Detection and Parameter Estimation Analysis of Binary Shift Keying Signals in High Noise Environments

Duong, Van Minh; Veselý, Jiří; Hubacek, Petr; Janu, Premysl; Phan, Nhat Giang

doi:10.3390/s22093203

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…However, only linear frequency modulation (LFM) was considered. In [12], a cross-correlation-based two-stage PW estimation method was proposed for binary phase shift keying (BPSK). In the first stage, the center frequency is estimated by conducting the cross-correlation between the received and the reference signals.…”

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%

“…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%

Section: Introductionmentioning

confidence: 99%

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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“…Ref. [ 24 ] proposes a two-stage method based on cross-correlation function which estimates the carrier frequency of the BPSK signal at first stage and estimates its the PN codes at second stage. However, this method is only suitable for Barker codes 7, 11 and 13.…”

Section: Introductionmentioning

confidence: 99%

PN Codes Estimation of Binary Phase Shift Keying Signal Based on Sparse Recovery for Radar Jammer

Peng

Chen

2023

Sensors

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Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time frequency analysis methods due to its phase jumping and abrupt discontinuity features which makes it difficult to extract PN (PN) codes of the BPSK signal. To solve this problem, a two-step PN codes estimation method based on sparse recovery is introduced in this paper. The proposed method first pretreats the BPSK signal by estimating its center frequency and converting it to zero intermediate frequency (ZIF). The pretreatment transforms phase jumps of the BPSK signal into the level jumps of the ZIF signal. By nonconvex sparsity promoting regularization, the level jumps of the ZIF signal are extracted through an iterative algorithm. Its effectiveness is verified by numeric simulations and semiphysical tests. The corresponding results demonstrate that the proposed method is able to estimate PN codes from the BPSK signal in serious electromagnetic environments.

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Detection and parameter estimation of intra-pulse modulated radar signals in complex interference environments+

et al. 2023

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The main aim of this review is to describe in detail an advanced technique to detect and estimate the prior unknown parameters of intra-pulse modulated signals and the verification with typical radar signals. The method is approved for detecting parameters successfully, including chirp rate, carrier frequency, and pulse width. The review presents already-done research on detecting and estimating single and multi-component linear frequency modulation (LFM) and binary phase shift keying (BPSK) signals in a strong noise environment and studies the technique on one more case, a mixture of LFM and BPSK signals. Firstly, the accuracy of the revised technique is shown in detecting and estimating parameters of a single and multi-component LFM signal in white noise and a mixture of continuous wave signals and noise, or a single BPSK signal in strong white noise. All of them have been done in the existing studies. This method is continuously tested in the second part by detecting a mixture of LFM and BPSK signals and estimating their parameters in intense noise. The tested experimental results demonstrate that the technique can detect single and multi-component real-time LFM signals, single BPSK as well as verification with a mixture of real-time LFM and BPSK signals with $$SNR \ge - 12{\text{ dB}}$$ S N R ≥ - 12 dB is performed. As a result, the technique outperforms the existing detection methods based on machine learning and artificial intelligence.

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Detection and Parameter Estimation Analysis of Binary Shift Keying Signals in High Noise Environments

Cited by 4 publications

References 9 publications

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

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

PN Codes Estimation of Binary Phase Shift Keying Signal Based on Sparse Recovery for Radar Jammer

Detection and parameter estimation of intra-pulse modulated radar signals in complex interference environments+

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