In this paper, an intelligent algorithm for clustering, intra-pulse modulation detection and separation and identification of overlapping radar pulse train is presented. In most cases, based only on primary features of incoming radar signals, the modern electronic intelligence system cannot recognize the different devices of the same type or class. A very important role is played by Measurement and Signature Intelligence. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. The information such as Pulse Repetition Interval (PRI), Angle of Arrival (AoA), Pulse Width (PW), Radio Frequencies (RF), and Doppler shifts are not usable. In the proposed algorithm, for clustering of overlapping pulses received from self-organization neural network SOFM (due to its high accuracy in comparison with other neural networks, such as CLNN and neural networks (Fuzzy ART), and for detecting intra-pulse modulation type, matrix method, and for identifying the radar type, RBF neural network have been used. The simulation results of the proposed algorithm shows that in the presence 5% noise and 5% missing pulse, the accuracy of the clustering part of the proposed algorithm is equivalent to 91/8%, intra-pulse modulation recognition accuracy is 98%, the detection accuracy is 2/99%, and the total output of the algorithm precision is 89/244%, respectively. پرونده مقاله

A closed-form solution for target localization based on the realistic distance-dependent noises in illuminator of opportunity passive radar and the reduction method of the bias which exists in the two-stage weighted least squares (2SWLS) method is proposed. 2SWLS is a classic method for time-of-arrival (TOA) and frequency-of-arrival (FOA) localization problem and has a couple of improved solutions over the years. The 2SWLS and its improved solutions have great localization performances in their established location scenarios on the basis of two approximations that setting the noise to a constant and ignoring the high-order terms of TOA and FOA measurement noises. It is these two approximations that lead to a sub-optimal solution with bias. The bias of 2SWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than active radar. Therefore, this paper starts by taking into consideration of the realistic distance-dependent characteristics of TOA/-FOA noises and improving 2SWLS method. Then, the bias of the improved 2SWLS method is analyzed and bias-reduced solution based on weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared to the existing improved solutions of the 2SWLS, the proposed method effectively reduces the bias and achieves higher localization accuracy. پرونده مقاله