Jamming-to-signal Ratio Research Articles

Defeating angle tracking: A comprehensive analysis of scan rate modulation jamming against sequential lobing radars

AbstractThe authors analyse the sequential lobing radar (SLR) resistance against the scan rate modulation (SRM) jamming technique. Unlike previous studies, the authors analyse SLRs with sinc antenna patterns, enabling investigation of the sidelobe region's vulnerability. By incorporating second‐order angular tracking loops (SOATLs), the first analysis of break‐lock probability for SLRs under SRM jamming is introduced. The analysis reveals the influence of SOATL parameters (damping ratio and natural frequency) on both angular root‐mean‐square‐error at low jamming‐to‐signal‐ratio (JSR) region and break‐lock probability at high JSR region. Additionally, the impact of squint angle on SLR resistance to SRM jamming is examined. Furthermore, the optimal duty cycle as a function of JSR, which maximises break‐lock probability is presented. Significantly, the theoretical results support the 20 dB JSR requirement for SRM effectiveness, often cited experimentally in literature, and extend the theoretical analysis of the SRM technique carried out for the main lobe region of SLRs. This finding bridges the gap between theoretical analysis and practical observations.

Moving target identification under deception jamming circumstance based on radon-Fourier transform

This paper addresses the identification problem between the moving targets and deceptive false targets in the presence of the across-range unit (ARU) effect. Specifically, a typical self-defense jammer that generates multiple range-Doppler joint deception false targets within a short period is considered. Based on the radon-Fourier transform (RFT) results of two-dimensional radar echo, the disparity features between the physical and false targets in the output amplitude are first revealed. In this respect, a novel multi-stage RFT processing schema is designed to provide the amplitude characteristic for the under-determined targets. According to the constructed amplitude vector, the variance of the derived amplitude ratio vector is selected as the identification criterion for the proposed multi-stage RFT-based target identification (MSRFT-TI) algorithm. The performance of the proposed method in target identification is evaluated through numerical simulations for two different radar systems. The results indicate that the proposed MSRFT-TI algorithm is effective even in challenging scenarios with a small signal-to-noise ratio (SNR) and large jamming-to-signal ratio (JSR).

Detection and Type Recognition of SAR Artificial Modulation Targets Based on Multi-Scale Amplitude-Phase Features

With respect to the detection and recognition of an Artificial Modulation Target (AMT) with different modulated types, the state-of-the-art methods generally suffer the deficiencies of overfitting and insufficient generalization of existing neural network solutions. To address these problems, this paper proposes a multi-scale amplitude-phase feature discrimination method for AMTs in SAR images. First, a multi-type modulated AMT Dataset is generated (AMT Detection and Modulation Type Recognition Dataset, ADMTR Dataset), wherein the factors of jamming position, jamming-to-signal ratio (JSR), and the modulated parameter are considered to enhance the generalization. Second, a Multi-Input Multi-Output Fusion Wavelet Neural Network (MIMOFWTNN) is established, which not only uses the amplitude information of the scene but also adequately makes use of the phase and high-frequency information. This empowers us to detect the AMT in a higher dimensional feature space such that the type recognition can be implemented with more certainty. Analysis and discussions conducted on comparison experiments and ablation experiments demonstrate that the proposed network can achieve an average accuracy of 96.96% on the cross-validation set and a correct rate of 99.0% on the completely independent test set, which outperforms the compared methods.

Anti-interrupted sampling repeater jamming based on intra-pulse frequency modulation slope agile radar waveform joint FrFT

Interrupted sampling repeater jamming (ISRJ) is coherent with the radar-transmitted signal, which can form multi-false targets at the radar receiver. Therefore, ISRJ suppression is a compelling task in electronic counter-countermeasure. Intra-pulse frequency coded signals are not well energy-concentrated in the time-frequency (TF) domain, resulting in interference and target overlapping in the TF domain when the number of intermittent samplings and the number of forwarding times is large. To solve this problem, an anti-ISRJ method is proposed based on intra-pulse frequency modulation (FM) slope agile radar waveform joint fractional Fourier transform (FrFT). Firstly, the radar transmits the intra-pulse FM slope agile signal, and the adjacent sub-pulses are modulated with positive and negative FM slopes. Next, the echo signals are divided into multiple slices with the same sub-pulse timing as the transmitted signals. Then, utilizing the characteristics of transceiver time-sharing and energy suppression of the ISRJ, the FrFT is performed on each slice of the echo, and the interfered echo slices can be identified by fractional search and fractional order comparison. Finally, the interference in interfered echo slices is suppressed by fractional filtering. Simulation results show that the proposed method can recognize the interfered echo slice 100 % when the jamming-to-noise ratio and jamming-to-signal ratio (JSR) are both greater than 0 dB and do not generate false alarms when the signal-to-noise ratio is greater than 0 dB and JSR is greater than 10 dB. Fractional filtering reduces the loss of target energy in the process of interference suppression. Simulation results also verify the effectiveness of the proposed method against three typical types of synchronous ISRJ and the compound interferences formed by the combination of different types of synchronous ISRJ.

Suppression Method for Smeared Spectrum Jamming Based on Lv’s Distribution Combined with Biorthogonal Fourier Transform

Smeared spectrum (SMSP) jamming is a deceptive jamming technique commonly used against linear frequency modulation (LFM) radar. This technique produces high-density false targets that resemble a comb shape after pulse compression processing, resulting in both deceiving and suppressing effects. In order to suppress jamming effectively, a new antijamming technique has been proposed that combines Lv’s distribution (LVD) with biorthogonal Fourier transform (BFT). The main idea of this antijamming method is based on a three-step process: “parameter estimation—echo modulation—identification and suppression.” The first step involves obtaining the delay and chirp rate of the jamming from the LVD matrix of the radar echo. Based on the estimated parameter information, a reference signal is designed to modulate the jamming into a complete chirp signal. In the second step, biorthogonal Fourier transform is used to distinguish between the target’s echo and jamming based on the difference in the chirp rate. Finally, jamming is filtered using narrow-band filtering in the chirp rate domain, and the original echo is recovered by using biorthogonal inverse Fourier transform (IBFT). Simulation results demonstrate that the proposed method achieves high accuracy in estimating jamming parameters and is capable of suppressing false targets under high jamming-to-signal ratio (JSR) conditions.

A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR.

Echo Preprocessing-Based Smeared Spectrum Interference Suppression

Self-protection deceptive interferences (SPDI) are widely used in electronic countermeasures. Smeared spectrum (SMSP) interference, as a typical SPDI, can form a large number of dense false targets at the receiver output to affect effective target detection. Therefore, the suppression of SMSP interference is a compelling issue. The existing SMSP interference suppression methods inevitably result in energy loss of the target due to signal processing. This paper proposes a novel interference suppression method based on echo preprocessing to address this problem. Firstly, the pulse compression (PC) and the coherent integration (CI) characteristics of SMSP interference in the pulse Doppler radar are obtained through the derivation of formulas. Then, echo preprocessing is introduced, and the steps of interference suppression are listed in detail. Finally, the SMSP interference is suppressed because the preprocessed interference forms a center-shifting and range-scaling in the distance dimension after PC, and CI gain cannot be further obtained. The proposed method does not lose the energy of the true target because it does not involve filtering and reconstruction processing. Simulations show that the target detection probability of the proposed method can reach 100% via peak search after the interference suppression when the signal-to-noise ratio is greater than −10 dB and the jamming-to-signal ratio (JSR) is less than 35 dB. Compared with three representative methods in the recent literature, the proposed method has better robustness and higher JSR tolerance.

Online unsupervised generative learning framework based radar jamming waveform design

AbstractThe jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one.

A Novel Method for Interferometric Phase Estimation in Dual-Channel Cancellation

Multichannel SAR systems have grown rapidly over the past decade due to their powerful high-resolution and wide-swath (HRWS) capabilities. Because spatially separated channels also have the potential to suppress jamming, dual-channel cancellation is a general method that is effective regardless of the type of jamming signal. In this paper, the principle of dual-channel cancellation (DCC) is introduced, and several practical problems using DCC are also discussed. Moreover, this paper emphasizes interferometric phase estimation, which is the key to DCC. If the jamming-to-signal ratio (JSR) is high, the interferometric phase can be estimated accurately from the interferometry of two channel signals, but estimation becomes rather difficult when the JSR decreases. To solve the problem of interferometric phase estimation under a low JSR, a novel interferometric phase estimation method using cosine similarity is proposed in this paper. L-band airborne dual-channel SAR is performed to investigate the applicability of the method. The results not only prove that cosine similarity is an effective method for interferometric phase estimation, but also demonstrate the potential of DCC in the SAR anti-jamming processing.

Analysis of outboard cross‐eye jamming using an unmanned platform

To address the problem and reduce the risk wherein a ship-based cross-eye jammer could set a protected ship as a target, an unmanned platform equipped with a jammer is proposed to be used. However, the jamming situation violates the original cross-eye jamming conditions and introduces some new elements. In this regard, the impact of outboard cross-eye jamming in the presence of ship echoes is derived in detail. The analysis shows that the outboard cross-eye jamming combines the characteristics of outboard active and traditional cross-eye jamming gains. It can reduce the standard cross-eye jamming requirement of the jamming-to-signal ratio (JSR) and improve the jamming effect. The phase difference of a cross-eye jammer can be controlled at 0° or 180° to obtain a larger angular positioning deviation based on the JSR requirement, thus providing a theoretical basis for applications of unmanned outboard cross-eye jamming.

Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.

Abnormal Communication Signals Recognition Based on Image Enhancement and Improved Memory-Augmented Autoencoder

Detecting increasing anomalous signals is critical to effective spectrum management due to the complexity of the use of electromagnetic spectrum. The anomaly recognition approach based on autoencoder (AE) heavily relies on the assumption that the reconstruction error of normal signals is generally lower than that of abnormal signals. Unfortunately, such an assumption does not necessarily hold due to the excessive generalization ability of the AE. The memory-augmented autoencoder model (MemAE) has been recently proposed to address this issue by introducing memory modules. Still, the standard MemAE model performs poorly on complex image datasets, and its performance for abnormal communication signals recognition is unknown. Therefore, we propose an image enhancement and improved memory-augmented autoencoder model (IIMemAE) to recognize abnormal communication signals. Specifically, we consider two key factors, i.e., the existence of redundant information in the time-frequency spectrogram and the low recognition accuracy of normal signals of the standard MemAE model. We introduce an image enhancement module and an anomaly determination module compared with the standard MemAE model. The proposed IIMemAE model can address the issue that the performance of anomaly recognition may be degraded due to the imbalanced communication signals in the real world. The simulation results show that IIMemAE can effectively recognize synthetic abnormal communication signals even at low signal-to-noise ratio (SNR) and jamming-to-signal ratio (JSR) conditions and outperform the standard MemAE model. Besides, the parametric Pauta criterion proposed can balance the recognition accuracy of normal and abnormal signals to meet the need for diverse recognition tasks.

Interrupted-Sampling Repeater Jamming-Suppression Method Based on a Multi-Stages Multi-Domains Joint Anti-Jamming Depth Network

Jamming will seriously affect the detection ability of radar, so it is essential to suppress the jamming of radar echoes. Interrupted-sampling repeater jamming (ISRJ) based on a digital-radio-frequency-memory (DRFM) device can generate false targets at the victim radar by the interception and repeating of the radar transmission signal, which is highly correlated with the true target signal. ISRJ can achieve main lobe jamming and has both deception and oppressive jamming effects, so it is difficult for the existing methods to suppress this jamming effectively. In this paper, we propose a deep-learning-based anti-jamming network, named MSMD-net (Multi-stage Multi-domain joint anti-jamming depth network), for suppressing ISRJ main lobe jamming in the radar echo. In the first stage of MSMD-net, considering that the target signal is difficult to detect under a high jamming-to-signal ratio (JSR), we propose a preprocessing method of limiting filtering on the time-frequency domain to reduce the JSR using the auxiliary knowledge of radar. In the second stage, taking advantage of the discontinuity of the jamming in the time domain, we propose a UT-net network that combines the U-net structure and the transformer module. The UT-net performs target feature extraction and signal reconstruction in the signal time-frequency domain and preliminarily realizes the suppression of the jamming component. In the third stage, combined with phase information, a one-dimensional complex residual convolution U-net network (ResCU-net) is constructed in the time domain to realize jamming filtering and signal recovery further. The experimental results show that MSMD-net can obtain the best jamming suppression effect under different transmitted signals, different jamming modes, and different jamming parameters.

Single and Multiple Continuous-Wave Interference Suppression Using Adaptive IIR Notch Filters Based on Direct-Form Structure in a QPSK Communication System

The removal filter coefficients in this technique are dependent on the jammer’s power and its Instantaneous Frequency (IF) information, which can both be obtained in the time–frequency domain (adaptive filtering techniques). The dependence of the removing/reducing filter characteristics on the interference power is critical, as it allows an optimal trade-off between removal interference and the amount of self-noise generated by the filter. This trade-off is bounded by the two extreme cases of no notch filter (no self-noise) and full suppression (k1 = 1) for both low- and high-power jammer values. In this paper, a cascade second-order adaptive direct Infinite Impulse Response (IIR) Notch Filter (NF) with a gradient-based algorithm to suppress the Continuous-Wave (CW and MCW) interference is proposed for maximizing the receiver Signal-to-Noise Ratio (SNR) in a Quadrature Phase-Shift Keying (QPSK)-modulated signal. The suppression approach consists of two Adaptive IIR NFs (ANFs) based on a direct-form structure: the Hd1(z) and Hd1(z). The proposal in this work presents a low-complexity Time-Domain (TD) algorithm for controlling the update filter coefficient and notch depth. Simulation results demonstrate that the proposed approach represents an effective method for removing/reducing the impacts of CWI/MCWI, resulting in improved system performance for low- and high-power jammer values when compared with the case of full suppression (k1 = 1); furthermore, it also improves the notch filter’s output SNR for a given Jamming-to-Signal Ratio (JSR) value and Bit Error Ratio (BER) performance. For example, the SNR output of the proposed IIR NF was enhanced by 7 dB versus the case without a filter when Eb/No = 15 dB and JSR = −5 dB. The proposed method can detect and mitigate weak and strong jamming with JSR values ranging from −30 to 40 dB, and can track the hopping frequency interference. Moreover, an improved BER performance is seen as compared to the case without an IIR NF.

BER Expression of QAM for Symbol-Level Jamming

In wireless communication systems, quadrature amplitude modulation (QAM) schemes are widely used for high-speed data transmission, and recently, symbol-level jamming (SLJ) is being studied as an effective jamming technique. However, bit error rate (BER) expressions of QAM for SLJ have not been presented in previous studies. In this paper, we derive the exact BER expressions of QAM considering phase coherent and noncoherent scenarios under SLJ. Using the derived expressions, we investigate the SLJ order that maximizes BER of QAM for jamming-to-signal ratio (JSR). Further, we present BER of QAM with tone jamming and show that tone jamming is a special case of noncoherent SLJ. In addition, we use a probability density function of the SLJ amplitude to show that the high order noncoherent SLJ is similar to noise jamming. To increase BER in a low JSR region, we present a BER expression of QAM when SLJ exists in a pulse form. Using the derived expression, we investigate the order and pulse duration ratio to maximize BER for JSR. The derived expressions are verified using computer simulations.

Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze (PDRF). To solve the problem, the matched filter outputs of the PDRF under the action of target and jamming are analyzed. Then, the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF, and the time-frequency joint feature is constructed. Based on the time-frequency joint feature, the naive Bayesian classifier (NBC) with minimal risk is established for target and jamming recognition. To improve the adaptability of the proposed method in complex environments, an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed. The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio (SNR) decreases and the jamming-to-signal ratio (JSR) increases. Moreover, the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Multi-Frequency Interference Detection and Mitigation Using Multiple Adaptive IIR Notch Filter with Lattice Structure

Radio Frequency Interferences (RFI), such as strong Continuous Wave Interferences (CWI), can influence the Quality of Service (QoS) of communications, increasing the Bit Error Rate (BER) and decreasing the Signal-to-Noise Ratio (SNR) in any wireless transmission, including in a Digital Video Broadcasting (DVB-S2) receiver. Therefore, this paper presents an algorithm for detecting and mitigating a Multi-tone Continuous Wave Interference (MCWI) using a Multiple Adaptive Notch Filter (MANF), based on the lattice form structure. The Adaptive Notch Filter (ANF) is constructed using the second-order IIR NF. The approach consists in developing a robust low-complexity algorithm for removing unknown MCWI. The MANF model is a multistage model, with each stage consisting of two ANFs: the adaptive IIR notch filter Hl(z) and the adaptive IIR notch filter HN(z), which can detect and mitigate CWI. In this model, the ANF is used for estimating the Jamming-to-Signal Ratio (JSR) and the frequency of the interference (w(0)) by using an LMS-based algorithm. The depth of the notch is then adjusted based on the estimation of the JSR. In contrast, the ANF HN(z) is used to mitigate the CW interference. Simulation results show that the proposed ANF is an effective method for eliminating/reducing the effects of MCWI, and yields better system performance than full suppression (kN=1) for low JSR values, and mostly the same performance for high JSR values. Moreover, the proposed can detect low and high JSR and track hopping frequency interference and provides better Bit error ratio (BER) performance compared to the case without an IIR notch filter.

Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard

The abundance of radio signals and their increasing number creates interferences on adjacent signals and sometimes, with co-channel communication. Jammers, which are operated by hackers or by military forces, are another source of smart and powerful interferences. This paper will discuss the effect of the continuous wave interference (CWI) on a radio communication receiver, specifically with the Digital Video Broadcasting for Satellite Second Generation (DVB-S2) communication standard. It investigates the general effect of the interference on a Quadrature Phase Shift Keying (QPSK) signal over each part of the DVB-S2 receiver. It also focuses on the impact of the center frequency and power of the interference on the critical blocks of a DVB-S2 receiver. This study also tries to determine the deviation from the normal operation in the format of mathematical expressions and simulation results. Based on the obtained results, there is a vulnerability in the chain of the receiver’s blocks that allows a smart jammer to affect the device with low power interference. The notch filter is utilized as a solution to mitigate the interference. In addition, the effects of this technique on the system’s performance are studied. The simulation results show that there is a great improvement after CWI removal according to the Jamming to Signal Ratio (JSR), the Signal-to-Noise Ratio (SNR), and the Bit Error Rate (BER). In some cases, the JSR was reduced by 15 dB, the SNR was improved by 10 dB and BER also improved by 7 dB. However, the notch filter deletes some information from the original signal. This study introduces new ways to clarify the tradeoff between the amount of interference power reduction and removed bandwidth from the signal with notch filtering.

GNSS jamming detection of UAV ground control station using random matrix theory

Global navigation satellite systems (GNSS) are the main navigation and control systems in unmanned aerial vehicles (UAVs) and their ground control stations. Without the GNSS signals, the UAV and its ground control stations cannot follow the waypoints of the desired path in jamming environments. In this paper, two new methods for detection of GNSS signal jamming attack for UAV ground control station are proposed based on random matrix theory. By using limiting distribution of mean vector and asymptotic behavior of the defined test statistic, a hypothesis test is introduced and evaluated to detect presence of jamming signal. Simulation results show that the proposed methods have significant performance in terms of detection and false alarm probabilities. Compared to existing methods, at low jamming-to-signal ratio (JSR), more than 2.5 dB improvement is achieved.

DOA Elevation and Azimuth Angles Estimation of GPS Jamming Signals Using Fast Orthogonal Search

This article introduces a new two-dimensional direction of arrival (DOA) elevation and azimuth angles estimation technique for global positioning system (GPS) jamming signals in challenging environments based on the fast orthogonal search (FOS) method. FOS-DOA estimation is accommodated to process array structure optimized for interference rejection. Performance of FOS-DOA is compared to the predominant multiple signal classification (MUSIC) DOA estimation method. Results showed significant improvement in jamming detection of the multiple sources of interference with slight variations in their amplitude at jamming to signal ratio (JSR) 15 and 45 dB. The improvement introduced by the proposed DOA estimation technique is mainly in the accuracy of detecting the number of jammers and their DOA.