恒虚警检测信源数的方法

提出了一种恒虚警检测信源数的方法,该方法通过定义一个由观测协方差矩阵相邻特征值之差统计量构成的五维矢量序列,并利用K均值(K-means)聚类算法将所定义的五维矢量序列分成两类,且视为信号和噪声子空间.当将噪声予空间所对应的特征值序列描述成一个统计分布,并通过期望最大(expectation maximization,...

A method for detecting the number of signal sources with constant false alarm

A new method with a given constant false alarm rate for detecting signal source numbers is proposed. A five-dimension vector with the statistics of the eigenvalue differences of an observation covariance matrix is first defined. Then, the K-means clustering algorithm is used to divide these vectors into two classes. which are respectively regarded as the signal and noises subspaces of the eigenvalues corresponding to the five-dimension vectors. When the eigenvalues of noise are expressed as a probability distribution with a Gaussian mixture one known to be able to describe any probability distribution, the expectation maximization (EM) algorithm can be utilized to estimate the distribution. By means of the estimated distribution, the Neyman-Pearson (NP) hypothesis test is exploited to do source number detection with a given constant false alarm rate. Moreover, an approximated NP hypothesis, where the noise eigenvalue distribution is assumed Gaussian one regardless of the actual one, is given in order to reduce the computation complexity of the proposed method. Numerical simulation results verify the effectiveness of the proposed method while its superiority over the methods reported in literature is shown.