基于单快拍信号到达角估计算法的室内入侵检测

针对基于信道状态信息（CSI）的入侵检测方法易受环境布局及噪声干扰的影响从而导致检测率下降的问题，提出一种基于单快拍信号到达角（DOA）估计算法的室内入侵检测方法。首先，结合无线信号空间选择性衰落的特点对天线阵列接收到的CSI数据进行数学分解，并将未知的DOA估计问题转化为一个过完备表示的问题。然后，利用l₁范数对稀疏信号的稀疏性进行约束，通过求解稀疏正则优化问题得到准确的DOA信息，由此在数据层面为最终检测结果提供了可靠的特征参数。最后，根据前后时刻的DOA变化评估出室内安全指数（ISIN），进而实现室内入侵检测。在实验中，利用真实的室内场景对检测方法进行验证，并与传统的主成分分析和离散小波变换的数据预处理方法进行对比。实验结果表明：该方法能够在不同的复杂室内环境下准确检测出入侵行为的发生，平均检测率达到98%以上，且在鲁棒性上明显优于对比算法。

Indoor intrusion detection based on direction-of-arrival estimation algorithm for single snapshot

Intrusion detection methods based on Channel State Information(CSI) are vulnerable to environment layout and noise interference, resulting in low detection rate. To solve this problem, an indoor intrusion detection method based on the algorithm of Direction-Of-Arrival(DOA) estimation for single snapshot was proposed. Firstly, the CSI data received by the antenna array was mathematically decomposed by combining the feature of spatial selective fading of the wireless signals, and the unknown DOA estimation problem was transformed into an over-complete representation problem. Secondly, the sparsity of the sparse signal was constrained by l1 norm, and the accurate DOA information was obtained by solving the sparse regularized optimization problem, so as to provide the reliable feature parameters for the final detection results at data level. Finally, the Indoor Safety Index Number(ISIN) was evaluated according to the DOA changes before and after the moments, and then indoor intrusion detection was realized. In the experiment, the method was verified by real indoor scenes and compared with traditional data preprocessing methods of principal component analysis and discrete wavelet transform. Experimental results show that the proposed method can accurately detect the occurrence of intrusion in different complex indoor environments, with an average detection rate of more than 98%, and has better performance in robustness compared to comparison algorithms.