非正侧视下机载雷达杂波补偿方法研究

发布时间：2018-05-29 03:17

本文选题：空时自适应处理 + 非正侧视　；参考：《南京邮电大学》2017年硕士论文

【摘要】：非正侧视下机载阵列雷达的杂波分布具有严重的距离依赖性,不满足独立同分布的条件。而且,在实际应用环境中,具有空时二维结构的机载雷达系统自由度很高,满足独立同分布的训练样本数目严重不足。此时,准确估计待检测距离单元的杂波协方差矩阵较为困难,传统的空时自适应处理(Space-Time Adaptive Processing,STAP)算法的杂波抑制效果严重下降。为解决上述问题,本文对非正侧视下机载雷达的杂波抑制算法进行研究。首先,针对非正侧视下的杂波距离依赖性问题,本文在配准补偿算法的基础上,提出了一种基于自适应平衡加载的配准补偿新方法。在重构数据和配准补偿的过程中,分别进行自适应平衡加载,提高对重构数据和待检测距离单元杂波协方差矩阵估计的准确性,并通过仿真验证了算法的有效性。其次,在非正侧视环境下,杂波分布具有严重的非均匀性,现有算法常常面临着训练样本不足的问题,导致杂波协方差矩阵的估计误差较大。针对这个问题,本文首先提出了最远配准补偿算法,将最远检测距离单元处作为参考单元进行配准,有效补偿了近程杂波的不平稳性。为了进一步解决样本不足的问题,提出一种基于最远配准补偿的空时自回归滤波方法。仿真实验表明,该方法在在训练样本数目较少的情况下,主瓣区性能得到明显改善,具有较好的杂波抑制性能。
[Abstract]:The clutter distribution of an airborne array radar is highly range-dependent and does not satisfy the condition of independent co-distribution. Moreover, in the practical application environment, the degree of freedom of the airborne radar system with space-time two-dimensional structure is very high, and the number of training samples satisfying the independent and same distribution is seriously insufficient. At this point, it is difficult to estimate the clutter covariance matrix of the distance unit to be detected accurately, and the traditional space-time adaptive processing algorithm of Space-Time Adaptive processing STAP (Space-Time Adaptive processing) algorithm has a serious decline in clutter suppression effect. In order to solve the above problem, the clutter suppression algorithm of airborne radar with non-positive side-looking is studied in this paper. Firstly, a new registration compensation method based on adaptive balanced loading is proposed based on the registration compensation algorithm. In the process of data reconstruction and registration compensation, adaptive balanced loading is carried out to improve the accuracy of estimation of clutter covariance matrix for reconstructed data and distance unit to be detected, and the validity of the algorithm is verified by simulation. Secondly, the clutter distribution has serious inhomogeneity in the non-positive side-looking environment. The existing algorithms often face the problem of insufficient training samples, resulting in a large estimation error of the clutter covariance matrix. In order to solve this problem, the furthest registration compensation algorithm is proposed in this paper. The furthest detection distance unit is used as the reference unit for registration, which effectively compensates the uneven stability of short-range clutter. In order to solve the problem of shortage of samples, a space-time autoregressive filtering method based on furthest registration compensation is proposed. Simulation results show that the performance of the main lobe region is improved obviously and the performance of clutter suppression is better.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN959.73

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