摘要
高频雷达在军事及民用应用中具有诸多优良特性,其研制日益受到重视。但是,由于它的工作频率较低(3~30MHz),相应的波长很长,为保障系统信噪比、角度分辨力等技术指标的要求,传统接收阵列的尺寸往往很大。然而,在很多应用场合下,不允许阵列天线占用过大的空间,只能采用小型化的阵列天线。对于小型化的阵列天线,由于其孔径有限,采用常规雷达测角技术对角度的分辨能力受限于瑞利限,阵列达不到同样方向性系数下常规阵列的分辨能力。为解决这一矛盾,必须采用超分辨算法进行波达方向估计。本文针对高频雷达进行了超分辨算法的研究。
鉴于MUSIC算法多方面的优良特性,论文首先从经典的MUSIC超分辨算法入手,从对它的性能分析中发现它在工程应用中的两个瓶颈:一是需要大量的快拍积累,不能对瞬时信号和快速运动信号进行测向;二是计算量巨大,不利于实时计算。本文重点从这两个方面入手优化算法。
针对第一个应用瓶颈,本文研究了一种单次快拍MUSIC算法,这种单次快拍MUSIC算法仅利用接收的一次快拍的M(M为阵元数)个数据,通过对这M个数据做统计处理,来估计阵列数据的协方差矩阵。仿真实验验证了这种算法的有效性。同时,通过与经典MUSIC算法的性能比较,分析出这种单次快拍MUSIC算法具有分辨力差等缺陷,需要对算法进一步优化。
针对第二个应用瓶颈,通过进一步的分析可以看出,MUSIC算法庞大的计算量集中体现在特征值分解和谱搜索这两个环节。为降低谱搜索过程庞大的计算量,本文采用了一种代替谱搜索过程来减少计算量的方法,这就是Root-MUSIC算法。仿真实验表明,在减少计算量的同时,求根MUSIC算法的性能也优于经典的MUSIC算法。为降低特征值分解过程庞大的计算量,本文采用了波束域MUSIC算法,它通过降低协方差矩阵维数来降低特征值分解的计算量。仿真实验表明,在减少计算量的同时,波束域MUSIC算法跟经典MUSIC算法相比,具有更小的分辨信噪比门限,可以用于优化单次快拍MUSIC算法。
最后,为同时解决经典MUSIC算法的两个应用瓶颈,综合各种优化方法,提出了一种波束域单次快拍求根MUSIC算法。通过对某高频雷达实验站实测数据的处理,验证了这种算法的有效性和实用性。
More attentions are paid to the research and development of high frequency Radar due to its’potentiality in military and civil application recently. As its working frequency is relatively low (3~30MHz), the wavelength is long. In order to maintain the performance of radar system, such as SNR (signal-to-noise ratio) and angular resolution, the size of receiving antenna array is always very large. However, in most circumstances, the space of the array antenna is limited. Therefore, compact array antenna is needed. As the size of the compact array is limited and the technique of the conventional angle estimation is restricted by the Rayleigh limitation, the resolution can not come up with the conventional array under the same directional coefficient. In order to solve this problem, super resolution algorithm must be employed in the DOA estimation. In this dissertation, super resolution algorithm in high frequency Radar is researched.
As the excellent performance of the MUSIC algorithm is considered, this dissertation first studies the classic MUSIC super resolution algorithm. From the study, we can get the conclusion that it has two main shortcomings in the project application: firstly, it can not estimate the direction of the instantaneous signals and fast-moving signals as it needs a lot of snapshots; secondly, the computational load is so huge that it is not suitable for real-time computation. In the dissertation, the algorithm will be optimized from the two aspects.
As to the first limitation, the single snapshot MUSIC algorithm is researched, which only uses M datas of the received single snapshot, where M is the number of the element. The covariance matrix of the array data is estimated by statistic processing of the M datas. Computer simulation verifies the validity of the algorithm. But compared with the performance of the conventional MUSIC algorithm, single snapshot has the limitation of low resolution and is needed to be optimized.
As to the second limitation, we can get the conclusion by analysis that the huge computation load is due to the eigenvalue decomposition and spectrum search. In order to reduce the computation load in spectrum search, Root-MUSIC is considered. Simulation result shows that the performance of Root-MUSIC algorithm is better than conventional MUSIC algorithm while the computation load is reduced. In order to reduce the computation load in the eigenvalue decomposition, beam-space MUSIC algorithm is employed which reduces the computation load by reducing the dimension of the covariance matrix. Compared with the conventional MUSIC algorithm, the beam-space MUSIC algorithm can maintain lower SNR threshold while reducing the computation load, and it can be used to optimize single snapshot MUSIC algorithm.
Finally, combining all the optimized methods, the single snapshot Root-MUSIC algorithm based on beam-space is proposed in order to solve the two limitations of MUSIC algorithm simultaneously. The validity and practicability of the algorithm is verified by processing the real datas measured by a high frequency Radar experimental station.
引文
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