机载相控阵雷达KA-STAP技术研究
发布时间：2018-10-13 12:14
【摘要】：空时自适应处理(STAP)是机载相控阵雷达进行动目标检测的关键技术。在非均匀杂波环境下,缺乏足够多的独立同分布(IID)训练样本估计杂波的统计特性,导致自适应性能严重下降,这是STAP处理所面临的最大问题。因此,发展能够适应非均匀环境的新型STAP算法已经成为STAP技术的研究方向之一。将检测环境的先验知识合理地运用在雷达信号处理中,实现知识辅助的空时自适应处理(KA-STAP)目前成为解决杂波非均匀问题和改善STAP性能的研究热点。此外,运算量巨大是STAP面临的另一问题。为此,本论文的研究将围绕KA-STAP技术及其数值域求解算法展开,主要包括以下几个方面:1.研究了探测环境的先验杂波协方差估计问题。利用真实场景的地形地貌、数字高程模型(DEM)数据、雷达系统参数、载机平台运动参数等先验信息,准确估计待检测单元的先验杂波模型。2.提出了一种知识辅助的自适应功率剩余(KA-APR)非均匀样本检测算法。该算法间接利用杂波先验知识对STAP训练样本进行非均匀检测,比常规检测方法具有更好的检测性能。根据提出的KA-APR检测算法判断待测采样数据的类型(均匀或非均匀),研究了不同类型检测样本的自适应算法智能选择问题。3.研究了KA-STAP色加载技术及其数值域求解算法。色加载算法利用杂波先验知识对采样杂波进行预白化滤波,降低杂波子空间的维数,减少后续STAP对IID样本数的需求。详细推导了一种有效的最优加载因子求解算法,使得色加载方法达到最优性能。深入研究了基于QR分解和逆QR分解的色加载数值域求解算法,该算法避免了杂波样本协方差矩阵直接求逆(SMI)问题,具有更好的数值稳定性,便于通过高度并行的流水运算结构快速递推实现,满足机载雷达STAP对大数据吞吐量实时处理的需求。4.构建了一个机载相控阵雷达KA-STAP仿真系统。分析了系统的设计方法和实现结构,便于KA-STAP技术的工程应用。
[Abstract]:Space-time adaptive processing (STAP) is a key technique for moving target detection in airborne phased array radar. In the non-uniform clutter environment, the lack of enough independent co-distributed (IID) training samples to estimate the statistical characteristics of clutter leads to a serious deterioration of adaptive performance, which is the biggest problem faced by STAP processing. Therefore, the development of new STAP algorithm which can adapt to non-uniform environment has become one of the research directions of STAP technology. The prior knowledge of detection environment is applied to radar signal processing reasonably, and the space-time adaptive processing (KA-STAP), which is aided by knowledge, has become the research focus in solving the clutter nonuniformity problem and improving the performance of STAP. In addition, the huge amount of computing is another problem facing STAP. Therefore, this paper will focus on the KA-STAP technology and its numerical range algorithm, including the following aspects: 1. The problem of prior clutter covariance estimation for detecting environment is studied. Based on the prior information such as terrain and geomorphology of real scene, (DEM) data of digital elevation model, radar system parameters and platform motion parameters, the prior clutter model of the unit to be detected is estimated accurately. 2. A knowledge aided adaptive power residue (KA-APR) nonuniform sample detection algorithm is proposed. The algorithm indirectly utilizes the prior knowledge of clutter to detect non-uniform STAP training samples, which has better detection performance than conventional detection methods. According to the proposed KA-APR detection algorithm to judge the type of sample data (uniform or non-uniform), the intelligent selection problem of adaptive algorithm for different types of samples is studied. The KA-STAP color loading technique and its numerical range solving algorithm are studied. The color loading algorithm uses the prior knowledge of clutter to prewhiten the sampled clutter to reduce the dimension of clutter subspace and to reduce the demand of subsequent STAP for IID sample number. An effective algorithm for solving the optimal loading factor is derived in detail, which makes the color loading method achieve the optimal performance. Based on QR decomposition and inverse QR decomposition, the algorithm of solving chromatic loading numerical range is studied in depth. The algorithm avoids the clutter sample covariance matrix to solve the inverse (SMI) problem directly, and has better numerical stability. It is easy to realize by high parallel pipelining operation structure quickly and recursively, which meets the need of real-time processing of big data throughput by airborne radar STAP. 4. An airborne phased array radar KA-STAP simulation system is constructed. The design method and implementation structure of the system are analyzed, which is convenient for the engineering application of KA-STAP technology.
【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】：TN958.92
本文编号：2268583
[Abstract]:Space-time adaptive processing (STAP) is a key technique for moving target detection in airborne phased array radar. In the non-uniform clutter environment, the lack of enough independent co-distributed (IID) training samples to estimate the statistical characteristics of clutter leads to a serious deterioration of adaptive performance, which is the biggest problem faced by STAP processing. Therefore, the development of new STAP algorithm which can adapt to non-uniform environment has become one of the research directions of STAP technology. The prior knowledge of detection environment is applied to radar signal processing reasonably, and the space-time adaptive processing (KA-STAP), which is aided by knowledge, has become the research focus in solving the clutter nonuniformity problem and improving the performance of STAP. In addition, the huge amount of computing is another problem facing STAP. Therefore, this paper will focus on the KA-STAP technology and its numerical range algorithm, including the following aspects: 1. The problem of prior clutter covariance estimation for detecting environment is studied. Based on the prior information such as terrain and geomorphology of real scene, (DEM) data of digital elevation model, radar system parameters and platform motion parameters, the prior clutter model of the unit to be detected is estimated accurately. 2. A knowledge aided adaptive power residue (KA-APR) nonuniform sample detection algorithm is proposed. The algorithm indirectly utilizes the prior knowledge of clutter to detect non-uniform STAP training samples, which has better detection performance than conventional detection methods. According to the proposed KA-APR detection algorithm to judge the type of sample data (uniform or non-uniform), the intelligent selection problem of adaptive algorithm for different types of samples is studied. The KA-STAP color loading technique and its numerical range solving algorithm are studied. The color loading algorithm uses the prior knowledge of clutter to prewhiten the sampled clutter to reduce the dimension of clutter subspace and to reduce the demand of subsequent STAP for IID sample number. An effective algorithm for solving the optimal loading factor is derived in detail, which makes the color loading method achieve the optimal performance. Based on QR decomposition and inverse QR decomposition, the algorithm of solving chromatic loading numerical range is studied in depth. The algorithm avoids the clutter sample covariance matrix to solve the inverse (SMI) problem directly, and has better numerical stability. It is easy to realize by high parallel pipelining operation structure quickly and recursively, which meets the need of real-time processing of big data throughput by airborne radar STAP. 4. An airborne phased array radar KA-STAP simulation system is constructed. The design method and implementation structure of the system are analyzed, which is convenient for the engineering application of KA-STAP technology.
【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】：TN958.92
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