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Under decompression, disordered solids undergo an unjamming transition where they become under-coordinated and lose their structural rigidity. The mechanical and vibrational properties of these materials have been an object of theoretical, numerical, and experimental research for decades. In the study of low-coordination solids, understanding the behavior and physical interpretation of observables that diverge near the transition is of particular importance. Several such quantities are length scales (ξ or l) that characterize the size of excitations, the decay of spatial correlations, the response to perturbations, or the effect of physical constraints in the boundary or bulk of the material. Additionally, the spatial and sample-to-sample fluctuations of macroscopic observables such as contact statistics or elastic moduli diverge approaching unjamming. Here, we discuss important connections between all of these quantities and present numerical results that characterize the scaling properties of sample-to-sample contact and shear modulus fluctuations in ensembles of low-coordination disordered sphere packings and spring networks. Overall, we highlight three distinct scaling regimes and two crossovers in the disorder quantifiers χz and χμ as functions of system size N and proximity to unjamming δz. As we discuss, χX relates to the standard deviation σX of the sample-to-sample distribution of the quantity X (e.g., excess coordination δz or shear modulus μ) for an ensemble of systems. Importantly, χμ has been linked to experimentally accessible quantities that pertain to sound attenuation and the density of vibrational states in glasses. We investigate similarities and differences in the behaviors of χz and χμ near the transition and discuss the implications of our findings on current literature, unifying findings in previous studies. [ABSTRACT FROM AUTHOR]

Physical interaction with patients, for example conducted as part of a diagnostic examination or surgical procedure, provides clinicians with a wealth of information about their condition. Simulating this interaction is of great interest to researchers in both haptics and medical education, and the development of softness changing tactile interfaces is important in recreating the feel of different soft tissues. This paper presents designs for a variety of novel electromechanical and electromagnetic mechanisms for controlling particle jamming-based, hardness changing tactile displays, intended to allow medical trainees to experience these physical interactions in a range of simulation settings such as clinical skills teaching laboratories. Each design is then subjected to a battery of mechanical tests to evaluate its effectiveness compared to the state of the art, as well as their suitability for simulating the physical hardness of different types of soft tissues, previously characterised in established literature. These results demonstrate that all of the technologies presented are able to exhibit a measurable hardness change, with Shore hardness values between 3A and 57A achieved by the most effective constriction-based device. The electromechanical devices based on constriction and compression, and the state-of-the-art pneumatic device, were able to achieve hardness changes within a range that is useful for replicating the softness of organic tissue. The electromechanical and electromagnetic devices were also found to effect their full range of hardness change in less than a second, compared to several seconds for the state-of-the-art. These results show that the performance of softness changing tactile displays can be improved with the electromechanical actuation techniques proposed in this paper, and that such displays are able to replicate the physical characteristics of soft tissues and may therefore be of benefit in medical training and simulation scenarios. [ABSTRACT FROM AUTHOR]

Corner reflector, as a passive jammer, can generate a large radar cross section (RCS) to interfere with radar for target recognition. However, the impact of corner reflectors on passive millimetre-wave (PMMW) recognition is unclear. In this paper, based on ray tracing, the brightness temperature (TB) of corner reflectors is calculated. Ray tracing paths are used to analyze the impact of the sky, sea surface, and sun on the TB of corner reflectors. The antenna temperature of corner reflectors and their array at different distances is simulated and compared with that of a metal ship to evaluate their jamming performance. Four series of experiments measuring the antenna temperature of corner reflectors and the ship are conducted to verify the jamming performance. The results indicate that the contrast of corner reflectors is much smaller than that of ship target, causing little jamming for PMMW recognition. [ABSTRACT FROM AUTHOR]

Regarding the suppression of radar mainlobe jamming, a blind extraction method for suppressing mainlobe jamming based on maximizing the signal to interference plus noise ratio (SINR) is proposed. Unlike blind separation, blind extraction can extract interested components from multiple mixed signals, making it more suitable for jamming suppression in complex electromagnetic environments with multiple sources and channels. This method establishes an optimization model for maximizing SINR in the distance domain. The target echo signal is solved and extracted using the particle swarm optimization (PSO) algorithm to achieve mainlobe jamming suppression. Simulation testing shows that the proposed method improves the jamming suppression effect compared to traditional blind separation jamming suppression methods. It does not require estimation of the number of sources and has lower requirements for the number of channels, making it applicable in underdetermined scenarios. Additionally, it reduces computational complexity, making it more suitable for complex electromagnetic environments. [ABSTRACT FROM AUTHOR]

The integration of radar interference is an effective technical path to optimize radio frequency resources and realize directional interference. Radar interference integrated waveform design is a hot spot in radar interference integrated research. This paper presents an radar interference integrated waveform based on Zadoff-Chu sequence and orthogonal frequency division multiplexing (OFDM) architecture, and studies the waveform design and processing methods. The waveform uses OFDM as the modulation mode and time- frequency division multiplexing as the basic architecture. The frequency domain subcarriers are reasonably allocated and modulated through Zadoff-Chu and Mersenne-Twister sequences to achieve the integration of detection and interference functions. In signal processing, the first step in to separate the time and frequency of interference detection, and the second step in to conduct pulse compression and coherent processing, so as to achieve ranging and velocity measurement. Theoretical analysis and simulation test show that the proposed waveform has the advantages of high ratio of main lobe to side lobe of pulse compression, strong Doppler tolerance, flexible frequency alignment and strong interference randomness. [ABSTRACT FROM AUTHOR]

In this article, we investigate a model‐free adaptive funnel control framework for nonlinear multiagent systems under jamming attacks. An equivalent time‐varying linear data model is built for agents by utilizing the dynamic linearization approach. Then, a predefined funnel function is presented such that the tracking errors are expected to evolve within a predetermined range. The transient and steady‐state behaviors of the tracking error can be improved by adjusting the parameter values of the funnel function. Moreover, the Stackelberg game is employed to determine the optimal transmission power between the transmitters and jammers considering the state of the transmission channel, which influences the jamming attack rate. Based on funnel control, a model‐free adaptive funnel control scheme is presented to guarantee that the tracking error remains within a funnel boundary (even in the presence of jamming attacks) and the transient and steady‐state performances are improved. The effectiveness of the proposed algorithm is further demonstrated through simulations. [ABSTRACT FROM AUTHOR]

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning. [ABSTRACT FROM AUTHOR]

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy of radar active jamming in strong noise environment, an algorithm for ER-C-L(Extended ResNet-CNN-LSTM) network model based on one-dimensional composite features is proposed. Firstly, the amplitude, instantaneous frequency, instantaneous envelope of power spectrum and their composite features are taken as network input to compare their recognition accuracy in ResNet-CNN model. The composite features of amplitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features. Then, the complex features are injammed into the ER-C-L network to identify six new active jamming models. Simulation experiments show that the recognition accuracy of jamming is 98.5% in strong noise environment within the JNR of -10 dB. Compared with other deep learning algorithms such as CNN, ResNet-CNN, extended ResNet-CNN and LSTM, it has higher interference recognition accuracy. [ABSTRACT FROM AUTHOR]

Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi‐realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre‐Decluttering Dual‐Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi‐stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal‐to‐jamming ratio, 0.9952 structure similarity index measure) on the semi‐realistic dataset. Its practical effect is further verified on measured data. [ABSTRACT FROM AUTHOR]

In the increasingly intricate electromagnetic environment, the radar receiver may simultaneously encounter multiple intentional or unintentional jamming signals, which results in temporal and spectral overlap of received signals and forms a composite jamming signal. The nature and extent of interference contained in the received signal are often unknown, while they significantly affect the accuracy of radar detection. AnOpen‐Set Compound Jamming Signal Recognition Framework based on Multi‐Task Multi‐Label (MTML‐OCJR) is proposed. Based on the time–frequency characteristic of compound jamming signals, the proposed framework employs multi‐label classification to identify components of compound jamming signals while incorporating an unknown signal detection task into the classification process. Time–frequency image reconstruction combined with extreme value model estimation is used to detect unknown types of jamming signals, enabling simultaneous signal recognition and anomaly detection. The obtained results show that the proposed approach has superior recognition performance for composite jamming signals in closed‐set environments and high anomaly detection ability for unknown signals in open‐set environments. This method has the potential to significantly enhance the effectiveness and reliability of jamming systems in battlefield scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a study of the impact of different types of waveform jamming schemes against remote detonation communication devices based on Long term evolution (LTE) technology. Two main categories of waveform jamming signals (single-carrier and multi-carriers) were used. Also, two jamming scenarios against LTE-based remote detonation devices (jamming the whole LTE frame and jamming the LTE synchronization channel) were investigated. The key point of evaluating the effectiveness of the presented waveform jamming signals is by measuring their ability to successfully cut off the connection between the transmitter and the LTE-based remote detonation device. This is achieved by verifying that the remote detonation device is unable to successfully receive the transmitted information (Deny of Service (DOS) mode). To this end, emulations of these scenarios are held using two Keysight software-defined radio software programs (SystemVue 2015 and VSA 89600). Finally, a real practical experiment of the impact of different waveform jamming schemes against LTE-based remote detonation devices is emulated to present the optimal waveform jamming scheme against these types of devices. [ABSTRACT FROM AUTHOR]

Herein, a comprehensive review of the evolution of intelligent communication anti‐jamming techniques is provided. First, a clear definition of the concept and elaboration on the inherent connotations and capability characteristics of intelligent communication anti‐jamming is provided. Additionally, the initial construction of an intelligent communication anti‐jamming system architecture is outlined. Subsequently, the development of intelligent communication anti‐jamming is delved, tracing its progression from early‐stage adaptive anti‐jamming techniques to the more recent advancements in intelligent anti‐jamming, which are primarily based on game theory and machine learning. Moreover, the latest research findings in this domain are thoroughly examined and the existing challenges and bottlenecks that hinder current research progress are highlighted. Finally, several viable research directions for future studies in the field of intelligent anti‐jamming are proposed. The objective is to facilitate readers in gaining a comprehensive understanding of the concept, characteristics, and system architecture of intelligent communication anti‐jamming. Additionally, it is aimed to provide a comprehensive overview of the developmental process and current state of research, thereby furnishing conceptual and theoretical support for the design of more effective and practical intelligent communication anti‐jamming systems. [ABSTRACT FROM AUTHOR]

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns. [ABSTRACT FROM AUTHOR]

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset. [ABSTRACT FROM AUTHOR]

Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition performance. Additionally, recognition performance is hindered by limitations in capturing the global features of radar jamming. The Transformer (TR) has advantages in modeling long-range relationships. Therefore, a weakly supervised Transformer is proposed to address the issues of performance degradation under weak supervision. Specifically, complementary label (CL) TR, called RadarCL-TR, is proposed to improve radar jamming recognition accuracy with noisy samples. CL learning and a cleansing module are successively utilized to detect and remove potentially noisy samples. Thus, the adverse influence of noisy samples is mitigated. Additionally, semi-supervised learning (SSL) TR, called RadarSSL-PL-TR, is proposed to boost recognition performance under unlabeled samples via pseudo labels (PLs). Network generalization is improved by training with pseudo-labeling unlabeled samples. Moreover, the RadarSSL-PL-S-TR is proposed to further promote recognition performance, where a selection module identifies reliable pseudo-labeling samples. The experimental results show that the proposed RadarCL-TR and RadarSSL-PL-S-TR outperform comparison methods in recognition accuracy by at least 7.07 % and 6.17 % with noisy and unlabeled samples, respectively. [ABSTRACT FROM AUTHOR]

This study investigates the consequences of unsustainable groundwater extraction in the Kabodarahang plain, a region significantly impacted by geohazards, such as land subsidence and sinkhole formation due to excessive groundwater use for agricultural and industrial activities. Over 32 years (1990–2022), a dramatic decrease in groundwater levels by approximately ~41 m has been observed, leading to notable geohazards. Employing radar interferometry techniques with Sentinel-1 satellite radar imagery and the Sentinel Application Platform (SNAP) tool, complemented by field data, this research aims to quantify the rate of subsidence and evaluate the associated risks, particularly in urban and residential zones. Findings from 2017 to 2018 indicate a subsidence rate of 14.5 cm, predominantly in urban areas, thereby elevating the risk of this geohazard. The results underscore the critical need for sustainable groundwater management policies and practices. The study demonstrates the effectiveness of radar interferometry in monitoring subsidence in the Kabodarahang plain and suggests that integrating such techniques with field surveys and satellite data can enhance the detection and management of risks related to unsustainable groundwater usage. This research contributes to the understanding of the impacts of groundwater depletion on geohazards and supports the development of strategies for sustainable groundwater use to mitigate such risks. [ABSTRACT FROM AUTHOR]

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed- l 0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

In 5G, users can easily enjoy services by accessing the edge devices (EDs) deployed around the base stations. Generally, due to the uneven distribution and mobility of users, one ED is required to serve multiple users simultaneously, that results in the ED overload, seriously affecting the quality of experience (QoE) of the users. By responding to users' requests, service migration realizes the cross edge device migrations, that dynamically provides services for mobile users, which effectively improves the resource utilization rate of edge servers and the QoE of users. However, during the service migrations, malicious users may deploy pseudo base stations, gateways and other devices to illegally eavesdrop or tamper with user service data, that causes user information loss or disclosure. To prevent these security issues, a fountain-coding based cooperative jamming strategy is proposed in this paper. Specifically, the fountain coding technology is introduced to construct a three-node transmission model, including an original node, a target node and a malicious node, to realize the secure data transmission process in service migration. Besides, a group of relay nodes are employed to carry out cooperative jamming, that deteriorate the illegal eavesdropping quality of malicious nodes on service data. Then secrecy rate and outage probability are utilized to evaluate the security and reliability of the whole service migration. Finally, the theoretical analysis is shown by simulation results. [ABSTRACT FROM AUTHOR]

The authors investigate the performance of an original multifunction system with two in‐band full‐duplex capable joint radar, communications and security (JRCS) transceivers in the presence of an eavesdropper. The system concept can be generalised to a network of more than two JRCS transceivers and multiple eavesdroppers, despite the present study focusing on the three‐node scenario. By combining a bandlimited pseudonoise waveform with a data‐containing orthogonal frequency‐division multiplexing (OFDM) waveform, the authors are able to ensure a certain jamming‐to‐signal power ratio (JSR) at the eavesdropper, whilst ideal synchronisation ensures that jamming causes no deterioration to their own data transfer or radar sensing performance as it is possible to remove just the known pseudonoise waveform. To validate the system, the authors investigate through simulations the OFDM symbol error rates of all the receivers, radar target signal‐to‐interference‐plus‐noise power ratios as well as receiver operating characteristic curves, and eavesdropper's detection signal‐to‐noise power ratios through two‐branch receiver cross‐correlation. The results show that already a very low JSR of −20 dB can improve physical‐layer security without a significant increase in friendly symbol error rate or deterioration in radar performance. Additionally, other full‐duplex transceivers potentially occupying the same radio resources improve the secrecy even further. [ABSTRACT FROM AUTHOR]

The increasingly complex electromagnetic environment of modern warfare and the proliferation of intelligent jamming threaten to reduce the survival rate of radio fuzes on the battlefield. Radio frequency (RF) stealth technology can fundamentally improve the anti-interception and reconnaissance capabilities of radio fuzes, thereby lessening the probability of them being intercepted, recognized, and jammed by the enemy. In this paper, an RF stealth waveform based on chaotic pulse-position modulation is proposed for ultra-wideband (UWB) radio fuzes. Adding a perturbation signal based on the Tent map ensures that the chaotic sequences have sufficiently long periods despite hardware byte limitations. Measuring the approximate entropy and sequence period shows that the Tent map with the addition of perturbation signals can maintain good randomness under byte constraints, closely approximating the Tent map with ideal precision. Simulations verify that the proposed chaotic mapping used to modulate the pulse position of an ultra-wideband radio fuze signal results in superior detection, anti-interception, and anti-jamming performance. [ABSTRACT FROM AUTHOR]

We propose a new method to estimate the waveforms of frequency-modulated continuous-wave (FMCW) interferers by intentional interference. The proposed method utilizes the crossing interference of FMCW radar by adaptive waveform configuration. The victim radar analyzes the periodicity and frequency of the interference signal from the mixer at the FMCW receiver. The bandwidth, slope, and intervals of the interferer waveform are derived from multiple adaptive waveforms from interference detection results. The estimated time and frequency waveform parameters of the interferer can be utilized to generate an interference-free waveform. The proposed approach has been tested and validated using two different mmWave commercial off-the-shelf automotive FMCW radars: the AWR2243 and AWR2944 evaluation boards. In three different scenarios in indoor and outdoor environments, the proposed method successfully estimated interferer waveform parameters with 0.9 seconds of monitoring processing and less than 3% error. [ABSTRACT FROM AUTHOR]

Distributed interrupted sampling repeater jamming (D-ISRJ) is the application of interrupted sampling repeater jamming technology within the framework of distributed jamming systems. It can generate coherent false targets after passing through the target radar's matched filter, but these false targets exhibit strong regularity in range and amplitude. Addressing this issue, a distributed interrupted sampling frequency-shifted repeater jamming method based on the group teaching optimization algorithm (GTOA) is proposed in this paper. By introducing frequency-shifted modulation during the retransmission of the jamming signal, the frequency shift amount of the jamming unit in each round of repeater jamming is used as an optimization variable to construct an optimization model for distributed interrupted sampling frequency-shifted repeater jamming. The parameters are then solved by using GTOA. Simulations are conducted to analyze the jamming effects under different distributed jamming modes, and the proposed optimization algorithm is compared to common swarm intelligence algorithms in the same optimization model. The method proposed in this paper can be used in the field of precision electronic warfare to improve the jamming effect of synthetic aperture radar. Experimental results show that under the given simulation conditions, the jamming signal generated by the proposed method can achieve better jamming effects. [ABSTRACT FROM AUTHOR]

Abstract In the increasingly intricate electromagnetic environment, the radar receiver may simultaneously encounter multiple intentional or unintentional jamming signals, which results in temporal and spectral overlap of received signals and forms a composite jamming signal. The nature and extent of interference contained in the received signal are often unknown, while they significantly affect the accuracy of radar detection. AnOpen‐Set Compound Jamming Signal Recognition Framework based on Multi‐Task Multi‐Label (MTML‐OCJR) is proposed. Based on the time–frequency characteristic of compound jamming signals, the proposed framework employs multi‐label classification to identify components of compound jamming signals while incorporating an unknown signal detection task into the classification process. Time–frequency image reconstruction combined with extreme value model estimation is used to detect unknown types of jamming signals, enabling simultaneous signal recognition and anomaly detection. The obtained results show that the proposed approach has superior recognition performance for composite jamming signals in closed‐set environments and high anomaly detection ability for unknown signals in open‐set environments. This method has the potential to significantly enhance the effectiveness and reliability of jamming systems in battlefield scenarios.

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment. [ABSTRACT FROM AUTHOR]

This paper investigates the airborne synthetic aperture radar via outfield experiments based on carrier-free ultra-wideband signals with fifth-order Gaussian pulses with imaging using the backward projection algorithm. Typical blanket jamming is selected in the study, and NAM, NFM, and SFM are used as jamming signals to investigate the backward projection BP imaging effect of airborne synthetic aperture radar when subjected to blanket jamming in a real-world environment. Subsequently, a variational modal decomposition algorithm is proposed, aiming at the anti-jamming processing of airborne synthetic aperture radar. Further, the variational modal decomposition algorithms are compared with the empirical modal decomposition for the anti-jamming effect, which verified the effectiveness and high efficiency of the method. This study provides a new solution for the imaging problem of airborne synthetic aperture radar when subjected to blanket jamming, and a helpful reference is provided for the selection of anti-jamming processing methods. [ABSTRACT FROM AUTHOR]

The corner reflector is an effective means of interference for radar seekers due to its high jamming intensity, wide frequency band, and combat effectiveness ratio. Properly arranging multiple corner reflectors in an array can form dilution jamming that resembles ships, substantially enhancing the interference effect. This results in a significant decline in the precision attack efficiency of radar seekers. Hence, it is critical to accurately identify corner reflector array. The common recognition methods involve extracting features on the high-resolution range profile (HRRP) and polarization domain. However, the former is constrained by the number of corner reflectors, while the latter is affected by the accuracy of polarization measurement, both of which have limited performance on the identification of corner reflector array. In terms of the evident variations in physical structures, there must be differences in their scattering characteristics. To highlight the differences, this paper proposes a new method based on the concept of mismatched filtering, which involves changing the frequency modulation slope of the chirp signal in the filter. Then, the variance of width and intervals within a specific scope are extracted as features to characterize these differences, and an identification process is designed in combination with the support vector machine. The simulation experiments demonstrate that the proposed method exhibits stable discriminative performance and can effectively combat dilution jamming. Its accuracy rate exceeds 0.86 when the signal-to-noise ratio is greater than 0 dB. Compared to the HRRP methods, the recognition accuracy of the proposed algorithm improves 15% in relation to variations in the quantity of corner reflectors. [ABSTRACT FROM AUTHOR]

Reactive jammers select jamming strategies according to the users' responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others' strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes. [ABSTRACT FROM AUTHOR]

This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses. [ABSTRACT FROM AUTHOR]

Frequency agility refers to the rapid variation of the carrier frequency of adjacent pulses, which is an effective radar active antijamming method against frequency spot jamming. Variation patterns of traditional pseudo-random frequency hopping methods are susceptible to analysis and decryption, rendering them ineffective against increasingly sophisticated jamming strategies. Although existing reinforcement learning-based methods can adaptively optimize frequency hopping strategies, they are limited in adapting to the diversity and dynamics of jamming strategies, resulting in poor performance in the face of complex unknown jamming strategies. This paper proposes an AK-MADDPG (Adaptive K-th order history-based Multi-Agent Deep Deterministic Policy Gradient) method for designing frequency hopping strategies in frequency agile radar. Signal pulses within a coherent processing interval are treated as agents, learning to optimize their hopping strategies in the case of unknown jamming strategies. Agents dynamically adjust their carrier frequencies to evade jamming and collaborate with others to enhance antijamming efficacy. This approach exploits cooperative relationships among the pulses, providing additional information for optimized frequency hopping strategies. In addition, an adaptive K-th order history method has been introduced into the algorithm to capture long-term dependencies in sequential data. Simulation results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar's beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods. [ABSTRACT FROM AUTHOR]

Addressing the formidable challenges posed by multiple jammers jamming multiple radars, which arise from spatial discretization, many degrees of freedom, numerous model input parameters, and the complexity of constraints, along with a multi-peaked objective function, this paper proposes a cooperative jamming resource allocation method, based on evolutionary reinforcement learning, that uses joint multi-domain information. Firstly, an adversarial scenario model is established, characterizing the interaction between multiple jammers and radars based on a multi-beam jammer model and a radar detection model. Subsequently, considering real-world scenarios, this paper analyzes the constraints and objective function involved in cooperative jamming resource allocation by multiple jammers. Finally, accounting for the impact of spatial, frequency, and energy domain information on jamming resource allocation, matrices representing spatial condition constraints, jamming beam allocation, and jamming power allocation are formulated to characterize the cooperative jamming resource allocation problem. Based on this foundation, the joint allocation of the jamming beam and jamming power is optimized under the constraints of jamming resources. Through simulation experiments, it was determined that, compared to the dung beetle optimizer (DBO) algorithm and the particle swarm optimization (PSO) algorithm, the proposed evolutionary reinforcement learning algorithm based on DBO and Q-Learning (DBO-QL) offers 3.03% and 6.25% improvements in terms of jamming benefit and 26.33% and 50.26% improvements in terms of optimization success rate, respectively. In terms of algorithm response time, the proposed hybrid DBO-QL algorithm has a response time of 0.11 s, which is 97.35% and 96.57% lower than the response times of the DBO and PSO algorithms, respectively. The results show that the method proposed in this paper has good convergence, stability, and timeliness. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in modern surveillance systems combined with stealth and jamming technologies have led to the development of high-directional and high-gain antennas. This paper presents a compact-sized antipodal Vivaldi antenna (AVA) consisting of an elliptical shaped radiating flare with rectangular slots at the flare edges for modern surveillance systems. The proposed antenna provides improvements in the antenna characteristics, including a higher front-to-back ratio, increased main lobe gain, reduction in the side lobe level (SLL), and lower frequency limit. The results reveal that at 20 GHz, an improved gain of 3.8 dB and a front-to-back ratio of more than 40 dB have been realised. Additionally, in contrast to the antenna without edge slots, the impedance bandwidth, |S11| < −10 dB of 15 GHz, and a reduction in the lower operating frequency by 36.7% have also been achieved. The prototype antenna is developed and tested to verify with simulation results. The proposed antenna with the miniaturised structure may be suitable for surveillance applications in modern radar systems because of its ultrawide fractional bandwidth, high directive gain, and reduced SLL. [ABSTRACT FROM AUTHOR]

Robot grippers based on the jamming of granular material have been studied widely in previous years. Recently, also other benefits and challenges of granular material have emerged for robotics. We discuss various functions of granular matter in robotic actuation, sensory processing, locomotion, and manipulation. We also provide a review of the design and methods of robots for moving in or on challenging granular environments. Drawing on the properties of granular material and their potential applications, we propose our unique perspectives and innovative ideas for future research and development in this field. [ABSTRACT FROM AUTHOR]

Accurate identification of jamming patterns is a crucial decision-making basis for anti-jamming in wireless communication systems. Current works still face challenges in fully considering the substantial influence of environmental noise on identification performance. To address the issue, this paper proposes an automatic threshold denoising-based deep learning model. The proposed method aims to mitigate the impact of noise on recognition performance within the feature space. Considering the challenges posed by nonlinear transformations in deep denoising, a shallow denoising approach based on deep learning is proposed. By constructing a dataset of 12 jamming patterns under noisy conditions, the proposed method exhibits excellent recognition performance and maintains a low computational cost. [ABSTRACT FROM AUTHOR]

Aiming at the quantitative evaluation requirements of radar second-order intermodulation false alarm (SIFA) effect, a radar SIFA effect model is established from the field-circuit coupling mechanism, and the parameter test method of the model is given. Taking a certain type of radar as the test object, the SIFA effect test is carried out by using the method of electromagnetic injection equivalent substitution irradiation. The results show that the tested radar will produce a SIFA signal higher than the selected sensitive level when the frequency difference of dual-frequency electromagnetic interference (EMI) is within 3 MHz and the frequency offset is within ±200 MHz. Using the model parameters of the SIFA interference effect measured in the experiment, it is assumed that they do not change with the interference field strength. Combined with the SIFA interference field strength of the tested radar and the single frequency blocking critical interference field strength, the effect model can evaluate the degree of radar SIFA interference. [ABSTRACT FROM AUTHOR]

Interrupted sampling repeater jamming (ISRJ) is an advanced form of coherent jamming, and the suppression of this jamming has become a critical problem for modern radar electronic countermeasures. In this paper, we propose a countermeasure based on the linear interpulse frequencycoding linear frequency modulation (LIFC-LFM) signal. The LIFC refers to the linear encoding of the frequency of each pulse transmitted by the radar system, which can change the distribution of the false targets formed by ISRJ in the range-Doppler (RD) spectrum. In this context, we design the frequency coding value to effectively separate the true and false targets in the RD spectrum. Furthermore, we propose a fast-time phase compensation method to separate the true and false targets in the Doppler dimension to facilitate false target suppression. Finally, ISRJ can be suppressed by oblique projection processing. Simulation examples demonstrate that the proposed method has an excellent and robust ISRJ suppression effect for direct forwarding ISRJ, repeated forwarding ISRJ, and frequency shifting ISRJ. Meanwhile, the signal-to-noise ratio loss caused by the jamming suppression is small. [ABSTRACT FROM AUTHOR]

The aim of this survey is to limit the largest number of these techniques in one place in the form of tables in order for the researcher to distinguish between them and know the extent of their benefits and disadvantages, as well as in order for the researcher to avoid falling into these defects as much as possible when he makes his own cloned contract detection system. In this paper, we have conducted a comprehensive review of the collection of several techniques for detecting centralized and distributed replication attacks, where nodes can be static or mobile sensor nodes, and tables were made summarizing what was mentioned in these techniques, each according to the results reached by the researchers. A Wireless Sensor Network (WSN) is a system of self-contained sensor nodes that monitor environmental (or physical) parameters with limitations on battery life, memory capacity, and computational power. WSNs are open to several types of attacks due to their use in unmoderated and insecure contexts. Cloning attacks, or (replication attacks), are a type of physical attack. A network adversary can quickly control a single node and collect data from it. Then reprogram it to make a copy of the captured node. Identifying a duplicate node becomes difficult once these clones are spread throughout the network and are accepted as original nodes. A technology or (protocol) must be found that ideally prevents the node from being cloned, as researchers have not been able to create a 100% secure system to prevent the effects of node cloning, which include network traffic monitoring, sensor spoofing, mock data injection, sabotage of data collection, signal jamming, denial-of-service attacks, and disrupting network tasks. Creating a comparison table between techniques for preventing node cloning provides many benefits, including quickly finding the appropriate technique. It is considered a comprehensive and quick-access reference. It facilitates the decision-making process and prevents making mistakes that researchers made previously. It provides visual assistance for analyzing the strengths and weaknesses of each technique in an easier and faster way. The researcher was able to choose the most appropriate technology to develop and improve the quality of its performance to reach the ideal technology in future works. [ABSTRACT FROM AUTHOR]

This study focused on the detection of water inrush in tunnels excavated by full-section hard rock tunnel boring machines (TBMs) and employed ground penetrating radar methods for conducting research on radar signal processing algorithms. The research demonstrates that conventional techniques are inadequate for eliminating the interference of TBM equipment on radar signal propagation. This study employs a radar antenna array method for signal transmission, utilizing a wavelet double-threshold filtering algorithm and wave propagation theory to suppress clutter. These methods exhibit strong signal reception capabilities and are effective in eliminating 13.1% of the direct wave components. The adoption of a novel, efficient radar signal imaging algorithm simplifies the imaging process. Results of verification indicate that the synthetic aperture algorithm, enhanced with cross-correlation calculation, yields the optimal imaging effect. This investigation, which was conducted in conjunction with the construction of a diversion tunnel in a specific region, has confirmed the applicability of the ground penetrating radar method for the detection of water inrush in TBM tunnels by conducting a comparative analysis of the direct wave removal algorithm and the integration of the optimal imaging algorithm. The innovative application of ground penetrating radar within TBM tunnels, along with a targeted technology to mitigate signal interference from metal equipment, has led to the selection of an appropriate algorithm for both signal processing and imaging. This approach offers a novel solution for the detection of water source disasters in TBM tunnels. [ABSTRACT FROM AUTHOR]

Nowadays, modern radar systems increase their target detection capabilities by processing pulses coherently. On the other hand, digital radio frequency memory‐based modern jammers have the ability to work coherently and can deceive radars even with a very low effective radiated power. These jammers, which have the capability of storing the radar's pulse, can use the previous pulses that they have stored in their memory during the electronic deception, without waiting for the last pulse of the radar, in other words, before the new pulse is received. If the radar does not change its parameters from pulse to pulse, such smart jamming techniques applied in this way can be very effective. In this article, the authors propose to use a smart binary phase‐coding method for pulse compression radar as an electronic protection technique against repeater jamming. This approach further improves the target detection capability of modern radar systems, which use coherent integration in the receiver. The proposed method can provide high protection against digital radio frequency memory‐based repetitive range deception techniques without compromising the radar's target detection capability. In the simulations, the traditional approach in which the same code is used without changing from pulse‐to‐pulse, and the approach using code sets obtained by the smart binary phase‐coding method in intra‐pulse modulation are compared. The results show that the proposed method can significantly improve the isolation against deception jamming and the target detection capability simultaneously. [ABSTRACT FROM AUTHOR]

In this article, a resilient model predictive control algorithm incorporating a dual‐mode control strategy is proposed for a class of cyber‐physical systems subject to state and input constraints, additive disturbances and denial‐of‐service (DoS) attacks. An adversary aims to disrupt the controller‐to‐actuator channel by deploying adversarial jamming signals. By exploring the prediction uncertainty between a nominal model and an actual model and utilizing a constraint tightening technique, a novel set of constraints is developed, which guarantees robust constraint satisfaction in open‐loop mode caused by DoS attacks. A robust positive invariant set is designed, which allows the control mode to switch from rolling optimization to a state‐feedback control law when the state enters its interior, thus saving computational resources. We explicitly determine a critical duration for DoS attacks and, through theoretical analysis, establish that when the duration of DoS attacks falls below this critical value, the proposed algorithm ensures guaranteed recursive feasibility and robust asymptotic stability. Finally, some comparisons are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Multi‐dimensional modulation of electromagnetic (EM) waves is critical in various fields such as electronic and photonic systems. Space‐time‐coding (STC) digital metasurfaces, an innovative platform for programmable metasurfaces, offer a straightforward and robust solution for spatio‐temporal modulation, thereby enabling EM‐wave manipulations in both space and frequency. Conventional STC schemes rely on periodic temporal modulations that are uniform in space (i.e., with same modulation frequency in all metasurface elements), and therefore do not take advantage of spatio‐temporal coupling effects that can be induced by non‐uniform periodic modulations (i.e., with different modulation frequencies across the metasurface elements). Here, a novel approach involving non‐uniform spatio‐temporal modulation, alongside co‐prime modulation, is introduced. This approach enhances the fundamental principles of STC digital metasurfaces and facilitates the synthesis of angular scattering responses across different frequencies, guided by harmonic coupling conditions. The proposed strategy results in a more even distribution of the scattered intensity in both space and frequency. For validation, an experimental proof‐of‐principle for spatial‐spectral diffuse scattering is presented. The experimental outcomes align closely with theoretical predictions, underscoring the effectiveness of co‐prime modulation in achieving ultralow‐scattering characteristics in STC digital metasurfaces. Moreover, this technique holds promise for applications in secure wireless communications, radar jamming, and complex beamforming. [ABSTRACT FROM AUTHOR]

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input–multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel. [ABSTRACT FROM AUTHOR]

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes. [ABSTRACT FROM AUTHOR]