstrate that the two proposed system architectures can achieve significant improvement on BER performance, multiple access capability, and anti-interference ability with spectral nulling.

The transmission frequency of power grids, i.e., electric network frequency (ENF), has become a common criterion to authenticate audio recordings during the past decade, drawing much attention from both the academic researchers and law enforcement agencies world widely. The properties of ENF enable forensic applications such as audio evidence timestamp verification and tampering detection. In this paper, based on a general review of existing works, we discuss several important practical problems and facts that have drawn less research attention or have not been formally studied, including ENF detection problems, limitations of the ENF-based tampering detection systems, and the difficulties in the ENF analysis. During ENF detection, the challenges come from not only the noise and the interference, but also the fact that audio recordings without captured ENF can still have signal components in the frequency band of interest (false positive). In ENF-based tampering detection systems, the weakness of commonly used assumptions and the limitations of several existing solutions are discussed. In addition, we reveal that in the most intensively studied ENF-based audio evidence timestamp verification, many works aiming at improving ENF estimation could only produce marginal performance improvement, while the main problems due to noise and interference remain open. All these analysis and discussions are related by a proposed big picture of ENF-based audio authentication systems. After that, we also investigate the strategies to design more reliable audio authentication systems based on the ENF, which consists of a series of research and investigation works.

This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior. The Bayesian inference with the Laplacian models is a challenge because the Laplacian prior is not conjugate to the Gaussian likelihood. To solve this problem, we first introduce the inverse-gamma prior, which is conjugate to the Laplacian prior, to model the distinctive scaling parameters of the Laplacian priors. Then the posterior of the sparse signal, approximated by the Laplace approximation, is found to be Gaussian distributed with the expectation being the result of maximum a posterior (MAP) estimation. Finally the expectation-maximization (EM)-based variational Bayesian (VB) inference is utilized to accomplish the sparse signal recovery with the LSM prior. Since the proposed algorithm is a full Bayesian inference based on the MAP estimation, it achieves both the ability of avoiding structural error from the sparse Bayesian learning and the robustness to noise from the MAP estimation. Analysis on experimental results based on both simulated and measured data indicates that the proposed algorithm achieves the state-of-art performance in terms of sparse representation and de-noising.

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

In this paper, we consider the direction of arrival (DOA) estimation issue of noncircular (NC) source in multiple-input multiple-output (MIMO) radar and propose a novel unitary nuclear norm minimization (UNNM) algorithm. In the proposed method, the noncircular properties of signals are used to double the virtual array aperture, and the real-valued data are obtained by utilizing unitary transformation. Then a real-valued block sparse model is established based on a novel over-complete dictionary, and a UNNM algorithm is formulated for recovering the block-sparse matrix. In addition, the real-valued NC-MUSIC spectrum is used to design a weight matrix for reweighting the nuclear norm minimization to achieve the enhanced sparsity of solutions. Finally, the DOA is estimated by searching the non-zero blocks of the recovered matrix. Because of using the noncircular properties of signals to extend the virtual array aperture and an additional real structure to suppress the noise, the proposed method provides better performance compared with the conventional sparse recovery based algorithms. Furthermore, the proposed method can handle the case of underdetermined DOA estimation. Simulation results show the effectiveness and advantages of the proposed method.

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.