Low Probability Of Intercept Research Articles

An Improved LPI Radar Waveform Recognition Framework With LDC-Unet and SSR-Loss

Low probability of intercept (LPI) radar signals have been widely used in modern radars due to the advantages of being hardly intercepted by non-cooperative receivers. Therefore, the waveform recognition of LPI radar signals has recently gained increasing attention. In this letter, an improved LPI radar waveform recognition framework with local dense connection U-net and novel loss function is proposed. Specifically, radar signals are transformed into time-frequency images (TFIs) through time-frequency analysis techniques. Then, the local dense connection U-net is proposed to reduce interference of noise and enhance TFIs features. Finally, the recognition task is implemented through a deep convolutional neural network, which is trained with a noise-robust loss function called SSR-loss. Compared with other existing LPI radar waveform recognition frameworks, our method can obtain a significant improvement in recognition accuracy under noise conditions. When signal-to-noise ratio (SNR) is as low as −10 dB, the overall probability of successful recognition on twelve kinds of typical modulated signals can reach up to 91.17%.

Convex Optimization-Based Power Allocation Strategies for Target Localization in Distributed Hybrid Non-Coherent Active-Passive Radar Networks

To more effectively exploit the omnipresent public non-cooperative illuminators of opportunity (IOs) to improve the low probability of intercept (LPI) performance, a power allocation strategy is developed for target localization in distributed hybrid non-coherent active-passive radar networks. The core of the strategy is to optimize each active radar power allocation to minimize the total intercepted power for the hostile intercept receiver in view of power constraints, while achieving the predetermined collaborative target localization accuracy with the assistance of public non-cooperative IOs. Considering the timing uncertainty of public non-cooperative IOs, the localization performance is determined by joint CramrRao lower bound (CRLB) of the target position and the time-of-arrival (TOA) of the direct-path in a distributed passive radar network. Then, the Bayesian CRLB (BCRLB) is derived to evaluate collaborative target localization accuracy, adopted as the optimization criterion for the power allocation strategy. Furthermore, the optimization problem of the power allocation strategy is strictly proved to be a convex optimization, which implies that the global optimal lower bound can be easily obtained. By introducing a semi-definite matrix, the optimization problem is subsequently converted to semi-definite programming (SDP), resolved by the exiting classical algorithm directly. Finally, the simulation results verify the theoretical analysis and the LPI performance of the proposed strategy which outperforms existing strategies.

Estimation of intrapulse modulation parameters of LPI radar under noisy conditions

AbstractLow probability of intercept (LPI) radars utilize specially designed waveforms for intra-pulse modulation and hence LPI radars cannot be easily intercepted by passive receivers. The waveforms include linear frequency modulation, nonlinear frequency modulation, polyphase, and polytime codes. The advantages of LPI radar are wide bandwidth, frequency variability, low power, and the ability to hide their emissions. On the other hand, the main purpose of intercept receiver is to classify and estimate the parameters of the waveforms even when the signals are contaminated with noise. Precise measurement of the parameters will provide necessary information about a threat to the radar so that the electronic attack or electronic warfare support system could take instantaneous counter action against the enemy. In this work, noisy polyphase and polytime coded waveforms are analyzed using cyclostationary (CS) algorithm. To improve the signal quality, the noisy signal is pre-processed using two types of denoising filters. The denoised signal is analyzed using CS techniques and the coefficients of spectral correlation density are computed. With this method, modulation parameters of nine types of waveforms up to −12 dB signal-to-noise ratio with an accuracy of better than 95% are extracted. When compared with literature values, it is found that the results are superior.

X-Band Low-Probability Intercept Marine Radar Antenna Design With Improved Bandwidth and High Isolation

A series-parallel hybrid-fed microstrip array antenna design is presented for X-band low-probability intercept (LPI) marine radars. Contrary to traditional counterparts, the proposed antenna has improved impedance and radiation bandwidth from 9.1 to 9.6 GHz to accommodate pulse compression and frequency diversity for LPI radars. The antenna structure consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$24\times4$ </tex-math></inline-formula> patch elements with horizontal polarization. The two halves of the structure are fed in parallel and each arm is fed in a series-series feed network to achieve low sidelobe level (SLL) of −26.5 dB. The manufactured antenna has a gain of 24.0 dBi with half-power beamwidth (HPBW) of 4.5° and 21.5° in azimuth and elevation, respectively. The measured gain flatness is ±0.3 dB over the radionavigation band and ±1.0 dB over the entire band. A novel grid baffle structure with capacitive surface impedance is proposed and implemented to provide high isolation over the target bandwidth. The maximum and average isolations between transmit and receive antennas are measured as 65 and 62 dB, respectively. The proposed antenna is very suitable for dual-mode pulsed and continuous-mode LPI marine radars.

Radar signal recognition based on triplet convolutional neural network

Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.

Counter-Interception and Counter-Exploitation Features of Noise Radar Technology

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.

LFM/PRBC Modulation Signal Parameter Estimation

Since pseudo-code phase modulation and carrier frequency modulation composite reconnaissance signals have both the good anti-interference capability and the low probability of interception, they have been widely used in electronic reconnaissance systems. This paper proposes a multi-parameter estimation method for pseudo-random binary phase code (PRBC) and linear frequency modulation (LFM) composite modulation signal in a complex electromagnetic environment. Firstly, this paper uses the square operation to remove the code phase information; secondly, we use the delay correlation and two times frequency estimation to estimate the starting frequency and the frequency modulation rate; finally, the wavelet transform algorithm is utilized to estimate the code rate parameter. Through the simulation analysis and full comparison with other methods, we can prove the performance superiority of this method under low signal-to-noise ratios (SNRs).

Deep learning-based LPI radar signals analysis and identification using a Nyquist Folding Receiver architecture

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

LPI‐based cooperative tactic for satellite communication uplink signal accompanied by directed and power limited jamming

To protect the satellite communication uplink signal (SCUS) from being intercepted by an eavesdropper, this study proposes several tactics to ensure SCUS being in the low probability of intercept (LPI) state. First, based on the satellite link equation, an optimisation model for LPI SCUS is established with coding and synchronisation of symbol and carrier frequency. However, when the satellite is on the synchronisation orbit, the LPI model of SCUS is still very likely to be intercepted by an eavesdropper that might be close to the SCUS. Aiming at this problem, the second tactic is to mask LPI SCUS with directed and power limited jamming. The purpose of using directed and power limited jamming is to reduce direction-finding accuracy of the eavesdropper, and to achieve the optimal LPI performance with the minimum radiation energy. The high-order cumulate algorithm is used to predict the signal-to-noise ratio of mixed signal in the second tactic. The simulation results show that the model of LPI SCUS can effectively reduce the detection probability compared with the traditional satellite uplink signals, and the second tactic can effectively reduce the direction-finding accuracy of the SCUS by the eavesdropper.

Detection of LFM Radar Signals and Chirp Rate Estimation Based on Time-Frequency Rate Distribution.

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.

A New Radar-Embedded Communication Waveform with Low Computational Complexity

Radar-Embedded Communication (REC) is a covert communication technology that utilizes a radio frequency (RF) tag or transponder to embed the communication signal into the radar backscatter. The RF tag must be triggered by the radar signal and quickly generate the required communication waveforms so as to ensure that these communication symbols can be timely and effectively embedded in the radar backscatter pulse. Such an operation poses a great challenge to the response speed of the tag transponder. The traditional method is based on multiple eigenvalue decompositions to generate quasi-orthogonal communication waveforms, which requires a large amount of calculation. This paper proposes a strategy that requires only one eigenvalue decomposition operation. However, such a strategy makes it possible to generate completely orthogonal communication waveforms, and at the same time, it will contribute to a great reduction in the computational pressure of the RF tag. In addition, by simulation analyses, the communication waveform design strategy proposed in this paper does not take the reliability and the low probability of interception (LPI) of the waveform as the cost. On the contrary, under some conditions, communication reliability and LPI characteristics can be improved.

Accurate LPI Radar Waveform Recognition With CWD-TFA for Deep Convolutional Network

Automotive radars, with a widespread emergence in the last decade, have faced various jamming attacks. Utilizing low probability of intercept (LPI) radar waveforms, as one of the essential solutions, demands an accurate waveform recognizer at the intercept receiver. Numerous conventional approaches have been studied for LPI radar waveform recognition, but their performance is inadequate under channel condition deterioration. In this letter, by exploiting deep learning (DL) to capture intrinsic radio characteristics, we propose a convolutional neural network (CNN), namely LPI-Net, for automatic radar waveform recognition. In particular, radar signals are first analyzed by a time-frequency analysis using the Choi-Williams distribution. Subsequently, LPI-Net, primarily consisting of three sophisticated modules, is built to learn the representational features of time-frequency images, in which each module is constructed with a preceding maps collection to gain feature diversity and a skip-connection to maintain informative identity. Simulation results show that LPI-Net achieves the 13-waveform recognition accuracy of over 98% at 0 dB SNR and further performs better than other deep models.

Assessment of RCS-specific SNR and Loglikelihood Function in Detecting Low-observable Targets and Drones Illuminated by a Low Probability of Intercept Radar Operating in Littoral Regions

The objective of this study is to deduce signal-to-noise ratio (SNR) based loglikelihood function involved in detecting low-observable targets (LoTs) including drones Illuminated by a low probability of intercept (LPI) radar operating in littoral regions. Detecting obscure targets and drones and tracking them in near-shore ambient require ascertaining signal-related track-scores determined as a function of radar cross section (RCS) of the target. The stochastic aspects of the RCS depend on non-kinetic features of radar echoes due to target-specific (geometry and material) characteristics; as well as, the associated radar signals signify randomly-implied, kinetic signatures inasmuch as, the spatial aspects of the targets fluctuate significantly as a result of random aspect-angle variations caused by self-maneuvering and/or by remote manipulations (as in drones). Hence, the resulting mean RCS value would decide the SNR and loglikelihood ratio (LR) of radar signals gathered from the echoes and relevant track-scores decide the performance capabilities of the radar. A specific study proposed here thereof refers to developing computationally- tractable algorithm(s) towards detecting and tracking hostile LoTs and/or drones flying at low altitudes over the sea (at a given range, R) in littoral regions by an LPI radar. Estimation of relevant detection-theoretic parameters and decide track-scores in terms of maximum likelihood (ML) estimates are presented and discussed.

Power control and beamforming in passive phased array radars for low probability of interception

In this paper, we investigate low probability interception (LPI) in passive phased array radars. Power control and transmit beamforming (BF) are used to minimize the received power by the interceptor. Meanwhile, desired target detection performances and the limited power budget of radar are guaranteed. The LPI problem is solved analytically and some theorems are derived for the design of optimum BF weights. Two LPI optimization problems from different perspectives are formulated and solved analytically in preparing closed-form solutions for designing transmit BF weights. LPI performance and computation time of the proposed algorithms are compared with that of the benchmark algorithms. Simulation results show that proposed algorithms not only minimize the interception probability of radar in comparison with benchmark algorithms but also satisfy desired detection performance and power budget constraints simultaneously. The proposed algorithms outperform the standard numerical Barrier algorithm in complexity and optimality.

Low-PAPR Waveforms with Shaped Spectrum for Enhanced Low Probability of Intercept Noise Radars

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.

Research on LPI radar signal detection and parameter estimation technology

Modern radar signals mostly use low probability of intercept (LPI) waveforms, which have short pulses in the time domain, multicomponent properties, frequency hopping, combined modulation waveforms and other characteristics, making the detection and estimation of LPI radar signals extremely difficult, and leading to highly required significant research on perception technology in the battlefield environment. This paper proposes a visibility graphs (VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation (TFR). On the one hand, the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms. On the other hand, the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency (IF). Simulation performance shows that, compared with the most advanced methods, the algorithm proposed in this paper has a valuable advantage. Meanwhile, the calculation cost of the algorithm is quite low, and it is achievable in the future battlefield.

Covert wireless communication using massive optical comb channels for deep denoising

Covert wireless communications are unprecedentedly vital for security and privacy of individuals, government, and military bodies. Besides encryption, hiding signal transmission deeply under noise background highly proliferates the covertness in the physical layer. A deep signal hiding leads to a low interception probability at the interceptor but a poor data recovery at the receiver. To ensure both high covertness and high-fidelity recovery, massive and dense optical comb channels are utilized for deep denoising through the analog spectrum convolution. Using an external modulation-based optical frequency comb (OFC) and a single detection branch, the available optical comb channels can sustainably scale up by breaking or greatly mitigating physical bottlenecks on immense hardware and spectrum requirements. Thus, a striking signal-to-noise ratio (SNR) rise can be achieved for deep denoising. Combination of 1024 comb channels (the first parallel comb channel number beyond 1000) and the analog spectrum convolution enable a record SNR enhancement of 29 dB for a microwave signal with a 10.24 GHz bandwidth and a 10 Mbit/s data rate, which is deeply hidden below the in-band noises by 18 dB or even 30 dB in both the frequency and time domains. This method opens a new avenue for covert communications.

LPI Property of FDA Transmitted Signal

Frequency diverse array (FDA) produces an angle range dependent and time-varying transmit beampattern due to the small frequency increment across its array elements, which provides potential applications for secure communications and radio frequency stealth. In this article, we study the low probability of intercept (LPI) properties of FDA transmitted signals under typical passive reconnaissance techniques. First, the passive reconnaissance property of direction-of-arrival estimation for FDA transmitted signals under phase interferometry measurement is evaluated, together with the corresponding Cramér–Rao bound analysis. Second, the passive reconnaissance property of time difference of arrival and frequency difference of arrival estimation for FDA transmitted signals are also evaluated for moving platforms. Both theoretical analysis and numerical results demonstrate that FDA transmitted signal indeed achieves significantly better LPI performance than phased-array transmitted signals, which is particularly useful for military applications.

Joint Design of the Transmit and Receive Beamforming via Alternating Direction Method of Multipliers for LPI of Frequency Diverse Array MIMO Radar in the Presence of Clutter

In view of the limitation of the phased-array on suppressing the range-dependent interference, a joint design of the transmit and receive beamforming via the Alternating Direction Method of Multipliers (ADMM) method for Low Probability of Intercept (LPI) of Multiple-Input Multiple-Output with Frequency Diverse Array (FDA-MIMO) radar in the presence of clutter is proposed. The problem of joint design is to maximize the performance of target parameter estimation, and minimize the transmit energy at the target region which enhances LPI capability. Following a weighted sum of the performance metric, the original problem is firstly recasts to a multiple-ratio Fractional Programming (FP) problem. Subsequently, an iterative algorithm is developed. Concretely, at each iteration, the transmit beamforming matrix is optimized by employing ADMM method and the quadratic approximation algorithm. Moreover, the computational complexity of the proposed algorithm is discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.

Collaborative transmit resource scheduling and waveform selection for target tracking in multistatic radar system

This paper proposes a collaborative transmit resource scheduling and waveform selection (CTRSWS) strategy for target tracking in multistatic radar system which consists of one transmitter and an arbitrary number of receivers. The main mechanism of the proposed CTRSWS strategy is to exploit the optimisation technique to jointly optimise the illumination power, dwell time, waveform bandwidth, and pulse length under the constraints of several resource budgets and the predefined waveform library, aiming at improving the low probability of intercept (LPI) performance and target tracking accuracy of multistatic radar system simultaneously. The analytical expressions for the probability of intercept and the trace of the predicted error covariance matrix corresponding to the target state estimation are derived and adopted to evaluate the LPI performance and target tracking accuracy, respectively. Subsequently, the resulting non-convex and non-linear optimisation problem is resolved by an efficient and fast four-stage solution methodology. Several numerical results are provided to verify the effectiveness and superiority of the proposed CTRSWS scheme in terms of the achievable LPI performance and target tracking accuracy of multistatic radar system.