分布式无人机雷达系统资源管理研究

发布时间：2018-05-21 19:52

本文选题：分布式无人机雷达 + 波形设计　；参考：《南京航空航天大学》2017年硕士论文

【摘要】：面对日益复杂的电磁环境和多样化的任务,单无人机雷达已满足不了系统越来越高的性能需求。分布式无人机雷达系统不仅具有更高的探测、跟踪和定位能力,还具有更强的反隐身和抗干扰能力。分布式无人机雷达系统的资源有限,在保证达到系统性能指标的前提下,有效地对雷达系统的资源进行合理分配,对进一步提高系统的资源利用率具有重要意义。本文基于分布式无人机雷达系统的协同感知和态势共享获得的环境、目标、噪声和杂波的先验信息,以建立不同约束条件下的资源优化模型,选用合适的优化算法求解优化问题,仿真验证算法的可行性为基本研究思路,对发射波形设计、目标定位时的功率分配、系统重构时的子集选择三个方面涉及的相关资源展开了基础性的研究,并利用合适的模糊规划模型和模糊优化算法对某些含有模糊参数的目标函数进行建模、求解和分析。本文的主要工作和创新点如下:1.以线性调频(Linear Frequency Modulation,LFM)信号为基础,研究了无人机雷达在非高斯杂波环境下的波形设计问题。首先建立了带宽和发射能量约束下,以最大化信杂噪比(Signal-to-Clutter-plus-Noise Ratio,SCNR)为目标函数的优化模型;然后利用非线性规划遗传算法(Nonlinear Programming Genetic Algorithm,NPGA)求解目标函数,得到了最优发射波形;最后仿真验证了NPGA相比内点法具有更快的收敛性;且在相同的约束条件下,优化后的波形相比频率扩展LFM信号具有更好的SCNR性能。2.研究了基于目标定位的功率分配问题。一方面研究了在目标定位误差一定时,最小化系统的发射功率;另一方面研究了在系统总功率一定时,最小化目标的定位误差。在对现有功率分配策略进行适当改进的基础上,实现了最小化系统发射功率或最小化目标定位误差的目的。3.考虑目标雷达截面积(Radar Cross Section,RCS)的模糊性,首先建立了基于模糊机会约束规划(Fuzzy Chanced-Constrained Programming,FCCP)的最小化系统总功率的分配策略;然后利用混合智能算法求得了FCCP问题的最优解;最后通过系列仿真,验证了FCCP模型在研究含有模糊变量的优化问题时相比不考虑模糊的情况,前者具有更好的稳定性和更高的参考价值。4.研究了无人机雷达系统的子集选择问题。一方面选择最少数目的雷达以达到给定的目标定位误差,同时考虑雷达成本因子的模糊性,建立了基于模糊线性规划(Fuzzy Linear Programing,FLP)的最小子集选择策略;另一方面选择给定数目的子集雷达以使目标的定位误差最小。仿真结果验证了两种情况下子集选择策略的有效性,并得出在对雷达成本因子的先验信息了解较少时,基于FLP模型的最小子集选择策略相比不考虑模糊的情况,前者具有更好稳健性的结论。
[Abstract]:In the face of increasingly complex electromagnetic environment and diversified tasks, the single UAV radar can not meet the higher performance requirements of the system. The distributed UAV radar system not only has higher detection, tracking and positioning capabilities, but also has a stronger anti stealth and anti-jamming capability. The resources of distributed UAV radar system are limited, On the premise of ensuring the performance of the system, it is of great significance to effectively allocate the resources of the radar system to further improve the resource utilization of the system. This paper is based on the environment, the target, the noise and the priori information obtained from the cooperative perception and the situation sharing of the distributed UAV radar system, in order to establish the difference. The resource optimization model under the constraint conditions, the selection of the appropriate optimization algorithm to solve the optimization problem, the feasibility of the simulation verification algorithm is the basic research idea, and the basic research on the related resources involved in the three aspects of the emission waveform design, the power allocation of the target location and the selection of the subset selection in the system reconfiguration is carried out, and the appropriate use is used. Fuzzy programming model and fuzzy optimization algorithm are used to model, solve and analyze some target functions with fuzzy parameters. The main work and innovation points of this paper are as follows: 1. based on Linear Frequency Modulation (LFM) signal, the design of unmanned aerial radar (UAV) radar in the non Gauss clutter environment is studied. Under the constraints of bandwidth and emission energy, the optimal model of the target function is the maximum signal to noise ratio (Signal-to-Clutter-plus-Noise Ratio, SCNR). Then the optimal launch waveform is obtained by using the nonlinear programming genetic algorithm (Nonlinear Programming Genetic Algorithm, NPGA) to obtain the optimal launch waveform. Finally, the simulation proves that NPGA is compared to the internal. The point method has faster convergence, and under the same constraints, the optimized waveform has a better SCNR performance than the frequency extended LFM signal..2. studies the power allocation problem based on the target location. On the one hand, the paper studies the transmission power of the system when the target location error is certain and the other is the system general. When the power is fixed, the positioning error of the target is minimized. On the basis of the appropriate improvement of the existing power allocation strategy, the purpose of minimizing the transmission power of the system or minimizing the target location error in.3. is to consider the ambiguity of the target radar cross section area (Radar Cross Section, RCS). First, a fuzzy chance constrained programming (Fu) is established. ZZY Chanced-Constrained Programming, FCCP) minimizes the allocation strategy of the total power of the system, and then uses the hybrid intelligent algorithm to obtain the optimal solution of the FCCP problem. Finally, through a series of simulation, it is proved that the FCCP model has a better stability and better stability when it studies the optimization problem with fuzzy variables. The higher reference value.4. studies the subset selection problem of the UAV radar system. On the one hand, we select the least target radar to achieve the given target location error and consider the fuzziness of the radar cost factor, and establish the minimum subset selection strategy based on the fuzzy linear programming (Fuzzy Linear Programing, FLP); on the other hand, the selection strategy is selected. A given number of subsets radar is selected to minimize the location error of the target. The simulation results verify the effectiveness of the two subsets selection strategy, and draw a conclusion that the minimum subset selection strategy based on the FLP model is better than the fuzzy case when the prior information of the radar cost factor is less, and the former has better robustness. Conclusion.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN959

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