| 引用本文: | 李坤坤， 程 婕， 张智香， 胡 爽， 吴力华. 基于随机矩阵建模的低空飞行器跟踪方法[J]. 雷达科学与技术, 2025, 23(1): 67-74.[点击复制] |
| LI Kunkun, CHENG Jie, ZHANG Zhixiang, HU Shuang, WU Lihua. Low⁃Altitude Aircraft Tracking Method Based on Random Matrix Modeling[J]. Radar Science and Technology, 2025, 23(1): 67-74.[点击复制] |
|
| 摘要: |
| 近年来，服务于环境监测、应急救援等任务的低空飞行器使用频次日益上升，在创造良好社会经济效益的同时也带来空域监管压力。针对空管高分辨雷达跟踪识别具有扩展形态的低空飞行器，本文提出一种基于随机矩阵建模的飞行器跟踪及其外形参数估计方法。首先，根据高分辨雷达探测该类飞行器时雷达单帧多量测及飞行器主体外形近似为椭圆体的特点，引入可描述椭圆（体）的对称正定随机矩阵建模其扩展外形；其次，基于高斯逆威沙特分布滤波估计飞行器的运动状态、扩展外形矩阵；最后，对扩展外形矩阵估计结果进行特征值分解，使用特征值平方根及最大特征值对应的特征向量分别估计飞行器的半轴尺寸及主轴方向，从而实现飞行器扩展外形参数的在线估计。仿真实验结果表明，本文滤波方法具有良好的低空飞行器跟踪性能，可为识别具有扩展形态的低空飞行器提供信息支撑。 |
| 关键词: 低空飞行器 运动状态 扩展外形 随机矩阵 特征值分解 |
| DOI：DOI:10.3969/j.issn.1672-2337.2025.01.007 |
| 分类号:TN959.1 |
| 基金项目:国家自然科学基金面上项目（No. 12472359） |
|
| Low⁃Altitude Aircraft Tracking Method Based on Random Matrix Modeling |
|
LI Kunkun, CHENG Jie, ZHANG Zhixiang, HU Shuang, WU Lihua
|
|
1. Northwest Institute of Nuclear Technology, Xi’an 710024, China;2. School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China
|
| Abstract: |
| In recent years, the frequency of low?altitude aircraft employment has been increasing for tasks like environmental surveys and emergency rescues. While creating well social and economic benefits, it also brings pressure of airspace supervision. To address the tracking and identification of low?altitude aircraft with extension shape using high?resolution airspace supervision radars, a random matrix modeling?based method on the aircraft tracking and its extension shape parameters estimation is proposed in this paper. Firstly, with the characteristics that the high?resolution radar gets multiple measurements per scan when detecting the extended aircraft whose extension shape can be approximate to ellipsoid, its extension shape is modeled by introducing a symmetric positive definite random matrix which can characterize ellipsoid. Then, based on Gaussian Inverse Wishart filtering, the estimates of the aircraft’s kinematic state and extension shape matrix can be achieved. Finally, the eigenvalue decomposition on the estimated extension shape matrix is performed. The square roots of the resulting eigenvalues and the eigenvector corresponding to the maximum eigenvalue are respectively viewed as the aircraft’s half?axis sizes and main?axis direction. Thus, the online estimation of the aircraft’s extension shape parameters is realized. The simulation experiment results show that the method has well performance on tracking low?altitude aircraft, enabling it to provide information support for identifying low?altitude aircraft with extension shape. |
| Key words: low⁃altitude aircraft kinematic state extension shape random matrix eigenvalue decomposition |
