Detection and parameter estimation of frequency hopping signal based on the deep neural network

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert–Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert–Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

Aiming at the problem that single‐network hopping signals have not fully utilised its frequency domain sparse characteristic in the parameter estimation, this study proposes a parameter estimation of frequency hopping (FH) signal based on multi‐measurement vector sparse Bayesian learning (MSBL) in modulation wideband converter (MWC). Since the FH signal is sparse in the frequency domain, the authors apply the MSBL method to estimate its parameters. After the signal is sampled by the MWC, the MSBL algorithm is used to reconstruct its support set. Then the time–frequency ridge method is used to estimate the signal's hop duration, time‐hopping, and carrier frequency based on the time–frequency map. Simulation experiments show that under the condition of low signal‐to‐noise ratio, the parameter estimation performance in the case can be improved by up to 65% and anti‐noise performance can be improved up to 6 db compared with the existing method. The result is very close to the Nyquist full sampling and can greatly improve the accuracy of the FH signal parameter estimation in the MWC system and relieve the pressure of the hardware.

In differential frequency hopping (FH) wireless systems, data are expressed by frequency hopping pattern, and frequency detection involving the Fourier transform and widebands requires large numbers of Nyquist samples, which makes them difficult to implement. Due to the sparseness of FH signals in the frequency domain, compressive sampling (CS) techniques have been explored for the sampling and reconstruction of such signals. In CS, the number of measurements must satisfy $$M\ge {C}\cdot {\mu }^2(\varvec{\varPhi },\varvec{\varPsi })\cdot {K}\cdot {}log(N)$$M?C·μ2(?,?)·K·log(N), where $$C$$C and $$\mu (\varvec{\varPhi },\varvec{\varPsi })$$μ(?,?) are the constants, $$K$$K is the sparsity level, and $$N$$N is the dimension of the original signal. Because of the large number of channels in the operation band, many of these channels are often occupied by diverse frequency-fixed (FF) signals. For this reason, the band compression rate (BCR $$=N/M$$=N/M) may be greatly reduced. However, the FH signals only occupy a single frequency at a time. This means that $$K=1$$K=1, and the number of measurements can be far smaller than the number to restore the mixture of FH and FF signals. In this paper, a CS method that is not subject to the influence is presented, and the BCR can reach 1,000:6 when the FH signal is mixed with 50 interferences. The proposed algorithm covers the generation of the sampling matrix and signal reconstruction. Specifically, it maximizes the signal-to-noise ratio of the compressed signal with a white noise background. The new reconstruction algorithm was found to be 5 times faster than the OMP algorithm in the case of that $$K=4$$K=4, and it is not limited by the inequality given above.

Frequency hopping (FH) signal has been widely used in military and civilian fields because of its superior anti-jamming performance. Meanwhile, its natural characteristics, i.e., hopped carrier frequency, brings a great challenge to the reconnaissance of FH emitters. To locate a high-speed moving FH emitter, the estimation of range difference (RD) and range rate difference (RRD) is a key process in passive localization. This paper proposes a joint RD and RRD estimation algorithm with the consideration of range migration (RM) during observation time. In this method, a new operation named frequency reversing transform is first presented to remove the random phase of the FH signal. Then, RD and RRD can be coherently estimated after RM correction via the scaled Fourier transform. This method accomplishes the RD and RRD estimation without knowing the hopped carrier frequencies of received FH signals, and it can be fast implemented by complex multiplications, fast Fourier transform (FFT) and inverse FFT operations without any searching process. Numerical experiments indicate that compared with several representative methods, the proposed estimation method achieves a better anti-noise performance with lower computational cost.

Alpha stable distribution is a proper model for describing impulsive noise in communication systems. To solve the degradation problem of frequency hopping (FH) signal parameter estimation in alpha stable noise with conventional time-frequency analysis, a new method based on fractional lower order short-time Fourier transform is proposed. The computer simulation results show that the proposed method is valid for frequency hopping signal parameter estimation in the presence of alpha stable noise, and it distinctively outperforms the conventional short-time Fourier transform.

Long-time coherent integration using frequency hopping signals is a challenging problem for passive bistatic radar due to its frequency hopping characteristics. Apart from range walk, range curve, and Doppler frequency migration, Doppler diffusion caused by frequency hopping characteristics occurs within the observation time, which also lowers the detection performance. To deal with this problem, a novel coherent integration method for frequency hopping signals based on passive bistatic radar is proposed in this paper. In this novel method, range curve and range walk are eliminated by applying generalized Keystone transform. Then, Doppler frequency migration caused by the target's acceleration is compensated for by a parameter search with a designed search scope. Finally, Doppler frequency migration caused by frequency hopping characteristics is compensated for by designing a new acceleration compensation function and a revised rotation factor for Fourier transform. Since migration effects caused by frequency hopping characteristics are considered and compensated for when using frequency hopping signals, the weak target echo can be better integrated in the observation time compared to when using the existing methods. The simulation results and performance analysis illustrate the effectiveness of the proposed method.

An analysis method of frequency hopping (FH) signals based on image processing is proposed in this paper. Firstly the time-frequency spectrum of FH signals is obtained using short time Fourier transform (STFT), secondly the background noise in time-frequency spectrum is removed by contrast stretch, binarization operation and morphological operation, and then time-frequency resolution is improved by morphological thinning algorithm, lastly the parameters of FH signals such as hopping frequencies, hop timing, hop time and hop duration are estimated. Simulation results demonstrate the validity of this method.

Detection of the frequency-hopping (FH) signal is challenging when the hopping rate is unknown. Conventional spectrogram-based schemes can detect FH signals, but its performance is limited by the time-frequency resolution trade-off and spectral leakage. To alleviate this issue, we design convolutional neural network (CNN) and hybrid CNN/recurrent neural network (RNN)-based schemes. The CNN-based scheme alleviates spectral leakage by using feature maps. The hybrid CNN/RNN-based scheme mitigates the time-frequency resolution trade-off by using feature maps extracted from spectrograms of various window lengths. In simulations, the hybrid CNN/RNN-based scheme is shown to outperform the CNN-based and conventional detection schemes.

Time-Frequency (TF) representation is a precursory work on signal analysis. This paper addresses a novel method for TF representation of Frequency Hopping (FH) signals based on the relation between cyclic spectrum and TF representation. Differently from other known methods, the proposed method provides a simple valid time frequency representation of FH signals with the property of minimizing the effect of interferences while still offering an accurate TF resolution. Experiment result proved the validity and feasibility of the approach.

Information communication of frequency hopping is a kind of spread spectrum in communication, it has strong anti-jamming ability, good performance of confidentiality, resistance to intercept ability and multi-user random access etc. Frequency hopping signal's spectrum is a very broad bring to unequal interval random jump, frequency hopping communication is using discrete channel. Today, the spectrum resources is shortage, in order to improve the existing spectrum utilization, cognitive radio therefore put forward the solution. The combination of cognitive radio and frequency hopping, not only improve the utilization rate of spectrum, but also improve the communication quality. This article is based on the combination of cognitive and frequency hopping, and proposed a kind of channel Information cognitive radio network channel access with good SNR selected.

Channelized intercept receivers with fast Fourier transform (FFT) front-end preprocessors are often used to detect frequency-hopped (FH) signals. Ideally, the FFT frequency band should coincide with the entire FH spread-spectrum bandwidth. However, in some practical implementations, hardware and/or real-time computational signal processing capabilities limit the FFT to a fraction of the total band of interest. In this case, the preprocessor will only intercept some of the received hops, but the FH signal can still be successfully detected assuming that enough hops are observed. If the receiver front end is tunable, an alternative approach is to step the FFT band within each hop such that more or all of the FH signal bandwidth is covered. The tradeoff is that more of the hops will be intercepted but the receiver will detect only part of each hop. This paper analyzes this partial-band FH detection scheme and determines the optimum performance strategy. >

Accurate jamming detection in the broadband frequency hopping (FH) systems plays a vital role in improving the anti-jamming performance of the communication systems. The traditional jamming detection algorithms are prone to misjudge FH communication signals as jamming signals, leading to inaccurate jamming detection. Therefore, this paper proposes a jamming detection algorithm for broadband FH systems based on multi-segment signals spectrum clustering (MSSC). According to the difference in the time-frequency characteristics between FH signals and jamming signals, the proposed algorithm accurately detects the jamming by clustering the spectral components of signals in multiple periods. In addition, a jamming detector suitable for broadband FH systems is designed and the theoretical solution to the threshold factor of signal detection based on the Welch spectrum is also given in this paper. The simulation results show that the MSSC-based jamming detection algorithm proposed in this paper can reduce the jamming false detection probability and significantly improve the estimation accuracy of jamming parameters, effectively overcoming the defects of the traditional FFT-based energy detection and the multi-hop accumulation algorithms whose detection accuracy is sensitive to FH signals.

Most of the earlier tracking and network sorting approaches with a high sampling rate for frequency hopping (FH) signals did not adapt to the wideband system during their implementation, whereas the sub-Nyquist based algorithms cannot satisfy the real-time requirement for dealing with the rapid change of sparsity. It is important to improve the compressed sensing (CS) methods for tracking and sorting wideband FH signals. In this paper, a dynamic programming modulated wideband converters (MWC) scheme is proposed. First, considering the wide gap of FH signals, an improved power estimation method is proposed to track the support set in the time domain. Second, to sort multiple signals more effectively, a feedback control algorithm based on dynamic programming is proposed. In the proposed method, the total sampling rate is decreased significantly, and multiple FH signals are separated rapidly without recovery based on the results of tracking and comparative power. Simulations show that the proposed method can track and sort FH signals efficiently and more practically than previous methods.

Compared with common jamming techniques, the correlated jamming is much efficient against the frequency hopping (FH) communication system. Because of the similarity between the correlated jamming signal and the FH signal, it is very difficult for common anti-jamming techniques to deal with such correlated jamming. According to the independence between the payload of the correlated jamming signal and the victim FH signal, a novel independent component analysis (ICA) based receiver is proposed in this paper. Two antennas are introduced at the receiver end. The received signals are despread firstly to reduce the dimension of mixing matrix. Then ICA is utilized to recover the useful signal from two baseband signals. The numerical simulation results indicate that the proposed ICA-based receiver is robust to the correlated jamming. Even when the power of correlated jamming is very strong, the proposed receiver can still improve the BER performance greatly.

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication networks. Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity, the FH frequency is thus predictable. In this paper, we investigate the FH frequency reconnaissance and prediction of a non-cooperative communication network by effective FH signal detection, time-frequency (TF) analysis, wavelet detection and frequency estimation. With the intercepted massive FH signal data, long short-term memory (LSTM) neural network model is constructed for FH frequency prediction. Simulation results show that our parameter estimation methods could estimate frequency accurately in the presence of certain noise. Moreover, the LSTM-based scheme can effectively predict FH frequency and frequency interval.