The generalized sparse iterative covariance-based estimation ($q$-SPICE) algorithm has significant capability to enhance angular resolution in scanning radar applications. However, two drawbacks remain: one is that it focuses solely on the azimuth dimension and ignores the pitch dimension, and the other is that its computational complexity grows exponentially with increasing data size. To address these, a 2D weighted$q$-SPICE (W$q$-SPICE) is proposed in this letter, which enables rapid azimuth-pitch sparse reconstruction of the scanning radar. The proposed algorithm is an extension of$q$-SPICE within the framework of weighted SPICE (WSPICE), and it is also a generalized version of the recent 2D WSPICE algorithm. Moreover, a selection criterion for$q$is provided. Experiment results based on simulated and real radar data illustrate the superiority of the proposed method over its counterparts in the task of 2D resolution enhancement for the scanning radar.

This article deals with the problem of tracking targets with X-band marine radars in the complicated sea clutter background. By jointly exploiting the target’s kinematic and appearance information, we propose a novel dynamic model-based Doppler-adaptive correlation filter. The proposed tracker mainly consists of two modules. First, a Doppler-adaptive correlation filter is developed based on the kernel correlation filter. This filter can effectively represent the appearance patterns of the target and is adaptive to the motion Doppler by using a multi-frequency centered filter bank. Second, the Bernoulli filter is employed to represent the kinematic patterns of the target. Within this filter, the converted measurement Kalman filter with range rate is applied to overcome the inconsistency between coordinate systems of the motion and measurement models. By exploiting the hybrid measurement likelihood, the two modules are then fused within the Bayesian framework to achieve improved tracking performance in the complicated sea clutter background. Experimental results based on both the simulated and real radar data demonstrate that the proposed tracker outperforms its representative counterparts.

The use of traditional compressed sensing methods in sparse aperture inverse synthetic aperture radar (SA-ISAR) imaging suffers from high computational burdens and complicated formula derivations. To solve these problems, a complex-valued autoencoder (CVAE) architecture trained to efficiently obtain ISAR images from incomplete echoes is proposed in this letter. In addition, a training set is generated for network training via self-supervised learning. Instead of using multiple simulated scenes as a training set, multiple range cells in a simulated scene are used based on two considerations. First, reducing the complexity of the dataset allows for faster and easier training of the network. Second, it is flexible as it does not require retraining when handling scenes with various numbers of range cells. In this way, due to the properties of matrix operations, similar time complexity can be achieved in both single measurement vector (SMV) and multiple measurement vector (MMV) cases. Notably, the network can be trained using only the training set and tested on the simulated electromagnetic (EM) data and the data obtained from an operational radar system. Our experimental results validate the superiority of the proposed method in terms of better performance, better robustness to noise, and higher computational efficiency.

Marine radar target tracking in atrocious sea conditions is often challenging, as the strong sea clutter makes conventional trackers non-robust in the marine environment, where it is very common. To improve the ability to identify targets from background clutter, the background-aware correlation filter (BACF) of the visual tracker is applied to marine radar target tracking. In addition, the BACF is optimized by adding the motion regularization term and the temporal consistency constraint to avoid tracking drift and stabilize filter variations. The experiment results on X-band Marine radar data demonstrate that the proposed algorithm performs favorably.

Recently, a generalized likelihood ratio test (GLRT)-based multipixel target detector for hyperspectral imagery was proposed. With joint exploitation of the pixels occupied by a target of interest, the detection performance was significantly improved. However, it still faces a pixel selection problem in practice. In this paper, we address the pixel selection problem for the multipixel target detector in practice. First, we propose an adaptive target pixel selection method based on spectral similarity and spatial connectivity characteristics. Second, we propose a method to collect the pixels spatially closest to the target pixels as the training background pixels so that their residual background component share the same statistical characteristics with high probability. To exclude potential target pixels in the collected training background pixels, an iterative version of the GLRT-based multipixel target detector is proposed. It is easy to set the key parameters of the proposed method, which is attractive in practice. Experimental results on four real hyperspectral datasets show that the proposed method outperforms its counterparts in terms of detection performance.

In hyperspectral imagery, target detection algorithms are usually based on the spectral signature information. Due to the advance of the spatial resolution of hyperspectral sensors, the ground sample distance may be much smaller than the size of targets. As a result, targets often occupy multiple consecutive pixels, which are referred to as multi-pixel targets. In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. Jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.

As a promising technology with disruptive innovation, user-centric network can meet the data traffic demand and user service demand in the future mobile networks. It completely transforms the traditional cell-centric paradigm into user-centric paradigm, requiring the deployment of a large number of access points (APs). However, the massive deployment of APs leads to issues such as high hardware cost, huge power consumption and complex interference management. Reconfigurable intelligent surfaces (RIS), as another promising technology, can expand signal coverage, suppress interference, reduce hardware cost and power consumption. Inspired by this, we propose a novel RIS assisted user-centric network that uses RIS to replace some low utilization APs to reduce cost as well as power consumption, and deploy more RIS to achieve energy-efficient user-centric communication. In order to take full advantages of RIS, we jointly optimize passive beamforming at RIS and power control at AP to maximize the energy efficiency of the network. Since this problem is intractable and non-convex, an effectively alternating optimization algorithm, capitalizing on fractional programming and successive lower-bound maximization is proposed. The simulation results verify that the proposed algorithm outperforms reference algorithms in terms of energy efficiency and sum rate.

In this letter, a structure filling and matching method is proposed for the 3-D reconstruction of buildings, particularly those with combinational structures, from a single high-resolution synthetic aperture radar (SAR) image. This method consists of two stages. First, the structure model of the investigated building is constructed by filling the building structure elements, i.e., walls and roofs, into an initialized empty scene. Subsequently, a series of simulated SAR images is generated by this structure model and matched with the real SAR image of the building. By finding the maximal mutual information between the simulated and real images, the optimal geometric parameters of the model are obtained, which make the output more accurate. Compared with other methods, this method can reconstruct different kinds of practical buildings and retrieve their exact geometric parameters, using only a single SAR image. The performance of this method is evaluated on real TerraSAR-X images to show its effectiveness.

The micromotion of a target will introduce additional frequency modulations on the radar echoes and lead to a contaminated inverse synthetic aperture radar (ISAR) image of the main body. In this letter, a novel ISAR imaging method is proposed for micromotion targets based on the joint sparsity of the main body spectrogram. Under the sparse Bayesian framework, we assign hierarchical priors to the spectrogram to statistically promote the joint sparsity of the main body signals and eliminate the interference of the micromotion counterparts. To avoid the intractability of calculating the posterior, we utilize the Markov chain Monte Carlo sampling in the Bayesian inference. Based on the approximate posterior, the main body spectrogram is estimated from the samples, and a clear image of the main body can be achieved by cross compression. Imaging results of the echoes generated using both the point-scattering model and the electromagnetic computation have validated the effectiveness of the proposed method.

In this letter, a double-layer fuzzy fusion method is proposed to produce a higher quality image by combing multiple through-wall radar images of the same scene. The first layer is termed as intraimage fusion, in which the global and local degrees of membership are fused within each input image based on intraimage fuzzy rules. Then, in the second layer, interimage fusion is performed between input images using interimage fuzzy rules, and the output image is obtained. Compared with other fusion methods, the proposed method decreases the possibility of losing a weak target and suppresses the clutter by taking into account the local information while enhancing the target intensity values. The method also introduces a K-means clustering technique to automatically select the parameters of the membership functions. The performance of the proposed method is evaluated using both simulated and real experimental data.

Sparse recovery algorithms with application to multiple-input-multiple-output (MIMO) radar imaging could lose their advantage under a phase mismatch among transmitter-receiver pairs. In this letter, we identify that the impact of a random phase mismatch on the imaging problem can come to a scale-down factor on the amplitude of the MIMO point spread function. We thereby establish the conditions of successful support recovery and the performance measure for the orthogonal matching pursuit (OMP) algorithm for the involved problem, both of which are functions of the scale-down factor. Meanwhile, sparse imaging via expectation-maximization (SIEM) is proposed to alleviate OMP performance loss in the face of a phase mismatch. Numerical results corroborate the analysis and illustrate the effectiveness of the SIEM algorithm.

Multiple-input multiple-output (MIMO) radar with colocated antennas is expected to achieve good imaging performance via coherent processing. However, a crucial factor to this process-phase synchronization, which directly determines the performance gain, has rarely been studied in previous works. Hence in this paper, given the sparsity of the target, we address the problem of imaging for colocated MIMO radar under the phase synchronization mismatch. Based on the model assumption that the phase synchronization mismatch in each propagation path is an independent and identically distributed uniform random variable, we combine the sparsity of the target and expectation maximization (EM) method to develop an EM-based sparse imaging algorithm against such random phase mismatch. The effectiveness of the proposed algorithm is demonstrated by numerical simulations.

Distributed passive radar imaging systems based on illuminators of opportunity such as frequency-modulation-based stations exhibit poor imaging performance owing to the narrow bandwidth of the radiated signals and the small number of illuminators. Moreover, the position errors of the illuminators and the receivers would further deteriorate the inversion performance. In this letter, the sparse self-calibration imaging via iterative maximum a posteriori probability method is proposed for simultaneous sparse imaging, self-calibrating, and parameter updating, which exploits the sparse priority of the target. Besides, the convergence and the initialization of the method are discussed. Numerical simulations verify the effectiveness of the proposed method and its analysis.

Multiple-input multiple-output (MIMO) radar is expected to achieve good inversion performance by utilizing space diversity technology. However, traditional imaging methods often fail owing to the practical constraints that the available transmitters and receivers are very few and the number of snapshots is very limited. More seriously, the unavoidable position errors of the transmitters and the receivers would further deteriorate the imaging results. In this paper, by exploiting the sparse priority of the target, the sparse self-calibration by maximum a posterior probability method (SSC-MAP) is proposed to provide high resolution image and realize accurate position calibration at the same time. Numerical simulations verify the effectiveness of the proposed method.