Subaperture processing is a commonly used method for time-domain SAR echo simulation. However, due to the effect of geometry configuration for bistatic SAR, doppler aliasing will appear in the simulated echo data, and then there will be ambiguity in the imaging result. In this paper, by analyzing the causes of ambiguity and introducing the interpolation in range, a modified subaperture echo simulation method for bistatic SAR is proposed. At last, simulation results are illustrated to verify the effectiveness of the method.

Bistatic synthetic aperture radar (BiSAR) has high geometric diversity and can obtain the target information from different observation angles. With the ability of azimuth beam steering for both platforms, different bistatic imaging modes can be implemented to achieve better cooperation of beam footprints for enhanced imaging performance. For the multimode general bistatic SAR with complex trajectory (MGCT-BiSAR), due to the different beam steering methods and the translational-variant geometry, the spatial variance of the Doppler centroid becomes complicated, which leads to a severe azimuth spectrum aliasing problem. Moreover, the complex trajectory of MGCT-BiSAR may introduce high-order range cell migration (RCM) and Doppler parameters, which leads to the 2-D coupling and the different Doppler characteristics for different beam steering modes. The existing imaging algorithm cannot uniformly process the multimode SAR data. This article proposes an improved polar formatting algorithm based on minimum azimuth spectrum width (minASW-PFA) to uniformly process the bistatic SAR data. The proposed method first calculates the unified deramping factors and proposes a minASW bulk deramping method to minimize the azimuth spectrum width of the different imaging modes. However, the process leads to a severe range-Doppler coupling, which cannot be fully eliminated by the traditional polar formatting and wavefront curvature compensation methods. Therefore, an improved polar format mapping and wavefront curvature compensation method are also proposed to solve the coupling problem and achieve high-order spatial variance compensation. Finally, numerical simulations verify the proposed algorithm to achieve unified imaging for multimode general bistatic SAR with complex trajectories.

Highly constrained multiobjective optimization problems (HCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with complex constraints and small feasible regions, which are commonly encountered in many real-world applications. Current constraint-handling techniques will face two difficulties when dealing with HCMOPs: 1) feasible solution is hard to be found and too much search effort is spent in locating the feasible region and 2) since the total feasible region of an HCMOP can consist of several disconnected subregions, the search process might be stuck in the comparatively larger feasible subregion, which does not contain the whole Pareto front (PF). To address these two issues, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm, that is, CRS-DE, is proposed in this article. In each generation, the CRS-DE relaxes the constraints by dividing the infeasible solutions into two subpopulations based on total constraint violation, that is, the "semifeasible" subpopulation (SF) and "infeasible" subpopulation (IF), respectively. The SF provides information on the promising regions of finding the feasible solution and is the driving force for convergence toward the PF, while the IF focuses on global exploration for new promising regions. Corresponding reproduction and selection strategies are devised for the SF, IF, and feasible subpopulations, which create a clear division of labor with cooperation to facilitate the search for feasible solutions. To leverage the influence of CRS and prevent the population from premature convergence, a mobility restriction mechanism is developed to restrict the individuals in the SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population. Comprehensive experiments on a series of benchmark test problems and a real-world CMOP demonstrate the competitiveness of our method compared with other representative algorithms in terms of effectiveness and reliability in finding a set of well-distributed optimal solutions for HCMOPs.

In the beam steering bistatic synthetic aperture radar (BS-BiSAR) system, both the transmitter and receiver beams can be steered in the azimuth direction, offering increased flexibility in mission planning to meet specific imaging requirements. Compared with the current SAR system, the system resources of BS-BiSAR have a high degree of freedom (DOF), including bistatic configuration, beam steering modes, and pulse repetition frequency (PRF), which are closely related to imaging performance. With appropriate beam steering strategy and observation geometry, better cooperation of beam footprints of the platforms can be achieved for enhanced imaging performance. The aim of this paper is to explore the resource management problem (RMP) for BS-BiSAR and optimize the comprehensive system performance, including imaging area, spatial resolution, signal ambiguity and radiometric performance. The RMP is then formulated as a single-objective problem subject to multiple constraints. An improved particle swarm optimization method combined with a sentry learning strategy is proposed to solve the optimization problem under strict constraints and high dimensional solution space. Simulation experiments are conducted to validate the effectiveness of the proposed method for managing system resources and optimizing performance. The result of the resource management method can be applied to guide the system design and achieve the comprehensive performance optimization of BS-BiSAR.

Beam steering is widely applied in the airborne and space-borne SAR system to achieve a balance between large imaging area and high azimuth resolution. However, it leads to a linear variation of Doppler centroid in monostatic SAR, which is more complicated in bistatic SAR due to the contribution built by the transmitter and the receiver and results in Doppler spectrum aliasing. The spatial variance of the Doppler centroid of the beam steering bistatic SAR is analysed. And a modified preprocessing method for azimuth-variant bistatic SAR with beam steering mode is proposed to remove the Doppler spectrum aliasing by effectively increasing the sampling rate of azimuth time. The movement of the platforms is considered with cured trajectory.

The 2-D space variance of echo signals is the key problem of image processing for bistatic synthetic aperture radar (BiSAR) under nonlinear platform trajectories. Although there are some existing studies that present solutions to deal with space variance under linear or low-order motion, this problem is more severe when the trajectories are more complicated and the imaging scene sizes are larger. To deal with this challenging problem, this article proposes a new imaging method based on a novel azimuth-regularized wavenumber mapping and a highly efficient spatially variable phase filter. The novel wavenumber mapping as the major novelty of the method can simultaneously realize range–azimuth decoupling and coarse focusing in the space domain for all the targets. What is more, the phase function of the echo signal in the wavenumber domain is analytically expressed as a binary polynomial by deriving the inverse mapping of the wavenumber with respect to range frequency and azimuth time. Then, the spatially variable filter is designed based on the analytical expression and realized by upgrading the parameters along the azimuth direction. The filtering process has low complexity and can be executed in parallel for every cross-azimuth cell. Moreover, an algorithm for constraining the residual phase error is presented to guarantee both the efficiency and the accuracy of the image processing. Verified by numerical simulations, the proposed imaging method achieves a superior focusing effect than the existing method that is designed for BiSAR with highly maneuvering platforms while having lower computational complexity.

This paper unrolls some preliminary results of a spaceborne-airborne bistatic SAR experiment, conducted in October, 2020 in Zhejiang, China, using GF-3 SAR satellite as the transmitter. Some important aspects of the experiment are firstly introduced, including bistatic acquisition geometry, receiving system, signal synchronization and theoretical spatial resolution. Then, the imaging processing flow is given, with emphasis on the data synchronization. A modified BP imaging method is proposed, which is suitable for direct signal synchronization scheme commonly used in spaceborne-airborne experiments. Finally, the imaging result is given with evaluation of the spatial resolution.

Unmanned ariel vehicle (UAV) can provide superior flexibility and cost-efficiency for modern radar imaging systems, which is an ideal platform for advanced remote sensing applications. In this paper, an energy-efficient passive UAV SAR system is proposed and investigated. The UAV platform passively reuses the backscattered signal from an external illuminator, such as SAR satellite, GNSS or ground-based stationary commercial illuminators, and achieves data communication and bi-static SAR imaging at a ground processing station. The mission concept and system block diagram are first presented with justifications on the advantages of the system. A set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and data communication. Finally, the validity of the proposed performance evaluators are verified by numerical simulations.

An efficient echo simulation algorithm considering topography in time domain for bistatic SAR is proposed in this paper. By using subaperture processing and equivalent scatter, this method can implement efficient echo simulation. At last, the effectiveness of this method is verified by numerical simulation.

Automation‐assisted cervical screening via liquid‐based cytology has achieved great success using segmentation and classification methods. This work tries to do abnormal region detection on field of view cervical cell images based on deep learning, which is a novel way to solve cervical cytological screening problem. Since some abnormal nuclei gather in groups, the proposed method chooses abnormal regions instead of abnormal nuclei as the detection targets in order to locate the abnormal regions for the further diagnosis of the pathologists. In this study, a novel abnormal region detection approach for cervical screening is proposed based on a size‐sensitive fully convolutional network (R‐FCN). Due to the regular feature distribution, a fewer‐layer convolutional neural backbone network is designed for more efficient feature extraction and less running time. In addition, a new measure named hit degree is defined to describe the degree how closely each detected region and the corresponding ground truth matches up. Experimental results show that an average precision of 93.2% is achieved for abnormal region detection in cervical smear images. The proposed method is promising for the development of computer‐aided systems in clinical cervical cytological screening.