相控阵及其MIMO模式下的自适应目标跟踪和波束调度研究

发布时间：2019-03-17 17:30

【摘要】：相控阵雷达与传统机械扫描雷达相比,其波束指向能在微秒间进行捷变。为了使雷达充分发挥系统性能的同时节省雷达系统资源,需要通过自适应目标跟踪与波束驻留调度合理配置雷达的工作参数。本文将针对相控阵雷达的自适应目标跟踪与波束驻留调度问题展开研究,在此基础上将相关理论运用至具有MIMO(Multiple Input Multiple output)模式的新体制雷达。首先,介绍了三种滤波算法,分别为:卡尔曼滤波算法,Singer模型滤波算法以及IMM(Interacting Multiple Model)滤波算法。其中,卡尔曼滤波算法适用于非机动目标跟踪,Singer模型与IMM滤波算法适用于机动目标跟踪。仿真表明,Singer模型跟踪误差比较稳定,适用于跟踪机动特性变化较小的目标。IMM算法适用于机动特性变化大的目标的跟踪,且其跟踪精度较高。其次,对相控阵雷达的自适应目标跟踪进行了研究。首先介绍了三种基于IMM滤波的自适应采样周期算法。为提高机动目标采样周期的选取速度,给出了一种基于目标机动参数估计的快速自适应采样周期跟踪方法。在此基础上,提出一种基于采样周期和发射波形联合控制的自适应目标跟踪算法,同时对采样周期与发射波形自适应选择。两种算法都能有效减小雷达的计算资源消耗。接下来,针对相控阵MIMO模式下的自适应目标跟踪,提出了两种MIMO模式下多目标自适应跟踪算法。其中,基于固定分析时刻的MIMO多目标自适应跟踪方法以固定的间隔确定分析时刻,基于可变分析时刻的MIMO多目标自适应跟踪方法根据目标的运动特性,确定可变的分析时刻间隔。两种方法在每个分析时刻都通过建立优化模型并求解,确定最优的雷达工作参数。仿真表明,与相控阵雷达相比,MIMO模式下的自适应目标跟踪可以有效减少雷达系统资源消耗,并有效提高雷达的时间资源利用效率。最后,对相控阵及其MIMO模式下的波束驻留调度展开了研究。其中,充分考虑了目标威胁程度和脉冲交错技术。仿真表明,通过在波束驻留调度算法中添加目标威胁度,有效提高了算法的执行威胁率。
[Abstract]:Compared with conventional mechanical scanning radar, phased array radar can change beam direction in microseconds. In order to make the radar make full use of the system performance and save the radar system resources, it is necessary to configure the working parameters of the radar reasonably through the adaptive target tracking and beam resident scheduling. In this paper, the adaptive target tracking and beam resident scheduling of phased array radar are studied. On the basis of this, the correlation theory is applied to the new system radar with MIMO (Multiple Input Multiple output) mode. Firstly, three kinds of filtering algorithms are introduced, which are Kalman filtering algorithm, Singer model filtering algorithm and IMM (Interacting Multiple Model) filtering algorithm. Kalman filtering algorithm is suitable for non-maneuvering target tracking, and Singer model and IMM filtering algorithm are suitable for maneuvering target tracking. The simulation results show that the tracking error of Singer model is relatively stable, and it is suitable for tracking targets with little change in maneuvering characteristics. Imm algorithm is suitable for tracking targets with large change in maneuvering characteristics, and its tracking accuracy is high. Secondly, the adaptive target tracking of phased array radar is studied. Firstly, three adaptive sampling period algorithms based on IMM filter are introduced. A fast adaptive sampling period tracking method based on maneuvering parameter estimation is proposed to improve the selection speed of maneuvering target sampling period. On the basis of this, an adaptive target tracking algorithm based on joint control of sampling period and transmitting waveform is proposed, and the adaptive selection of sampling period and transmitting waveform is also proposed. Both algorithms can effectively reduce the computational resource consumption of radar. Then, for adaptive target tracking in phased array MIMO mode, two adaptive tracking algorithms for multi-target in MIMO mode are proposed. Among them, the MIMO multi-target adaptive tracking method based on the fixed analysis time determines the analysis time at a fixed interval, and the MIMO multi-target adaptive tracking method based on the variable analysis time is based on the moving characteristics of the target. Determine variable time intervals for analysis. At each time of analysis, the optimal radar parameters are determined by establishing the optimal model and solving the optimization model. Simulation results show that compared with phased array radar adaptive target tracking in MIMO mode can effectively reduce radar system resource consumption and improve the efficiency of radar time resource utilization. Finally, the beam resident scheduling in phased array and its MIMO mode is studied. Among them, the target threat level and pulse interleaving technology are fully considered. The simulation results show that by adding the target threat to the beam resident scheduling algorithm, the execution threat rate of the algorithm is improved effectively.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN958.92

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