...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Signal processing >Detection of subspace distributed target in partial observation scenario with Rao test
【24h】

Detection of subspace distributed target in partial observation scenario with Rao test

机译：Rao检验在局部观测场景中子空间分布目标的检测

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper deals with the problem of detecting a subspace distributed target obscured by disturbance consisting of clutter plus white noise. We focus on the partial observation scenario where some of the radar observations are missing, a phenomenon that usually caused by interference, spectrum sharing, compressed sampling, and so on. Detection strategies are established based on the Rao test. Specifically, we first derive the Rao test with the assumption that the disturbance covariance matrix under the null hypothesis is known. Then, the unknown covariance matrix in the test statistic is replaced with a suitable estimate to make the detector adaptive. At the estimation stage, two cases are considered, involving with and without disturbance only secondary data. The estimate of the disturbance covariance matrix is obtained by solving an optimization problem in the respective case that considers both the likelihood maximization and low-rank property of the clutter covariance matrix. Simulation results are presented to verify the effectiveness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文研究的问题是检测由杂波加白噪声组成的干扰所掩盖的子空间分布目标。我们重点关注部分雷达观测丢失的部分观测情况，这种现象通常是由干扰，频谱共享，压缩采样等引起的。基于Rao检验建立检测策略。具体而言，我们首先假设已知零假设下的干扰协方差矩阵，然后得出Rao检验。然后，将测试统计量中的未知协方差矩阵替换为合适的估计值，以使检测器具有自适应性。在估计阶段，考虑了两种情况，仅涉及和不涉及干扰的辅助数据。扰动协方差矩阵的估计是通过解决相应情况下的优化问题而获得的，该优化问题同时考虑了杂波协方差矩阵的似然最大化和低秩特性。仿真结果证明了所提方法的有效性。 （C）2019 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号