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中文摘要：
提出了一种基于LKRX检测器的实时异常检测算法．利用局部因果滑动阵列窗，使检测系统保持因果性：根据卡尔曼滤波器的递归思想，利用Hermitian矩阵分块求逆引理和Woodbury引理，将LKRX算法中核协方差矩阵以及其逆矩阵以递归方式更新，避免了数据的重复计算和逆矩阵的求解，大大降低了算法复杂度．通过真实数据进行实验，结果表明，与LKRX算法相比，实时LKRX算法在保持相同检测精度的同时，消耗更少的计算时间；而与实时RX算法相比，实时LKRX算法能够检测到更多的异常目标．
英文摘要：
LKRX detector-based hyperspectral real-time anomaly detection algorithm was proposed. U- sing local causal sliding array window, the causality of detection system is remained. According to Kalman filter, by using Hermitian lemma and Woodbury＇ s identity, the kernel covariance matrix and its inverse in KRX algorithm are updated recursively. This thereby leads to low computational complexity. Experimental results demonstrated that real-time KRX detector consumes less time in comparison with KRX detector by keeping the same detection performance, which detects more anomalies.
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