无人机编队优化部署:主动被动雷达探测隐形目标策略

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本文主要探讨了在无人机编队(UAV Swarms)中部署空对地雷达系统以探测隐形目标的最优配置分析。作者Weijia Wang、Peng Bai、Xiaolong Liang*、Jiaqiang Zhang和Liping Hu来自中国空军工程大学国家航空碰撞预防重点实验室,位于西安。他们关注的核心问题是,如何通过结合主动和被动雷达技术,提高隐形目标的定向检测性能,同时优化能量消耗,以实现空中雷达网络的高效运行。 隐形目标由于其设计特性,其后向散射截面(RCS)较低,这对常规雷达的探测构成了挑战。文中提出了一种计算隐形目标的二元静态散射区域(Cassinioval sensing region)的方法,通过这种区域来确定多部移动适应性前向接收器与多部地面基站(BRs)的最佳几何布局。这种布局有助于扩展动态探测范围和覆盖区域,提升整体探测能力。 为了实现这一目标,研究者构建了一个多目标优化模型,考虑了诸如雷达的有效探测范围、信号传输效率、飞行器的负载限制、以及环境因素等复杂约束条件。他们采用了网格划分方法和狼群算法(Wolf Pack Algorithm, WPA)进行搜索,寻找在满足所有约束条件下最优化的雷达部署方案。特别地,他们关注的是在保证隐形目标有效探测的同时,如何有效地部署空中无人机,以平衡任务完成的效率和资源的合理利用。 总结来说,这篇研究论文深入研究了在现代战争背景下,利用无人机编队和新型雷达技术应对隐形威胁的关键问题,通过优化理论与算法相结合,寻求在复杂环境中实现雷达网络最佳性能的策略。这不仅对军事应用有重要价值,也为未来的智能无人系统部署提供了理论基础和技术支持。

Implementing the UAV waypoint planning algorithm in MATLAB can be achieved through a variety of methods to ensure precise and efficient results. Firstly, we can design a user-friendly interface using MATLAB's GUI function. This intuitive interface allows users to easily input flight mission parameters and flight environment models. Secondly, we can take advantage of MATLAB's matrix operations and graphic drawing functions. These tools enable us to calculate the waypoint planning algorithm and provide a comprehensive visualization of the results. Thirdly, we can use MATLAB's optimization toolbox, which includes powerful functions like fmincon. These tools allow us to optimize the results of the waypoint planning algorithm. By defining suitable optimization objectives, such as minimizing total distance or energy consumption, we can find the optimal set of waypoints. Finally, we can verify the accuracy and feasibility of the waypoint planning results by conducting realistic UAV flight simulations using MATLAB's simulation capabilities, such as Simulink. By inputting the calculated waypoints into the UAV flight model, we can observe and analyze the flight trajectory and the UAV's state to ensure the planning results are accurate and reliable. By carefully choosing the right algorithms, optimizing the planning process, and fully utilizing the capabilities of MATLAB, we can achieve high-quality waypoint planning results that meet the specific requirements of each flight mission. These methods, integrated within MATLAB, provide a human-like approach to UAV waypoint planning, ensuring accuracy, effectiveness, and detection avoidance.还能检测出来,这个基础上再修改

2023-07-16 上传

class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序,使其能返回所有已经探索过的目标点visited_points的数量,请用代码表示

2023-06-10 上传