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

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