卫星编队分布式MIMO系统关键技术研究

发布时间：2018-04-23 15:23

本文选题：卫星编队 + 分布式MIMO　；参考：《电子科技大学》2014年硕士论文

【摘要】：卫星通信作为当下通信的主要方式之一,在军事应用和航天科技领域具有重要的地位。随着卫星通信事业的发展,各个国家纷纷发射了许多卫星,导致轨道资源和频谱资源越来越珍贵。为了满足日益发展的卫星通信需求,节省研制和发射成本,微小卫星的研制逐渐进入了人们的视野。对微小卫星进行编队除了能使其具有单颗大卫星的功能之外,还具有覆盖更灵活、系统更稳健、更新周期更短、成本更低廉的优点。现有的微小卫星编队方法计算复杂度高,误差会随着时间变大,不适合实际应用。为了方便初期理论分析,本文第二章根据卫星编队飞行时相对运动的轨道参数提出了一种新的编队构型设计方法,能够根据期望的队形简便地计算出绕飞卫星的轨道根数。对卫星进行编队是构成分布式卫星MIMO系统的基础。卫星分布式MIMO系统可以利用编队的分布式构型,为通信信道提供空间宏分集,增大信道容量。本文第三章对MIMO系统的空域相关性和容量进行了分析,得出了影响MIMO系统空间相关性的因素以及MIMO信道相关性对其信道容量的影响。在此基础上研究了极化天线去相关技术对MIMO系统相关性、容量和误码率的改善。最后提出了一种搭载极化天线的卫星分布式MIMO系统的模型,并以此模型为基础,对编队卫星分布式MIMO系统的信道容量和误码率进行了进一步仿真分析。证明利用极化分集的编队卫星分布式MIMO比传统的单天线系统和集中式MIMO系统有更大的信道容量和更好的误码率性能。智能天线技术和分布式MIMO系统相结合,可以进一步增加编队卫星分布式MIMO系统的容量和抗干扰性能。智能天线波束成形技术可以有效控制卫星点波束的指向,从空域上实现干扰消除和信号增强。实现波束成形有LMS算法、RLS算法等。本文第四章先对LMS算法和RLS算法进行了仿真,然后对LMS波束成形算法进行了改进,给出了一种简化的变步长LMS算法。该算法对迭代误差范围进行判断,在某个范围内使用一种步长。使得求解天线元权值向量的迭代过程中不需要频繁地对步长进行求解,从而达到减少算法复杂度,节约硬件资源的目的,符合小卫星硬件资源少、功耗低的特点。经过仿真验证,该算法是有效的。本文第五章是结束语,对全文的工作成果以及不足的地方进行了总结,并计划了下一步的工作。
[Abstract]:As one of the main communication methods, satellite communication plays an important role in military applications and space science and technology. With the development of satellite communications, many countries have launched many satellites, resulting in more and more precious orbital and spectrum resources. In order to meet the increasing demand of satellite communication and save the cost of research and launch, the development of micro satellite has gradually entered the field of vision. The formation of small satellites not only makes them have the function of single large satellite, but also has the advantages of more flexible coverage, more robust system, shorter update period and lower cost. The existing small satellite formation methods have high computational complexity and the error will increase with time, so it is not suitable for practical application. In order to facilitate the initial theoretical analysis, a new formation configuration design method is proposed according to the orbit parameters of the relative motion of satellite formation flying in the second chapter, which can easily calculate the orbital root number of flying satellites according to the expected formation. Satellite formation is the foundation of distributed satellite MIMO system. Satellite distributed MIMO system can use the distributed configuration of formation to provide spatial macro diversity for communication channel and increase channel capacity. In the third chapter, the spatial correlation and capacity of MIMO system are analyzed, and the factors affecting spatial correlation of MIMO system and the influence of MIMO channel correlation on channel capacity are obtained. On this basis, the improvement of correlation, capacity and bit error rate of MIMO system by polarization antenna de-correlation technique is studied. Finally, a model of satellite distributed MIMO system with polarized antenna is proposed. Based on the model, the channel capacity and bit error rate (BER) of distributed MIMO system are simulated and analyzed. It is proved that the distributed MIMO with polarization diversity has greater channel capacity and better bit error rate performance than the traditional single-antenna system and centralized MIMO system. The combination of smart antenna technology and distributed MIMO system can further increase the capacity and anti-jamming performance of formation satellite distributed MIMO system. Smart antenna beamforming technology can effectively control the satellite spot beam pointing, from the airspace interference cancellation and signal enhancement. The realization of beamforming includes LMS algorithm, RLS algorithm and so on. In the fourth chapter, the LMS algorithm and the RLS algorithm are simulated, then the LMS beamforming algorithm is improved, and a simplified variable step size LMS algorithm is presented. The algorithm determines the range of iterative error and uses a step size in a certain range. In order to reduce the complexity of the algorithm and save the hardware resources, it is not necessary to solve the step size frequently in the iterative process of solving the antenna element weight vector, which is in line with the characteristics of small satellite hardware resources and low power consumption. Simulation results show that the algorithm is effective. The fifth chapter is the conclusion, summarizes the work achievements and shortcomings of the paper, and plans the next work.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN927.2

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