In recent years, the synthetic aperture radar tomography (TomoSAR) technique is becoming an increasingly hot research topic in the three-dimensional (3D) reconstruction of the structure of urban buildings. The robustness of the many advanced non-parametric spectral estimation methods in use is based on a high number of observations. This letter aims to evaluate the ability of the five non-parametric estimator techniques, including propagator, generalized maximum entropy (GME), minimum norm (MN), singular value decomposition (SVD), and capon in the separation of scattering contributions with a small number of SAR images. The performance comparison of the employed algorithms is carried out on the simulated and real datasets. The study results show that the novel high-resolution propagator and generalized maximum entropy estimators, respectively, based on the partitioning and extrapolation of the covariance matrix, can discriminate the number of scatterers in one azimuth-range resolution and satisfactorily prevent the appearance of undesirable sidelobes. The experimental results on real SAR images acquired by the TerraSAR-X satellite show the proposed propagator spectral estimation can significantly improve the 3D building reconstruction in the urban environment. Also, the estimated height of the scatters using the propagator technique is similar to the data obtained from ground observations.

Multiple‐input multiple‐output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors’ goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS‐MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS‐MIMO radar in the case of multiple targets, suitable for such purpose; Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

In this letter, array null steering is formulated as a sparse recovery problem. In addition, a novel null steering scheme for large arrays by perturbing only a few elements is presented. To achieve this goal, compressed sensing (CS) is used to exploit the sparsity of the perturbed elements. The advantages of the proposed scheme are a significant reduction in hardware cost and lower power consumption, as well as less aging of the elements and faster response. Simulation results show that the CS-based method could be efficiently used to generate wide nulls using at least two elements. The interference rejection ratio achieved by the proposed method is 10-20 dB better than the existing solutions. The performance of different null generation methods in terms of peak-to-sidelobe ratio, pointing error, and beam-width is compared as well. The proposed algorithm is a prominent solution for the future 5G base stations, where a fast and low-cost beam shaping algorithm is required.

In this paper, we consider an exponentially windowed sample covariance matrix (EWSCM) and propose an improved estimator for its eigenvalues. We use new advances in random matrix theory, which describe the limiting spectral distribution of the large dimensional doubly correlated Wishart matrices to find the support and distribution of the eigenvalues of the EWSCM. We then employ the complex integration and residue theorem to design an estimator for the eigenvalues, which satisfies the cluster separability condition, assuming that the eigenvalue multiplicities are known. We show that the proposed estimator is consistent in the asymptotic regime and has good performance in finite sample size situations. Simulation results show that the proposed estimator outperforms the traditional estimator, significantly.

In this paper, we consider the problem of transmit code design to optimize the detection probability of an extended target embedded in Gaussian signal-dependent interference. We model the target with a priori known target impulse response (TIR) structure along with an unknown reflection factor. As is known, the performance of the generalized likelihood ratio test (GLRT) detector, for the case at hand, monotonically increases with the signal-to-interference-plus-noise ratio (SINR). Hence, we deal with the code design problem via maximizing the SINR. We also enforce a peak-to-average power ratio (PAR) constraint on the sought code. We devise a cyclic method to tackle the highly non-convex design problem. Numerical results highlight the effectiveness of the proposed method to improve the detection performance of the system.

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal‐to‐noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace‐based beamformer. In this paper, a new signal‐plus‐interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal‐to‐interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms.

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. The objective of the present study is to propose a new adaptive beamformer which is robust against direction‐of‐arrival (DOA) mismatch and its convergence rate is not sensitive to the presence of the DS. This method is applicable to the arrays with specific structure such as the linear array. Our approach is based on the DS elimination from the training snapshots and the sub‐array beamforming technique. To accomplish this goal, a blocking matrix which converts the primary data to the DS‐free data is synthesized. The Synthesis process is based on the desired degree of freedom and the uncertainty of DOA of the DS. Using the signal‐free data, the beamforming vector is calculated through the presented algorithm. Owing to elimination of the DS from the training snapshots and performing the adaptive operations in the sub‐array level, our algorithm has high convergence rate and excellent performance even in cases with the small sample size. Simulations show that the proposed beamformer can achieve a much better performance in terms of output SINR compared to the existing ones.

Using a second receiver antenna close to the main transceiver antenna of inverse synthetic aperture radar (ISAR), it is possible to find 3-D positions of target scattering points. Such system is called bistatic, monopulse, or interferometric ISAR (InISAR). In the conventional model of ISAR, the unknown flying object should have a linear trajectory, and only small deviations from this trajectory can be compensated. Target motions which are highly nonlinear or curvy cannot be used in the conventional model. In this paper, we propose a new model for InISAR to process all collected data from the target, regardless of the form of the flight path. More accuracy is achieved for 3-D positioning of the target scattering points by this model because all parts of the flight path contribute in it. We will show the effectiveness of the proposed model in a nonlinear flight path by simulation.

In this paper, we investigate the effect of applying an exponential window on the limiting spectral distribution (l.s.d.) of the exponentially windowed sample covariance matrix (SCM) of complex array data. We use recent advances in random matrix theory which describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We derive an explicit expression for the l.s.d. of the noise-only data. Simulations are performed to support our theoretical claims.

This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko–Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces. Numerical simulations demonstrate that the proposed method estimates the true number of signals for large arrays and a relatively small number of snapshots. In particular, the proposed method requires less number of samples to achieve the same correct enumeration probability compared to the state-of-the-art methods. The authors evaluated the assumed pdf in order to justify the limitation and the behaviour of the proposed method for small number of snapshots and array sizes.