Research on Application of Statistical Pattern Recognition in Phased Array Antenna Fault Diagnosis

As phased array antenna is widely used in radar system, fast diagnosis of element has become a challenge. According to the difficulty of phased array antenna elements detection, a novel method based on statistical pattern recognition is proposed. At first, the phased array diagnosis principle is stated. Simulation environment is built with MoM. Time domain feature and wavelet feature are extracted. In order to enlarge the mean distance between classes, the fault tree diagnosis model is built to reduce the scale of the discrimination problem, subspaces are divided in time domain feature space using projected clustering algorithm, and wavelet feature is utilized to discriminate in leaf nodes. The location of the faulty elements is realized. Simulations show that at low SNR, the method has obvious advantages over nonhierarchic method. At SNR higher than 8dB, recognition rate reaches 95%. As the scale increases, recognition rate does not decrease obviously. Results turn out that this method is effective in diagnosis of phased array element theoretically. In application, only conduct the diagnosis on each row or column of the array, the failure information of the whole antenna can be learned.