Search Results (25)

A deep reinforcement learning (DRL)-based power allocation method is proposed to achieve a low probability of intercept (LPI) in a netted synthetic aperture radar (SAR). To provide a physically meaningful and intuitive assessment of a netted radar for LPI performance, a netted circular equivalent vulnerable radius (NCEVR) is proposed and adopted. For SAR detection performance, the resolution, signal-to-noise ratio in a single pulse, and signal-to-noise ratio in SAR imaging are integrated at the task level. The LPI performance is achieved by minimizing NCEVR within the constraints of SAR detection performance. The powers in multiple moments are optimized using the DRL proximal policy optimization algorithm with the designed reward and observation. A DRL-based solver is provided for LPI radar, which handles problems that are difficult to optimize using traditional methods. The effectiveness is verified by simulations. Full article

For radar system tracking, a higher radar echo signal to interference and noise ratio (SINR) implies a higher tracking accuracy. However, in a countermeasures environment, increasing the transmit power of a radar may not lead to a higher SINR due to suppressive jamming. Also, the variation in the target radar cross-section (RCS) is an important factor affecting the SINR, since to achieve the same SINR value, a large RCS value needs less transmit power and a small RCS value needs more transmit power. Therefore, to design an efficient power allocation strategy, the influence of the electronic jamming and the target RCS need to be jointly considered. In this paper, we propose an adaptive interacting multiple power (AIMP)-based power allocation algorithm for radar network tracking by jointly considering the electronic jamming and the target RCS, achieving better anti-jamming capability and lower probability of intercept (LPI) while not reducing the tracking accuracy. Firstly, the model of the radar network tracking is established, and the power allocation problem is formulated. Next, the target RCS prediction algorithm is introduced, and the AIMP power allocation method is proposed jointly considering the electronic jamming and the impact of the target RCS. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposals in this paper. Full article

Low probability of intercept (LPI) radar signal recognition under low signal-to-noise ratio (SNR) is a challenging task within electronic reconnaissance systems, particularly when faced with scarce labeled data and limited resources. In this paper, we introduce an LPI radar signal recognition method based on a semi-supervised Support Vector Machine (SVM). First, we utilize the Multi-Synchrosqueezing Transform (MSST) to obtain the time–frequency images of radar signals and undergo the necessary preprocessing operations. Then, the image features are extracted via Discrete Wavelet Transform (DWT), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are input into the semi-supervised SVM to complete the classification and recognition of LPI radar signals. The experimental results demonstrate that the proposed method achieves high recognition accuracy at low SNR. When the SNR is −6 dB, its recognition accuracy reaches almost 100%. Full article

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. Full article

Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%. Full article

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models. Full article

Coherent frequency-agile radar (FAR) has a low probability of intercept (LPI) and excellent performance of electronic counter-countermeasures (ECCM) and electromagnetic compatibility, which can improve radar cooperation and survivability in complex electromagnetic environments. However, due to the nonlinearity of radar carrier frequency and the limitation of the Doppler tolerance of high-resolution range cells, the undesirable blind-speed sidelobes are generated in the two-dimensional (2D) range–velocity plane after coherent integration (CI) using the traditional methods based on a matching filter, which may degrade the target detection performance. To solve this problem, an adaptive step-size sparsity adaptive matching pursuit (SAMP) algorithm combining off-grid correction (ASSAMP-OC) is proposed in this paper, which seeks to achieve a better trade-off between recovery efficiency and detection performance. Firstly, an adaptive iteration step size based on the Spearman correlation coefficients (SCCS) is devised, which solves the problem of the traditional SAMP algorithm being insensitive to the change in iteration step size when the residuals vary slightly, and improves the recovery speed. Secondly, the off-grid correction method by combining a regularized stagewise backtracking idea and gradient descent optimization (GDO) is adopted to improve the recovery accuracy and suppress the blind-speed sidelobe energy (BSSE), which helps to reduce CI gain loss and improve the target detection performance without the prior information of the sparsity lever. Finally, simulation and experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of target detection probability, target signal energy ratio after recovery, and computational cost, compared to several existing methods. Full article

The utilization of intermittent sampling jamming can engender a lofty verisimilitude false target cluster that exhibits coherence with the transmitted signal. Such an assemblage bears the hallmarks of both suppression jamming and deceitful jamming, capable of inflicting substantial impairment upon the radar, potentially leading to its profound incapacitation. Henceforth, the precise discernment of the target and various forms of intermittent sampling jamming emerges as a novel endeavor. In response to this predicament, this paper posits a pulsed radar waveform featuring intra-pulse random orthogonal frequency modulation (FM) and inter-pulse phase coherence. This innovative approach not only presents formidable challenges for the jammer in acquiring radar waveform parameters, but also bolsters the radar’s low probability of intercept (LPI), while maintaining the phase coherence of sub-pulses between pulses. Furthermore, based on this waveform, the characteristics of the intermittent sampling jamming signal and its differences from the target echo signal are analyzed in the time domain, frequency domain, time-frequency domain, and pulse compression domain. Building upon these findings, this paper proposes the sub-division algorithms for typical types of intermittent sampling jamming under this waveform: the full-pulses multi-level maximum inter-class variance and sub-pulses multi-level maximum inter-class variance anti-intermittent sampling jamming algorithms. Simulation analysis demonstrates that this waveform and the anti-jamming algorithms can accurately identify and effectively counteract different types of intermittent sampling jamming in typical scenarios. Full article

Since the passive sensor has the property that it does not radiate signals, the use of passive sensors for target tracking is beneficial to improve the low probability of intercept (LPI) performance of the combat platform. However, for the high-maneuvering targets, its motion mode is unknown in advance, so the passive target tracking algorithm using a fixed motion model or interactive multi-model cannot match the actual motion mode of the maneuvering target. In order to solve the problem of low tracking accuracy caused by the unknown motion model of high-maneuvering targets, this paper firstly proposes a state transition matrix update-based extended Kalman filter (STMU-EKF) passive tracking algorithm. In this algorithm, the multi-feature fusion-based trajectory clustering is proposed to estimate the target state, and the state transition matrix is updated according to the estimated value of the motion model and the observation value of multi-station passive sensors. On this basis, considering that only using passive sensors for target tracking cannot often meet the requirements of high target tracking accuracy, this paper introduces active radar for indirect radiation and proposes a multi-sensor collaborative management model based on trajectory clustering. The model performs the optimal allocation of active radar and passive sensors by judging the accumulated errors of the eigenvalue of the error covariance matrix and makes the decision to update the state transition matrix according to the magnitude of the fluctuation parameter of the error difference between the prediction value and the observation value. The simulation results verify that the proposed multi-sensor collaborative target tracking algorithm can effectively improve the high-maneuvering target tracking accuracy to satisfy the radar’s LPI performance. Full article

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB. Full article

In defense applications, the main features of radars are the Low Probability of Intercept (LPI) and the Low Probability of Exploitation (LPE). The counterpart uses more and more capable intercept receivers and signal processors thanks to the ongoing technological progress. Noise Radar Technology (NRT) is probably a very effective answer to the increasing demand for operational LPI/LPE radars. The design and selection of the radiated waveforms, while respecting the prescribed spectrum occupancy, has to comply with the contrasting requirements of LPI/LPE and of a favorable shape of the ambiguity function. Information theory seems to be a “technologically agnostic” tool to attempt to quantify the LPI/LPE capability of noise waveforms with little, or absent, a priori knowledge of the means and the strategies used by the counterpart. An information theoretical analysis can lead to practical results in the design and selection of NRT waveforms. Full article

Linear frequency-modulated (LFM) signals are the most significant example of waveform used in low probability of intercept (LPI) radars, synthetic aperture radars and modern communication systems. Thus, interception and parameter estimation of the signals is one of the challenges in Electronic Support (ES) systems. The methods, which are widely used to accomplish this task are mainly based on transformations from time to time-frequency domain, which concentrate the energy of signals along an instantaneous frequency (IF) line. The most popular examples of such transforms are the short time Fourier transform (STFT) and Wigner-Ville distribution (WVD). However, for LFM waveforms, methods that concentrate signal energy along a line in the time-frequency rate domain may allow to obtain better detection and estimation performance. This type of transformation can be obtained using the cubic phase (CP) function (CPF). In the paper, the detection of LFM waveform and its chirp rate (CR) parameter estimation based on the extended forms of the standard CPF is proposed. The CPF was originally introduced for instantaneous frequency rate (IFR) estimation for quadratic frequency modulated (QFM) signals i.e., cubic phase signals. Summation or multiplication operations on time cross-sections of the CPF allow to formulate the extended forms of the CPF. Based on these forms, detection test statistics and the estimation procedure of LFM signal parameters have been proposed. The widely known estimation methods assure satisfying accuracy for high SNR levels, but for low SNRs the reliable estimation is a challenge. The proposed approach based on joint analysis of detection and estimation characteristics allows to increase the reliability of chirp rate estimates for low SNRs. The results of Monte-Carlo simulation investigations on LFM signal detection and chirp rate estimation evaluated by the mean squared error (MSE) obtained by the proposed methods with comparisons to the Cramer-Rao lower bound (CRLB) are presented. Full article

Noise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a random waveform such as bandpass Gaussian noise as a probing signal provides limited radar performance. After a concise review of a particular noise radar architecture and related correlation processing, this paper justifies the rationale for having synthetic (tailored) noise waveforms and proposes the Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR) algorithm, which can be utilized for synthesizing noise-like sequences with a Taylor-shaped spectrum under correlation sidelobe level constraints and assigned Peak-to-Average-Power-Ratio (PAPR). Additionally, the Spectral Kurtosis measure is proposed to evaluate the LPI property of waveforms, and experimental results from field trials are reported. Full article

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB. Full article

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB. Full article