Noise Jamming Research Articles

Design of a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface

This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non-convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes.

Jamming Effects on Hybrid Multistatic Radar Network Range and Velocity Estimation Errors

This research studies the effects of three noise jamming techniques on the performance of a hybrid multistatic radar network in a selection of different electronic warfare (EW) situations. The performance metrics investigated are the range and velocity estimation errors found using the Cramér-Rao lower bounds (CRLBs). The hybrid multistatic network simulated is comprised of a single active radar transmitter, three illuminators of opportunity (IO), a receiver co-located at the active transmitter site, and two separately located silent receivers. Each IO transmits at a unique frequency band commonly used for civilian applications, including Digital Video Broadcasting-Terrestrial (DVB-T), Digital Audio Broadcasting (DAB), and FM radio. Each receiver is capable of receiving signals at all three IO frequency bands as well as the operating frequency band of the active radar transmitter. The investigations included compare the performance of the network at detecting a single airborne target under conditions where different combinations of jammer type, operating mode, directivity, and number of operating jammers are used. The performance degradation of the system compared to operation in a non-contested environment is determined and a comparison between the performance of the hybrid multistatic radar with that achievable by a monostatic radar and an active-only multistatic radar network within a selection of contested scenarios is made. Results show that the use of spatially distributed nodes and frequency diversity within the system enable greater theoretical functionality in the presence of jamming over conventional radar systems.

Selection of Number of Compensatory Channels and Placement of Receivers with Non-Identical Frequency Response at Phased-Array Radar under Gaussian Noise Jamming

Selection of Number of Compensatory Channels and Placement of Receivers with Non-Identical Frequency Response at Phased-Array Radar under Gaussian Noise Jamming

Artificial Neural Network (ANN) - Bayesian Probability Framework (BPF) based method of dynamic force reconstruction under multi-source uncertainties

In view of the universal existence of multi-source uncertainty factors in engineering structures, a novel method of dynamic force reconstruction is investigated based on Artificial Neural Network (ANN) and Bayesian probability framework (BPF). The deterministic force reconstruction is first solved by the back propagation (BP) ANN, in which the mapping relationship of external forces and displacement responses can be readily obtained through reiterative supervised learning rather than fussy formula deduction. The interval-set model is adopted to characterize uncertain variables and the most potential points in the interval domain (determine the boundaries of dynamic forces) are selected adaptively utilizing the BPF approach. Different from traditional uncertainty propagation methods, the present BPF strategy will construct a surrogate model of uncertain forces using the Bayesian theorem combined with two acquisition functions. The usage of the ANN-BPF based method is demonstrated with three numerical examples: a cantilever beam, a stiffened plate and a missile structure, in which some negative issues related to stiffness degradation, noise jamming and complicated boundary constraints, are discussed as well. In addition, some auxiliary indices are defined to evaluate the effects of reconstructed nominal forces and force boundaries of transient states and overall processes, like peak relative error (PRE), normalized mean squared error (NMSE), mean uncertain level (MUL), etc. The results indicate that the identified curves are all consistent with the real dynamic forces both in magnitude and regularity aspects, and the quantized data obtained by deterministic cases and uncertain cases is measured in reasonable ranges for engineering applications.

Cooperative jamming in downlink satellite network with hardware impairments

AbstractIn this article, a land mobile satellite communication network with an external jammer to fight against the eavesdropper is investigated, where the hardware impairments are introduced to make the analyses more practical. It is worth noting that three jamming schemes, that is, the Gaussian noise jamming, the friendly jamming, and the scheme without jamming, are taken into account to explore the effect of the jammer comprehensively. The secrecy metrics such as the connectivity outage probability (COP), the security outage probability, and the secrecy throughput (ST), are derived for the passive eavesdropping scenario, while the ergodic secrecy capacity is analyzed for the active eavesdropping scenario. Moreover, to get intuitive insight from the closed‐form expressions, the asymptotic ST and the asymptotic COP under the condition of high signal‐to‐noise ratio are also considered. Finally, all the derivations are validated by Monte Carlo simulations, and the effects of different parameters on the performance of the network are analyzed.

An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas.

Design and Experimental Assessment of Detection Schemes for Air Interface Attacks in Adverse Scenarios

In this letter, we propose three schemes designed to detect attacks over the air interface in cellular networks. These decision rules rely on the generalized likelihood ratio test, and are fed by data that can be acquired using common off-the-shelf receivers. In addition to more classical (barrage/smart) noise jamming attacks, we further assess the capability of the proposed schemes to detect the stealthy activation of a rogue base station. The evaluation is carried out through an experimentation of a LTE system concretely reproduced using Software-Defined Radios. Illustrative examples confirm that the proposed schemes can effectively detect air interface threats with high probability.

Anti-jamming approach based on radar transmitted waveform matching

This study proposes a novel approach to suppress noise jamming and smart jamming. The traditional method of using auxiliary channels to cancel interference requires pure interference samples to calculate weights, which is almost impossible for pulsed interference signals. In this work, to avoid the difficulty of choosing suitable interference samples, we construct the parameterized expected signal according to the time-delay relation between target reflecting echo and transmitted signal. The objective function is established in the form of the minimum mean square error between the recovered signal and the expected signal. The optimization problem is solved by an alternating iteration method. Simulation results demonstrate that the proposed method achieves excellent performance for suppressing noise jamming and smart jamming and is not sensitive to signal-to-noise ratio and jamming-to-noise ratio. The processing results of the measured data show that the method has a certain practical application value.

Design and Experimental Assessment of Detection Schemes for Air Interface Attacks in Adverse Scenarios

In this letter, we propose three schemes designed to detect attacks over the air interface in cellular networks. These decision rules rely on the generalized likelihood ratio test, and are fed by data that can be acquired using common off-the-shelf receivers. In addition to more classical (barrage/smart) noise jamming attacks, we further assess the capability of the proposed schemes to detect the stealthy activation of a rogue base station. The evaluation is carried out through an experimentation of a LTE system concretely reproduced using Software-Defined Radios. Illustrative examples confirm that the proposed schemes can effectively detect air interface threats with high probability.

Secure Voice Interactions With Smart Devices

Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeVI</i> , to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generates jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Meanwhile, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the-shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.

Energy Efficient Secure Communication Model against Cooperative Eavesdropper

In a wiretap channel system model, the jammer node adopts the energy-harvesting signal as artificial noise (jamming signal) against the cooperative eavesdroppers. There are two eavesdroppers in the wiretap channel: eavesdropper E1 is located near the transmitter and eavesdropper E2 is located near the jammer. The eavesdroppers are equipped with multiple antennas and employ the iterative block decision feedback equalization decoder to estimate the received signal, i.e., information signal at E1 and jamming signal at E2. It is assumed that E1 has the channel state information (CSI) of the channel between transmitter and E1, and similarly, E2 has the CSI of channel between jammer and E2. The eavesdroppers establish communication link between them and cooperate with each other to reduce the information signal interference at E2 and jamming signal interference at E1. The performance of decoders depends on the signal to interference plus noise ratio (SINR) of the received signal. The power of information signal is fixed and the power of the jamming signal is adjusted to improve the SINR of the received signal. This research work is solely focused on optimizing the jamming signal power to degrade the performance of cooperative eavesdroppers. The jamming signal power is optimized for the given operating SINR with the support of simulated results. The jamming signal power optimization leads to better energy conservation and degrades the performance of eavesdroppers.

Помехоустойчивость передачи цифровой информации в системе радиосвязи сигналами ДОФТ с ППРЧ при воздействии шумовой помехи в части полосы.

The article deals with an algorithm for noncoherent reception of signals with Differential Quadrature Phase Shift Keying (DQPSK) using their weight processing for radio communication systems (RCS) with Frequency-hopping spread spectrum (FHSS) under Partial-band jamming noise (concentrated in the interference spectrum). Numerical calculations of bit/symbol error probability in the reception of DQPSK signals with a symbol-by-symbol frequency hopping are presented, as well as the results of modelling the considered reception algorithm for a radio communication system with an intra-symbol frequency hopping to study the noise immunity of receiving a DPQSK signal in the considered mode. It is shown that for not too large signal-to-jamming ratios when only a part of the operating frequencies is clogged by this noise, the noncoherent reception algorithm with weight processing provides higher noise immunity than the symbol-by-symbol frequency-hopping algorithm.

LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

Deep neural networks are used as effective methods for the Low Probability of Intercept (LPI) radar waveform recognition. However, existing models’ performance degrades seriously at low Signal-to-Noise Ratios (SNRs) because the effective features extracted by the networks are insufficient under noise jamming. In this paper, we propose a multi-resolution deep feature fusion method for LPI radar waveform recognition. First, we apply the enhanced Fourier-based Synchrosqueezing Transform (FSST), which shows good performance at low SNRs, to convert radar signals into time-frequency images. Then, we construct a multi-resolution deep convolutional network to extract more deep features from each resolution channel. Next, we explore an interactive feature fusion strategy for deep feature fusion. By some down-sampling or up-sampling blocks, different resolution features are fused to generate new features. Finally, we apply a fusion algorithm to the fully connected layer to achieve classification fusion for better performance. Simulation experiments on twelve kinds of LPI radar waveforms show that the overall recognition accuracy of our method can reach 95.2% at the SNR of −8 dB. It is proved that our approach does indeed improve the recognition accuracy effectively at low SNRs.

Q-Learning-Based Power Allocation for Secure Wireless Communication in UAV-Aided Relay Network

Unmanned aerial vehicle (UAV)-aided wireless relay networks are at risk of eavesdropping activities due to their open nature. In this paper, we study the security of a UAV-aided selective relaying wireless network in which $N$ UAVs are employed as decode-and-forward (DF) relays linking a ground base station (BS) with $L$ legitimate users on the ground in the presence of a passive eavesdropper ( $Eave$ ). Direct links between the ground BS and both the ground users and the eavesdropper are assumed to be blocked. The ground-to-air and air-to-ground channels are assumed to follow Rician fading model with opportunistic scheduling scheme for UAVs and users selection. In order to secure data transmissions against such an interception action, the UAV of the worst UAV-selected user link transmits a jamming artificial noise (AN) signal to degrade $Eave$ ability in decoding the confidential information successfully. The transmission outage probability, intercept probability, and hybrid outage probability are derived and analyzed. Due to the heavy computation burden raised by increasing the number of UAVs and users as well as the difficulty in estimating the instantaneous channel state information (CSI), existing traditional optimization methods are not highly efficient in solving the considered power allocation problem. Therefore, we propose a dynamic power control scheme based on Q-learning algorithm combined with statistical CSI where the hybrid outage probability is minimized. Simulation results show that the proposed algorithm efficiently reduces the hybrid outage probability with a noticeable reduction in the computational time.

An evolutionary band-limited Gaussian noise jamming algorithm for LMS-based GPS

In this paper, evolutionary algorithm is used to optimize the flicker function of band-limited Gaussian noise interference, which disturbs the convergence of adaptive algorithm based on LMS in GPS receiver, so that the average null depth of GPS receiver under equal power is lower, and better jamming effect is achieved. The simulation results show that the jamming algorithm in this paper has better jamming effect than the band-limited Gaussian noise jamming in different jamming quantity.

Physical resilience to insider attacks in IoT networks: Independent cryptographically secure sequences for DSSS anti-jamming

Wireless communication is a key technology for the Internet of Things (IoT). Due to its open nature, the physical layer of wireless systems is a high-priority target for an adversary whose goal is to disrupt the normal behavior of the system. In particular, jamming attacks are one of the most straightforward and effective types of attacks: information flow of the system is stopped or severely disturbed. In this paper, we propose a method to improve the jamming resilience of IoT systems based on Direct-Sequence Spread-Spectrum (DSSS) techniques. Our proposal is inspired by the Moving Target Defense (MTD) paradigm. MTD strategies randomize components of a system, increasing the effort an attacker needs to compromise the system. We use state-of-the-art Cryptographically Secure Pseudo-Random Number Generators outputs as spreading sequences for DSSS. The sequences of the proposed system are generated in an ad-hoc, independent, and distributed way. We show probabilistically that the generated sequences have robust cross-correlation properties. We define a multi-user system model to evaluate the Bit-Error-Rate of our proposal in the presence of two types of jammers: a classical band-limited Gaussian noise jammer, and an insider smart jammer with knowledge of one spreading sequence used in the system. Our proposal proactively mitigates the insider jammer attack. We quantify the insider smart jammer resilience of a system implementing our proposal, as a function of the length of the spreading sequences and the jammer power.

Эффективность применения помехоустойчивого кодирования в системах передачи цифровой информации с широкополосными сигналами.

The method of transmitting information using broadband signals is recognized as an effective way to combat various types of organized intentional and unintentional interference. Among them, methods for transmitting digital information with frequency-hopping spread spectrum (FHSS) have found wide application. Due to its frequency and energy efficiency, the transmission method with frequency hopping is widely used in information transmission systems for various purposes, including military radio communication systems (RCS), where high interference immunity is required. Both when transmitting digital information under the conditions of the destructive effect of electronic suppression means, and without this effect, the received data sequence contains errors. To detect and correct them, noise-correcting coding is effective. The article considers the case when at the input of the receiver, in addition to its own noise, there is also a deliberate concentrated partial-band noise jamming, occupied by the RCS. An analysis is made of the noise immunity of a RCS with a frequency hopping and DBPSK with the combined use of error-correcting coding and time-frequency diversity of the code symbol. The application in the analyzed RCS of the algorithm of incoherent reception of diversity signals with weight processing and subsequent decoding is considered. It’s shown that the noise immunity of the RCS under the considered conditions turns out to be significantly higher than the RCS, in which the frequency diversity of the code symbol and the noise-immune coding are not used.

Radar Surveillance of Unmanned Aerial Vehicles (Review)

Radar surveillance of unmanned aerial vehicles (UAVs) is actively developing area of scientific research. This article provides a review and analysis of publications in recent years devoted to the methods and radar systems of detection and recognition of classes and types of UAVs. It is noted that the most difficult targets for radar detection are low-sized, low-speed small UAVs (drones) flying at low and extremely low altitudes. If large and medium-sized UAVs can be detected by modern radar systems, then for the detection of small UAVs it is advisable to create specialized highly efficient, highly mobile, portable and inexpensive active UAV detection radars. The technical requirements for such radars are defined and recommendations for their implementation are provided. High-performance protection systems based on adaptive lattice filters are offered to protect UAV detection radars from noise jamming and passive interference. It is shown that the research on the methods of recognizing UAV classes and types is a development of the existing theory and technology of radar recognition of air targets.

Robust artificial noise-aided beamforming for a secure MISO-NOMA visible light communication system

Visible light communication (VLC) and non-orthogonal multiple access (NOMA) have been deemed two promising techniques in the next wireless communication networks. In this paper, secure communications in the presence of potential eavesdropper are investigated for a multiple-input single-output VLC system with NOMA. The artificial noise jamming and beamforming technologies are applied to improve secure performance. A robust resource allocation scheme is proposed to minimize the total transmit power taking into account the constraints on the quality of service requirement of the desired users and the maximum tolerable data rate of the eavesdropper, and the practical imperfect channel state information of both the desired users and the eavesdropper. The formulated non-convex optimization problem is tackled based on S-Procedure and semi-definite programming relaxation. Simulation results illustrate that our proposed resource allocation scheme can effectively guarantee communication security and achieve transmit power saving. Moreover, the height and number of LED can significantly affect system performance and the optimum LED height can be obtained for different LED numbers.

JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals

As electromagnetic environments in battlefields are more and more complex, there are more kinds of suppression jamming noise including both single jamming signals and compound jamming signals. Thus, suppression jamming recognition especially for compound jamming signals is becoming vital and challenging. Whereas conventional methods are prone to owning the low recognition accuracy and the high computational complexity, especially under low jamming-to-noise ratio (JNR) conditions. In this paper, a novel jamming recognition network (JRNet) based on robust power-spectrum features is proposed to recognize ten kinds of suppression jamming signals including four single jamming patterns and six compound jamming patterns. The proposed method combines the significant representative power of the JRNet and distinguished power-spectrum features of jamming signals to promote the recognition performance. By integrating residual blocks and asymmetric convolution blocks, the JRNet is capable to address the degradation problem and enhance the recognition ability for subtle features. The simulation results show that the overall recognition accuracy of the proposed method is more than 90% even when the JNR is −18 dB and is close to 100% at −6 dB. Compared with five comparison methods in recent literatures, the proposed JRNet achieves better and stable recognition performance especially under low JNR conditions with relatively less storage source and a bit more FLOPs and inference time.