摘要
针对雷达目标检测中由于训练数据缺失导致传统自适应检测方法的检测性能下降的问题,提出一种改进的自适应匹配滤波方法.该方法首先将杂波用自回归过程表示;然后假设自回归参数已知,推导出广义似然比检验表达式;最后将采用训练数据估计得到的自回归参数的最大似然估计值代入广义似然比检验表达式中,代替已知的自回归参数.仿真实验结果表明,与传统的自适应方法相比,这种方法能在训练数据不足时提高检测性能.当雷达回波数目较大时,这种方法的检测性能接近理想的匹配滤波方法.
In order to overcome the detection degradation for the conventional detectors in the limited- training environment, a modified adaptive matched filter is proposed by modeling the disturbance as an autoregressive process with unknown parameters. The detector is derived by resorting to a two-step design procedure： first derive the generalized likelihood ratio test under the assumption that the parameters of the autoregressive process are known, and then, the maximum likelihood estimates of the parameters, based on the training data, are substituted in place of the true parameters into the test. The detection performance of the new receiver shows that the proposed receiver can lead to a noticeable performance improvement over the conventional adaptive matched filter. For a moderate size of radar echoes, the proposed detector performs close to the optimum matched filter even in the limited-training environment.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2018年第1期12-16,82,共6页
Journal of Xidian University
基金
国家自然科学基金资助项目(61271297
61272281
61301284)
博士学科点科研专项基金资助项目(20110203110001)
国家部委预研基金资助项目(9140A07020913DZ01001)
关键词
雷达检测
自适应匹配滤波
自回归建模
radar detection
adaptive matched filter
autoregressive process
