Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

Swarm unmanned aerial vehicle synthetic aperture radar (UAV SAR) system leverages multiple UAVs to form a formation, overcoming the limitations of a single platform and enabling the execution of advanced SAR missions. By forming a uniform linear array formation, the swarm UAV SAR system is able to coherently enhance resolution in one direction. Extend to 2-D cases, a uniform planar array needs to be formed for resolution enhancement. However, the requirement for a large number of UAVs to form the planar array can lead to significant costs. In addition, the performance of resolution enhancement is intricately tied to the chosen system formation. Therefore, there is a pressing need to conduct research on methods to obtain the optimal formation. In this article, a hybrid resolution enhancement (HRE) framework has been proposed for the swarm UAV SAR system to optimize resolution enhancement performance while mitigating costs. The proposed framework is mainly divided into two stages: cost-effective formation strategy and optimal HRE. The cost-effective formation strategy, which lays down the foundation for resolution enhancement, is comprised of three steps. First, to achieve HRE with a reduced number of UAVs, a cross-shape formation structure is established. Second, to effectively optimize the position and velocity of the central node of the UAV swarm for optimal resolution performance, a constrained differential evolution (DE)-nondominated sorting (CDE-NS) algorithm is proposed. Third, baseline design is conducted to determine the attached nodes’ positions for optimal coherent resolution enhancement (CRE). After the ideal formation is obtained, optimal HRE can be accomplished. Specifically, the principle of CRE is explained. The inspiration, motivation, and novelty of the proposed noncoherent resolution enhancement method named minimum combination (MC) are elucidated. Simulation results have demonstrated the validation of the proposed framework.

In the application of vehicular MIMO (Multiple Input Multiple Output) radar, it is particularly important to measure and associate the position and velocity of targets. However, traditional methods like 3D-FFT have low angular resolution, and velocity ambiguity resolution is required. Combined with Super-resolution DOA (Direction Of Arrival) algorithm, we propose a multi-dimensional information association method based on target tracking, which uses the labeled GM-PHD (Gaussian Mixture Probability Hypothesis Density) algorithm. This method simultaneously completes the association task about position and velocity and the tracking task, and does not require an additional step on velocity ambiguity resolution. Finally, we use experimental data to verify the effectiveness of the algorithm.

Multistatic synthetic aperture radar (SAR) can obtain abundant information from different angles for terrain classification and tomography. However, multistatic SAR systems, particularly the multistatic global navigation satellite systems (GNSS) face the problem of insufficient resolution. To address this issue, a novel noncoherent combination method termed as minimum combination (MC) is proposed. Inspired by dual apodization, MC is specifically designed to improve the resolution of the multistatic SAR system. Relative to the conventional noncoherent addition (NA) method, MC yields an image with higher resolution and reduced sidelobe level. Simulation results are presented to illustrate the effectiveness and superiority of the proposed approach.

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 our earlier study, an energy-efficient passive UAV radar imaging system was formulated, which comprehensively analyzed the system performance. In this article, based on the evaluator set, a mission planning framework for the underlying energy-efficient passive UAV radar imaging system is proposed to achieve optimized mission performance for a given remote sensing task. First, the mission planning problem is defined in the context of the proposed synthetic aperture radar (SAR) system and a general framework is outlined, including mission specification, illuminator selection, and path planning. It is found that the performance of the system is highly dependent upon the flight path adopted by the UAV platform in a 3-D terrain environment, which offers the potential of optimizing the mission performance by adjusting the UAV path. Then, the path planning problem is modeled as a single-objective optimization problem with multiple constraints. Path planning can be divided into two substages based on different mission orientations and low mutual correlation. Based on this property, a path planning method, called substage division collaborative search (Sub-DiCoS), is proposed. The problem is divided into two subproblems with the corresponding decision space and subpopulation, which significantly relax the constraints for each subproblem and facilitates the search for feasible solutions. Then, differential evolution and the whole-stage best guidance technique are devised to cooperatively lead the subpopulations to search for the best solution. Finally, simulations are presented to demonstrate the effectiveness of the proposed Sub-DiCoS method. The result of the mission planning method can be used to guide the UAV platform to safely travel through a 3-D rough terrain in an energy-efficient manner and achieve optimized SAR imaging and communication performance during the flight.

Multistatic synthetic aperture radar (SAR) can realize 3D imaging of observation scenes by single navigation using distributed aperture. Meanwhile, due to the flexible baseline configuration of unmanned aerial vehicle (UAV), and has broad application prospects in remote sensing, surveying and mapping fields. However, the 3D reconstruction performance of multistatic SAR is closely related to its multistatic baseline. In this paper, a multistatic baseline design method is proposed to achieve optimal 3D reconstruction performance. Firstly, the quantitative relationship model between multistatic baseline and 3D imaging measurement matrix is established, and the cross-correlation value of measurement matrix is introduced as the evaluation index of reconstruction performance. Then, the multistatic baseline design problem is modeled as an optimization problem with an optimal crossrelation number. Finally, the differential evolution algorithm is used to obtain the multistatic baseline of the optimal design. Simulation results show that compared with the unoptimized multistatic baseline, the optimized multistatic baseline can obtain better reconstruction performance when the typical sparse recovery method is used for 3D imaging.

The unmanned aerial vehicle (UAV) is a low-cost and high-efficiency lightweight synthetic aperture radar (SAR)-mounted platform that can be used for a variety of military and civilian missions. Using multiple UAVs to form a swarm can break through the limitations of a single platform and has broad application prospects. In this article, swarm UAV SAR that contains tens or hundreds of UAV platforms is proposed for the first time. The concept and advantages of swarm UAV SAR are investigated, and the mission outlook is given. Afterward, the swarm UAV 3-D linear array SAR (LASAR) is illustrated, which enables high-resolution 3-D imaging in a single flight. Since the antenna array of the swarm UAV 3-D LASAR is sparse, the compressed sensing (CS) algorithm is applied, whose reconstruction performance is closely related to the correlation coefficient of the sensing matrix. Hence, the signal model of swarm UAV 3-D LASAR is derived, and the expression of the sensing matrix is deduced. The sensing matrix design in this article aims at obtaining satisfactory reconstruction performance by optimizing the distribution of the antenna elements, which directly influences the correlation coefficient of the sensing matrix. Considering the limitation of the practical conditions, the sensing matrix design problem is modeled as a constrained integer programming problem. Finally, a sensing matrix design method based on discrete constrained differential evolution (DCDE) algorithm is proposed to solve the optimization problem. Experimental results demonstrate the effectiveness and superiority of the proposed method.