| 摘要: |
| 机载雷达通常工作在强杂波、强干扰的复杂电磁环境中，如何有效消除压制干扰抑制杂波是其中的关键问题。传统的两级空时自适应处理（STAP）算法在静默期采集信号得到干扰子空间，在开机阶段运用空间投影技术抑制干扰并采用降维STAP抑制杂波。虽然该算法在干扰平稳时具有较好的效果，但是在实际应用中，复杂的干扰环境会破坏广义平稳假设。因此，仅采集单个静默期的干扰样本来估计协方差矩阵会出现偏差。为了克服该问题，提出采集多个静默期的干扰信号来估计当前的干扰协方差矩阵，以此来提高估计准确度，可以更好地抑制干扰。仿真结果表明即使在当前静默期采集的干扰信息不完整时，该技术仍然能够利用以前的干扰信息准确估计干扰协方差矩阵。 |
| 关键词: 机载雷达；基于知识的干扰抑制 空时自适应处理 贝叶斯滤波 |
| DOI： |
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| 基金项目: |
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| Knowledge-aided two-stage adaptive anti-jamming technology |
| LEI Junke,LI Ruiyang,LEI Hao |
| () |
| Abstract: |
| Airborne radar usually works in complex electromagnetic environment with strong clutter and strong jamming. How to eliminate the interference and suppress clutter is one of the key issues. Traditional two-stage space-time adaptive processing(STAP) method collects signal in the quiet period to acquire jamming subspace while suppress interference at the starting period using space projection technique.Although the algorithm has good effect when interference is stable,in the actual application,complex jamming environment will damage the generalized stationary hypothesis. Therefore,using only a single silent period interference samples may not estimate the covariance matrix accurately. To solve this problem,a method for collecting interference signals in several silent period to estimate the current jamming covariance matrix to improve the estimation accuracy is proposed. The simulation results show that even the jamming information collected in the current silent period is not complete,the interference covariance matrix can still be accurately estimated using previous jamming information. |
| Key words: airborne radar knowledge-based interference suppression space-time adaptive processing(STAP) Bayesian filtering |
