In this letter, a method is proposed to suppress the interrupted sampling repeater jamming (ISRJ) by jointly designing the radar waveform and mismatch filter. Firstly, the joint design problem under multiple constraints is formulated with the optimization criterion of minimizing the integrated sidelobe levels (ISLs) of waveform mismatch filter output and integrated levels (ILs) of ISRJ signal mismatch filter output. An iterative algorithm is proposed to optimize the waveform and mismatch filter using Lagrange multiplier method and alternating direction multiplier method (ADMM), respectively. Simulation results show that the proposed method achieve good correlation performance between the waveform and mismatch filter, and demonstrate the effectiveness of anti-ISRJ.

The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the synthetic aperture radar (SAR) image quality. The anti-ISRJ methods are studied in this work in order to suppress the ISRJ based on the waveform and filter design for SAR. First, the relationship between the ISRJ and waveform is obtained by analyzing the principle of the ISRJ using the ambiguity function. The ISRJ produces multiple false targets based on the high Doppler tolerance of waveform and the characteristic of the matched filter. Next, a method is proposed to counter the ISRJ by transmitting a phase-coded (PC) waveform with low Doppler tolerance and designing the corresponding mismatched filter. The joint design method is then developed to improve the anti-ISRJ and imaging performance. In the proposed methods, the majorization minimization framework is introduced to solve the nonconvex waveform and filter design problem. Finally, several simulations are conducted to demonstrate the effectiveness of the proposed methods. Simulation results show that the joint design method shows better anti-ISRJ and imaging performance in comparison with the separate design method, but it is more sensitive to the ISRJ sampling duty ratio and period.

A joint waveform and filter design method is developed for suppressing radar pulse compression sidelobe level in this paper. The problem is formulated as the minimization of the integrated sidelobe level (ISL) under the constraint of waveform constant modular and filter energy. To control the loss-in-processing gain (LPG), an additional function is introduced to constrain the peak level based on penalty function method. The joint design algorithm is then derived based on the alternating minimization and majorization minimization (MM) schemes. The computation complexity is analyzed and the convergence analysis verifies that the algorithm can converge to a critical point. Specifically, the proposed method is extended to the waveform and filter design for suppressing the peak sidelobe level (PSL). Numerical simulations are carried out to analyze the key parameters, which demonstrate the feasibility of the proposed method. Simulation results show that the ISL and PSL can be significantly reduced with small LPG. Moreover, the proposed method exhibits faster running speed than the existing one and it thus can be applied to longer sequence designs.

Ray-based radius estimations of subsurface cylindrical objects such as rebars and pipes from ground-penetrating-radar (GPR) measurements are not accurate because of their approximations. We have developed a novel full-waveform inversion (FWI) approach that uses a full-waveform 3D finite-difference time-domain (FDTD) forward-modeling program to estimate the radius including other object parameters. By using the full waveform of the common-offset GPR data, the shuffled complex evolution (SCE) approach is able to reliably extract the radius of the subsurface cylindrical objects. A combined optimization of radius, medium properties, and the effective source wavelet is necessary. Synthetic and experimental data inversion returns an accurate reconstruction of the cylinder properties, medium properties, and the effective source wavelet. Combining FWI of GPR data using SCE and a 3D FDTD forward model makes the approach easily adaptable for a wide range of other GPR FWI approaches.

In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method.

This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use.