Low Probability Of Intercept Research Articles

Radar signal modulation identification using global context vision transformer

Abstract The accurate identification of phase-coded radar waveforms is critical in electronic warfare (EW) systems, particularly with the increasing use of low probability of intercept (LPI) radars. However, current methods struggle to reliably recognize these waveforms at low signal-to-noise ratios (SNRs). To address this challenge, we propose an AI-based Global Context Vision Transformer (GC-ViT) model that leverages short-time Fourier transform (STFT) phase spectrum for feature extraction. The GC-ViT model enhances recognition accuracy by incorporating both local and global self-attention mechanisms, enabling more effective identification of phase-coded signals in noisy environments. Experimental results demonstrate that the proposed method achieves approximately 80% recognition accuracy at an SNR of −12 dB, which significantly outperforms existing techniques. This advancement in radar waveform recognition enhances the situational awareness and decision-making capability of EW systems in complex electromagnetic environments.

LPI radar waveform design based on complementary phase and discrete chaotic frequency joint coding

In order to reduce the probability of radar radiation signals being detected by enemy passive detection systems, this paper proposes a phase and frequency joint coding low intercept Radar waveform design method. Based on the linear frequency modulation signal, this method uses complementary binary code and chaotic sequence to phase encode the intra-pulse modulation. Code and frequency coding. The numerical simulation results show that the designed waveform exhibits pseudo-random characteristics in the time-frequency domain, and the low recognition performance is improved; The signal has an extremely low peak sidelobe level after matched filtering, showing excellent low intercept performance; its three-dimensional ambiguity function diagram presents an ideal “graph”. Fishing type”, with good distance, speed resolution and anti-interference characteristics.

Joint LPI waveform and passive beamforming design for FDA-MIMO-DFRC systems

Dual-functional Radar-Communication (DFRC) systems have been recognized as one of the most promising technologies in the field of wireless communications. Nevertheless, the low probability of intercept (LPI) performance in the DFRC systems cannot be overlooked. Based on the frequency diverse array multiple-input–multiple output (FDA-MIMO) radar, a DFRC system is proposed to enhance target detection in clutter environment and achieve desirable LPI against an underlying hostile interceptor. The issue can be cast into an optimization problem that maximizes the radar signal-to-interference-plus-noise ratio (SINR) while satisfying the communication quality-of-service (QoS) requirement of each user under one of three metrics, the required LPI performance and the constant-modulus waveform constraint. To solve this challenging problem, we reformulate it into an equivalent but more tractable form by resorting to an auxiliary variable. Subsequently, we employ the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) algorithms to solve the resultant problem. Simulation results validate that the proposed LPI FDA-MIMO-DFRC scheme exhibits superior sensing performance over the conventional scheme with MIMO.

Low probability of interception radar overlapping signal modulation recognition based on an improved you-only-look-once version 8 network

Low probability of interception (LPI) radar is widely used in modern electronic warfare. With the increased radiation sources, multiple signals will arrive simultaneously. The traditional feature extraction method has too many features, which brings great trouble to the subsequent data processing. Most modulation recognition methods based on deep learning only consider the single signal after preprocessing, and the generalization ability is weak. This paper proposes a deep learning solution based on an improved you only look once version 8 (YOLOv8) network with a global attention mechanism (GAM), achieving recognition accuracy over 98% in a -10 dB signal-to-noise ratio (SNR) scenario to address the above problems. We improved the performance of the original network, which can be used in electronic countermeasures.

A SIAR transmitting waveform design utilizing positive and negative random carrier frequency coding technique for coupling reduction in circular array

The Synthetic Impulse and Aperture Radar (SIAR) is an advanced radar system which utilizes different orthogonal carrier frequencies. As a four-dimensional (4-D) radar, SIAR offers numerous advantages such as protection against anti-radiation missiles (ARM), detection of stealth targets, low probability of interception (LPI), and tracking of multiple targets. However, one of the key challenges facing these radars is the coupling between range and angle measurements, where errors in range measurements can impact angle measurements and vice versa. To tackle this issue, radar designers commonly develop transmitting waveforms based on carrier frequency coding to minimize the coupling. Various methods, including random frequency coding, sequential frequency coding, and positive and negative sequential frequency coding, have been proposed to reduce the coupling between range and angle measurements for two-dimensional (2-D) linear arrays. This article focuses on extending these ideas to circular arrays (3D radar) and introduces a method known as positive and negative random carrier frequency coding. We take advantage of random code and the concept of positive-negative frequency coding. Additionally, mathematical equations and simulations demonstrate that the coupling between azimuth angle and elevation angle is independent of the frequency coding type and remains consistent across all conditions. Through our research, we demonstrate that the proposed method effectively reduces coupling by approximately 40% more than other frequency coding methods. In the proposed method, we combine output of two consecutive periods by an approach similar to what happens in pulse integration in radar, so we don't require complex computations or new hardware. This method has another advantage such as pulse-to-pulse frequency agility, enhancing protection against ARM through virtual displacement of transmitters. Circular arrangements ensure simultaneous coverage of the entire space.

LPI-based resource allocation strategy for multiple targets tracking in CMIMO radar system with array division

Considering the scenario of multiple targets equipped with interceptors in the modern electronic warfare environment, an LPI-based Resource Allocation Strategy (LPIRAS) for multiple targets tracking (MTT) in collocated MIMO (CMIMO) radar is proposed in this paper, where subarray number is exploited in the low probability of intercept (LPI) optimization problem for the first time. As the problem is formulated as a nonconvex and nonlinear optimization problem, the array power, target-beam assignment, subarray number, and subarray-beam assignment are corporately managed through a methodology incorporating the two-stage search algorithm (TSA), permutation-based target swarm generation algorithm (PTSGA) and adaptive extra subarray assignment algorithm (AESAA), resulting the LPIRAS. Finally, numerical simulations show the proposed strategy is effective and real-time. In comparison to other benchmarks, the LPIRAS achieves superior LPI performance.

LPI radar waveform recognition model based on multiple feature image and quasi-residual attention module

Abstract Detecting and classifying the modulation type of the intercepted noisy LPI radar waveform has become a hot topic in the field of Electronic Countermeasures (ECM). In this paper, we propose a recognition model based on multiple feature images (MFI) and convolutional neural networks (CNN) with the quasi-residual attention module (QRAM). The core technologies of this model are divided into two parts. One is the MFI that combines the ambiguity function image (AFI) of the complete signal, covariance matrix image (CMI) of the reconstructed signal, and short-time Fourier transform image (STFTI) of the truncated signal into RGB color image in signal processing as the input for recognition network. The other is CNN with a QRAM recognition network, in which the input is sliced into two mappings called identity mapping and residual mapping to build the quasi-residual module, and the attention module is embedded in each mapping to denoise and enhance the feature. The performance of the MFI-QRAM model is demonstrated by recognizing 12 modulation types of the signals defined in this paper. The simulation experiments show that the model has strong robustness with the number of train sets and the variation of SNR. The overall probability of successful recognition (PSR) is 96.17% when the SNR is −6 dB.

Artificial Intelligence applications in Noise Radar Technology

AbstractRadar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use.

Improving physical layer security in distributed multiple‐input multiple‐output dual‐function radar‐communication systems

AbstractA distributed multiple‐input multiple‐output (MIMO) dual‐function radar‐communication (D‐MIMO DFRC) system is composed of multiple distributed dual‐function transmitters, multiple radar receivers and multiple communication receivers, which is capable of performing communication and radar tasks simultaneously. In a DFRC system, the goal is on optimising both the sum ‐rate in communication receivers and detection/localisation performance in radar receivers. The secrecy rate is maximised in D‐MIMO DFRC systems by decreasing the eavesdropper data rate as much as possible with a two‐step antenna selection method while maintaining optimal radar performance. In the first step of the proposed method, all transmitter antennas have been classified into groups based on their distance from each other, and each group is called a cluster. Then, a cluster of distributed transmitter antennas is selected based on path fading effects. In the second step of this method, the antenna selection algorithm is performed in the pre‐selected cluster based on channel capacity information utilising QR decomposition. The results show that this antenna selection method, along with low computational complexity and high performance, leads to the maximisation of the secrecy rate. In DFRC systems, it is desirable to minimise the total transmit power while satisfying system requirements to provide low probability of interception (LPI). Finally, after antenna selection, a power allocation strategy is also applied on the selected antennas to optimise the total transmit power and to maximise throughput in communication radar receivers simultaneously, and as a result it leads to provide LPI.

LPI Sequences Optimization Method against Summation Detector Based on FFT Filter Bank

Waveform design is a crucial factor in electronic surveillance (ES) systems. In this paper, we introduce an algorithm that designs a low probability of intercept (LPI) radar waveform. Our approach directly minimizes the detection probability of summation detectors based on FFT filter banks. The algorithm is derived from the general quadratic optimization framework, which inherits the monotonic properties of such methods. To expedite overall convergence, we have integrated acceleration schemes based on the squared iterative method (SQUAREM). Additionally, the proposed algorithm can be executed through fast Fourier transform (FFT) operations, enhancing computational efficiency. With some modifications, the algorithm can be adjusted to incorporate spectral constraints, increasing its flexibility. Numerical experiments indicate that our proposed algorithm outperforms existing ones in terms of both intercept properties and computational complexity.

LPI radar waveform recognition based on semi-supervised model all mean teacher

Low probability of intercept (LPI) radar signal identification plays an important role in electronic warfare, but most existing algorithms are proposed under the condition of sufficient samples, ignoring the problem of a small amount of labeled data in the actual electromagnetic environment. To solve the problem, in this paper, a semi-supervised learning model All Mean Teacher (AMT) based on Mean Teacher (MT) is proposed. First, the LPI radar signal is transformed into Time-frequency images (TFIs) by using the Choi-Williams distribution, and Random Erasing is used for TFIs which improves the generalization ability of the model. Then the Multi-headed Self-Attention Network (MSA-Net) is aimed to extract features, combined with AMT to realize the automatic waveform recognition of radar signals. MSA-Net facilitates feature information propagation by computing contrast costs on TFIs between the student and teacher networks. It solves the problem that TFIs are not easy to train for small amounts of labeled data, improving the accuracy of signal recognition in semi-supervised learning scenarios. Experimental results show that the average recognition accuracy of the proposed method is up to 85.7% at a signal-to-noise ratio of -8 dB.

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications.

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than -10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.

Joint design of the transmit and receive beamforming via ADMM for LPI radar

Joint design of the transmit and receive beamforming via ADMM for LPI radar

On cyclic frequency sidelobe and range sidelobe reduction for LPI radar waveform

On cyclic frequency sidelobe and range sidelobe reduction for LPI radar waveform

Recurrent Waveform Optimization for Desired Range-Doppler Profile With Low Probability of Interception: A Particle Filter Approach

Recurrent Waveform Optimization for Desired Range-Doppler Profile With Low Probability of Interception: A Particle Filter Approach

Bi-directional extraordinary absorption, transmission multifunctional metamaterial based on circular metal-VO2 composites

Multifunctional metamaterials play a crucial role in advancing terahertz technology. In this paper, a multifunctional metamaterial composed solely of circular metal-vanadium dioxide (VO2) is designed and numerically demonstrated. The device comprises a top layer of circular VO2 ring, two polyimide dielectric layers, with two metal layers etched and filled with VO2, and a bottom embedded silver (Ag) circular ring dielectric layer. The metamaterial serves as a broadband and narrowband absorptive or transmissive substrate, utilizing the phase transition of VO2 between its metal and dielectric states. When VO2 is in its metal phase, the top circular VO2 ring periodic array functions as a broadband absorber, exhibiting a relative absorption bandwidth of approximately 93%. Meanwhile, the bottom circular Ag ring periodic array functions as a narrowband absorber and exhibits high sensitivity. In contrast, when VO2 is in the insulating phase, the metamaterial acts as a broadband transmittance substrate, exhibiting a maximum transmittance greater than 85%. This proposed metamaterial holds tremendous potential for utilization across various applications within the terahertz band, spanning from terahertz stealth radar to tunable biosensors and photodetectors.

Detection of Signals with Chaotic Varying Forms and Low Intercept Probability

Low probability of intercept performance of direct-sequence spread-spectrum system with chaotic spreading sequences is investigated. The energy detectors, synchronous and asynchronous, coherent and non-coherent structures are studied here to detect the presence of chaotic direct-sequence spread-spectrum signals. Simple detection approach using a binary correlation function to detect nonbinary chaotic sequences is proposed. The expressions of detection probabilities of chaotic spreading signals are derived. Comparisons between systems using chaotic and binary sequences are given in terms of the low probability of intercept performance, and the performance improvement with chaotic spreading sequences is observed.

Reinforcement Learning-Enhanced Adaption of Signal Power and Modulation for LPI Radar System

Reinforcement Learning-Enhanced Adaption of Signal Power and Modulation for LPI Radar System

Wideband LPI Radar Subpulse Waveform Design, Processing and Analysis

Wideband LPI Radar Subpulse Waveform Design, Processing and Analysis

Recognition of LPI Radar Signal Intrapulse Modulation Based on CNN and Time-Frequency Denoising

Recognition of LPI Radar Signal Intrapulse Modulation Based on CNN and Time-Frequency Denoising