混合MIMO-相控阵雷达的子阵级阵列结构优化

发布时间：2018-06-08 07:53

本文选题：遗传算法 + 阵列优化　；参考：《哈尔滨工业大学》2014年硕士论文

【摘要】：与相控阵雷达相比，多输入多输出(MIMO)雷达发射在采用正交波形时，虽然能够得到波形分集增益，但同时也损失了相干处理增益。针对这一问题，近几年，国外一些学者试图通过结合相控阵雷达和多输入多输出雷达各自的优点形成一种新的雷达体制，即混合MIMO-相控阵雷达。混合MIMO-相控阵雷达就是把发射天线阵元按一定方式划分形成多个子阵，每个子阵发射相互正交的波形，通过设计每个子阵的加权矢量使天线在空间形成波束，并且每个子阵可以构成多输入多输出雷达模型。利用子阵内波形的相关性和子阵间波形的正交性来同时获取相干处理增益和波形分集增益。本文采用发射和接收使用同一平面阵的阵列结构，其发射端不同子阵间发射正交信号而子阵内部发射相干信号，且在发射端和接收端均使用子阵级信号处理方法，从而实现相控阵和多输入多输出雷达的统一。本文主要研究的内容有：混合MIMO-相控阵系统模型的建立，推导出了混合MIMO-相控阵雷达的发射、接收和波形分集波束方向图；混合MIMO-相控阵雷达阵列的编解码方案和子阵划分的约束条件；子阵优化的目标函数和适应度函数；利用多目标遗传算法进行子阵结构优化设计。混合MIMO-相控阵系统模型的建立在相控阵和MIMO雷达的基础之上的,我们从发射信号-目标回波-接收信号处理这一流程分别介绍传统的相控阵和MIMO雷达，然后推导二维子阵级MIMO-相控阵混合系统的信号处理流程的模型。编解码方案，采用染色体编码，使平面阵列结构编码成一组供多目标进化算法处理的向量形式，并且为了使阵列结构相对集中设置了约束条件：阵元不重叠准则和满布准则等。本文设置了多个目标函数，它们体现了波形分集性能、相干处理增益性能、发射/接收方向图的性能指标，利用适应度函数对目标函数值进行处理，，适应度函数设计为要解决问题的函数的负值。多目标遗传算法进行子阵结构优化设计，本文采用两种多目标遗传算法：Pareto秩和系数加权法，分别对目标函数进行优化，比较两种方法的优化情况，最后得到阵列子阵划分均匀相对集中的最优阵列结构。
[Abstract]:Compared with phased array radar, multi-input and multi-output MIMO-radar transmit with orthogonal waveform, although it can obtain waveform diversity gain, but it also loses coherent processing gain. To solve this problem, in recent years, some foreign scholars have tried to form a new radar system by combining the advantages of phased array radar and multi-input multi-output radar. That is, hybrid MIMO-phased array radar. The hybrid MIMO-phased array radar is to divide the antenna array elements into several sub-arrays in a certain way, and each sub-array transmits orthogonal waveforms. By designing the weighted vector of each subarray, the antenna is beam-forming in space, and each subarray can form a multi-input and multi-output radar model. The coherent processing gain and waveform diversity gain are obtained simultaneously by using the correlation of waveforms in subarrays and the orthogonality of waveforms between subarrays. The transmitter transmits orthogonal signals between different subarrays and transmits coherent signals inside the subarrays, and the sub-array level signal processing method is used in both the transmitter and receiver. In order to achieve the unity of phased array and multi-input multi-output radar, the main contents of this paper are as follows: the establishment of hybrid MIMO-phased array system model, the derivation of transmission, reception and waveform diversity beam pattern of hybrid MIMO-phased array radar; The coding and decoding scheme of hybrid MIMO-phased array radar array and the constraint condition of subarray partition, the objective function and fitness function of subarray optimization; Multi-objective genetic algorithm is used to optimize the subarray structure. The hybrid MIMO-phased array system model is based on the phased array and MIMO radar. We introduce the traditional phased array and MIMO radar separately from the process of transmitting signal, target echo and receiving signal. Then we derive the model of signal processing flow of MIMO-phased array hybrid system. Using chromosome coding, the planar array structure is encoded into a set of vector forms for multi-objective evolutionary algorithms. In order to make the array structure relatively centralized, the constraint conditions are set up, such as the nonoverlapping criterion and the full spread criterion, etc. In this paper, several objective functions are set up, which embody the waveform diversity performance, coherent processing gain performance, and so on. The performance index of transmit / receive pattern is processed by fitness function. The fitness function is designed as the negative value of the function to solve the problem. The multi-objective genetic algorithm is used to optimize the subarray structure. In this paper, two kinds of multi-objective genetic algorithm: Pareto rank sum coefficient weighting method are used to optimize the objective function, and the optimization of the two methods is compared. Finally, the optimal array structure with uniform relative concentration is obtained.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN958.92

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