Accurate Estimation of LPI Radar Pulse Train Parameters via Change Point Detection

Estimating the time of arrival (TOA), pulse width (PW), and pulse repetition interval (PRI) is critical for identifying the low probability of intercept (LPI) radar signals as it is common in electronic warfare (EW) to experience a pulse train being received. To determine these parameters, we need to differentiate between pulse and noise in a pulse train. However, separating the LPI radar pulse train into pulse and noise is challenging owing to its characteristics and modulation scheme. Therefore, we propose a method to accurately estimate TOA, PW, and PRI using change point detection, which can effectively classify the pulse train into pulse and noise. The main contributions of this study are as follows. First, a denoising method robust to various modulation schemes is introduced. Second, an algorithm capable of estimating the parameters without considering the threshold calculation is presented. Moreover, most prior studies have entailed parameter estimations for a single pulse. In contrast, we focus on estimating parameters when a pulse train is intercepted and generating superior results even in the signal-to-noise ratio under 0 dB. We present experiments performed on the following eight modulation schemes: linear frequency modulation, Costas, Barker, Frank, P1, P2, P3, and P4 codes. Furthermore, we compare the proposed method with the wavelet-based method, which has received much attention in EW. The results demonstrate that the proposed method outperforms the conventional approaches.