This article presents a practical synthesized sliding-mode control (SSMC) scheme and, then, applies it to quadrotor unmanned aerial vehicle (UAV) systems. A new adaptive law is developed to be suitable for real applications with small moment of inertia such as quadrotor UAV systems that become more sensitive to abrupt and external disturbances. Since it is designed to respond to both sliding variables and time-delayed estimation errors, it makes the switching gains provide remarkably fast convergence rate and high robustness while compensating for abrupt and external disturbances. Besides, the proposed SSMC scheme works together with the pole-placement control to enhance the system stability while yielding the desirable closed-loop dominant poles. Also, the proposed control scheme employs one-sample delayed information to cancel out the system uncertainties and disturbances so that it makes a simple and compact structure. It is shown that attitude trajectory tracking errors of the proposed SSMC scheme are guaranteed to be bounded by the bounded-input bounded-output stability theory. The practical effectiveness and feasibility of the proposed control scheme are illustrated in real implementation with a quadrotor UAV system, which are compared with those of the existing control schemes.

In synthetic aperture radar (SAR) imaging, perturbations in the motion of the moving platform induce an undesired phase error due to imprecise knowledge of the motion, which results in the significant degradations in the quality of SAR images. In this paper, we present an efficient compressive sensing (CS)-based SAR imaging integrated with autofocus technique. The novel approach is based on an estimation-theoretic l ₁ -norm-based approach coupled with Tikhonov-type regularization which combines an observation model of the SAR image formation process with the CS reconstruction problem of the SAR image regarding the sparsity. The optimization problem derived by considering spatially variant phase errors along azimuth domain and the dataset sampled at low rates can be effectively addressed by an efficient iterative method, wherein each iteration both SAR image formation and phase error correction are simultaneously carried out. The simulations and experimental results are presented to validate the effectiveness of the proposed method in terms of reliable image recovery and efficient computational complexity.

In this paper, a synthetic aperture radar (SAR) change detection approach is proposed based on a structural similarity index measure (SSIM) and multiple-window processing (MWP). The proposed scheme is performed in two steps: (1) generation of a coherence image based on MWP associated with SSIM and (2) gamma correction (GC) filtering. The proposed method is capable of providing a high-quality coherence image because the MWP operation based on SSIM has high sensitivity to the similarity measure for intensity between two SAR images. By finding an optimum value of order of GC, the proposed method can considerably reduce the effect of speckle noise on the coherence image, while retaining nearly all the information related to changed region involved in the change detection map. Several experimental results are presented to demonstrate the effectiveness of the proposed scheme.

Interferometric synthetic aperture radar (InSAR) denosing is an essential processing step in deformation measurement and topography reconstruction. A noisy InSAR phase image gives rise to the phase unwrapping difficulties and even results in the degradation on various final products of InSAR. To address this issue, we develop a compressive sensing (CS)-based InSAR phase denoising technique in this paper. Since the spectrum of InSAR phase image is usually sparse in two-dimensional frequency domain, the estimation of sensing dictionary matrix of linear system between the InSAR phase signal and its spectrum in the pursuit of sparsity is considered for InSAR phase denoising. The optimization problem derived by signal parameterization approach is effectively carried out by estimating the basis function that is closely analogous to the strongest signal component in the spectrum of InSAR phase image. The proposed method is effectively capable of eliminating noise and preserving detailed fringe information of InSAR. In the end, simulations and experimental results demonstrate that the proposed scheme outperforms other conventional InSAR phase denoising methods.

In this paper, a novel approach to compressive sensing (CS) based synthetic aperture radar (SAR) focusing is presented based on an approximate version of an omega-K algorithm (AωA) without the Stolt interpolation. The optimal AωA operator for SAR focusing, based on the CS framework, is formulated as a linear system that converts the received raw data into the SAR image. The entire process of the proposed method can be efficiently carried out using a rapid matrix–vector multiplication operations. The image reconstruction process is implemented two-dimensionally, covering simultaneously both the range and azimuth domains from a small number of the raw data. Several experimental results using both simulated and measured data show that proposed method is effective in creating SAR images from subsampled data.