Enhanced pulse amplitude estimation for electronic warfare systems

Lee, Yu-Ri; Kim, Dong-Gyu; Kim, Hyoung-Nam

doi:10.1049/iet-rsn.2018.5415

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…In addition, a study on wavelet denoising has also drawn considerable attention in EW. The estimation method employing wavelet packet decomposition and kurtosis enhanced the pulse amplitude estimation accuracy by determining the exact PW [15]. As a threshold based on kurtosis is used to decide the presence of noise in each packet, it is suitable for a signal with an SNR of upper 0 dB.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this study is that it provides an accurate estimation method for the TOA, PW, and PRI in a weak-signal environment. In addition, most studies have not considered various modulation schemes [9], [10], [11], [12], [13], [14], [15], [16], [17], whereas the proposed method shows robust estimation performance for the eight modulation schemes, such as LFM, Costas, Barker, Frank, P1, P2, P3, and P4 codes. Furthermore, most threshold-based estimation methods use a fixed value or a variable value (e.g., constant false alarm rate) as a threshold [9], [10], [11], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, most studies have not considered various modulation schemes [9], [10], [11], [12], [13], [14], [15], [16], [17], whereas the proposed method shows robust estimation performance for the eight modulation schemes, such as LFM, Costas, Barker, Frank, P1, P2, P3, and P4 codes. Furthermore, most threshold-based estimation methods use a fixed value or a variable value (e.g., constant false alarm rate) as a threshold [9], [10], [11], [14], [15]. However, fixed-threshold-based methods are vulnerable in coping with a weak-signal environment because the noise is dominant as SNR decreases.…”

Section: Introductionmentioning

confidence: 99%

“…To minimize these potential troubles, we take a different approach of separating the pulse train into pulse and noise in advance and then using a fixed threshold. Lastly, most studies consider single pulses [12], [13], [14], [15], [16]. Although some studies concentrate on parameter estimation for the pulse train, they are fragile to weak signals [9], [10] or consider only single modulation [11].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.INDEX TERMS Electronic warfare, LPI radar signal, parameter estimation, change point detection.

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Section: Introductionmentioning

confidence: 99%