Time-modulated metasurfaces (TMMs) attached to target surfaces can generate range-extended deceptive false targets by periodically modulating incident radar signals in the fast-time domain. As an open-loop jamming technique, TMM-based deceptive jamming operates without knowledge of the radar pulse repetition interval (PRI). This lack of real-time PRI awareness enables the radar to intentionally control the azimuthal displacement of the false targets by selecting a PRI that is not an integer multiple of the TMM modulation period. Exploiting this inherent vulnerability, this article proposes a jamming suppression method for inverse synthetic aperture radar (ISAR) systems based on optimized dual-PRI design and differential processing. The method first estimates the TMM modulation parameters from an initial ISAR image acquired under a baseline PRI. An optimized PRI is then designed to maximize the azimuthal separation of false target clusters between adjacent coherent processing intervals (CPIs). By alternating the PRI between the baseline and optimized values across CPIs, the radar acquires a sequence of ISAR images in which false targets shift position while the true target remains stationary. A multi-differential processing method is subsequently applied to this image sequence to suppress the false targets effectively. The feasibility and effectiveness of the proposed method are validated through extensive simulations using a 330-point aircraft model and controlled measurements in a microwave anechoic chamber. The experimental results confirm that the method achieves robust suppression of TMM-based deceptive jamming under both simulated and real-world conditions, effectively eliminating false targets while preserving the true target structure. Notably, successful suppression is demonstrated in both unambiguous and aliased ISAR imaging scenarios, validating the potential practical applicability of the approach.

Noise convolution jamming (NCJ) can achieve partial pulse compression gain, thus producing a stronger jamming effect on the radar than traditional noise-like jamming. Particularly at high jamming power levels, NCJ can significantly degrade the radar sensitivity, posing a severe threat to the detection performance. Based on the analysis of the NCJ principle, we propose a method for suppressing NCJ via the joint design of the waveform set and filter set. By considering both the cross-correlation and auto-correlation objective functions, we formulate the joint design problem under the constraints of constant modulus waveforms and constant filter energy. To solve this joint design problem, a Riemannian product manifold is constructed by exploiting the characteristics of the feasible region, and the Riemannian gradient of the objective function is derived. Furthermore, based on the limited-memory Riemannian Broyden–Fletcher–Goldfarb–Shanno (LRBFGS) opti mization framework, an algorithm for the joint design problem is developed on the established product manifold. The proposed algorithm can simultaneously update the waveform set and the filter set in each iteration with low computational complexity. The validity of the proposed algorithm is confirmed through simulations. Compared with existing algorithms, the proposed algorithm exhibits higher computational efficiency, and the designed waveform set achieves better jamming suppression performance in different signal-to-jamming ratio scenarios.

High-resolution range profile (HRRP) reconstruction is essential for extracting range-direction scattering characteristics in wideband radar remote sensing, particularly in synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging. Coded interrupted sampling (CIS) can improve radar low probability of intercept (LPI) performance by controlling signal transmission with a binary sequence. However, the reduced number of valid echo samples may degrade HRRP reconstruction, especially under low-duty-ratio and low signal-to-noise ratio (SNR) conditions. Conventional orthogonal matching pursuit (OMP) processes each frame independently and ignores the inter-frame continuity of scattering-center positions, which may lead to false selections and missed detections. To address this problem, this paper proposes a candidate-interval-assisted orthogonal matching pursuit (CI-OMP) algorithm based on multi-frame sequential priors. Stable scattering-center positions are extracted from historical reconstruction results and expanded into candidate intervals to guide atom matching in the current frame. Simulation results show that CI-OMP outperforms standard OMP in terms of normalized mean squared error (NMSE), tolerant support recovery rate (Tol-SRR), and peak-to-sidelobe ratio (PSLR). At a duty ratio of 0.20, CI-OMP reduces the NMSE by 1.71 dB and improves the PSLR by 7.56 dB compared with OMP. In addition, the candidate-interval strategy reduces the atom-search range by approximately 54–75% under different duty ratios and by approximately 50–83% under different SNRs, demonstrating improved search efficiency. These results demonstrate that CI-OMP improves the accuracy, robustness, and search efficiency of HRRP reconstruction for CIS radar echoes, particularly under low-duty-ratio and low-to-medium-SNR conditions.

Noise convolution jamming can produce stronger jamming effects than conventional noise jamming under the same jamming power, reducing radar sensitivity and creating significant difficulties for radar target detection. To suppress noise convolution jamming, a joint design method of transmitted waveforms and receiving filters is proposed in this paper. By utilizing the weighted integrated sidelobe level (WISL) metric, peak penalty function, and weighted integrated level (WIL) metric to control the pulse compression performance and the jamming suppression performance, a joint transmit-receive design problem is established under the constraints of constant modulus waveform and constant filter energy. To solve this non-convex optimization problem, we develop an efficient alternating iterative algorithm based on the majorization-minimization (MM) optimization framework. Furthermore, an acceleration scheme is adopted to accelerate the algorithm. Simulations are conducted to demonstrate the effectiveness of the proposed method.

The proliferation of space objects necessitates efficient multitarget monitoring under constrained radar resources. This article proposes a novel two-stage inverse synthetic aperture radar (ISAR) imaging framework that synergizes full-aperture (FA) characterization with sparse-aperture (SA) compressed sensing (CS). The method operates through two cascaded stages: in the initial stage, multiple space targets are independently processed through FA radar operations to generate baseline imagery via fast Fourier transform (FFT) techniques. This stage also involves extracting the signal support domain and creating a weighting function. The subsequent stage implements SA imaging through a weighted 2-D orthogonal matching pursuit (2D-OMP) algorithm, where the derived weighting matrices impose solution space constraints during iterative reconstruction. This constraint reduces the scope of the solution space, thereby enhancing the image quality of the reconstruction result and improving the robustness of imaging. Rigorous validation through two distinct satellite models quantitatively demonstrates the efficacy of the method. Simulation experiments have revealed that even at a sparsity level of 20%, the reconstruction algorithm enhanced with support domain weighting is capable of producing imaging outcomes of considerable quality, with a 2-D correlation coefficient (2D-Corr) metric of 40%. This demonstrates that the proposed method can successfully reconstruct images for at least four targets simultaneously while maintaining high image quality.

Time-coding metasurfaces (TCMs) have emerged as a promising approach for radar target feature modulation (RTFM), attracting significant attention in recent years. However, existing methods often encounter a tradeoff between system complexity and modulation flexibility, with limited multidimensional control. Based on the linear discrete harmonic generation characteristics of periodic modulation TCMs, this article establishes a grid distribution model of false targets in the range-Doppler (RD) domain. A phase-modulated reflector (PMR) system is further proposed to achieve high-degree-of-freedom joint modulation of target range and Doppler features, while maintaining system complexity comparable to that of 1-bit TCMs. The core of the system lies in periodic pseudorandom coding, which supports flexible RD feature modulation. By integrating a genetic algorithm (GA) with a suitable fitness function, the system enables customized harmonic generation, thereby redistributing real target energy over the RD grid to synthesize false target peaks at specified locations. Leveraging the multireflection and retro-reflection properties of corner reflectors, the system can be configured in either 1- or 2-bit phase modulation mode to generate symmetrically or asymmetrically distributed false targets in the RD domain, while preserving low system complexity. The proposed RTFM method is validated through both simulation and practical radar experiments, demonstrating its potential for diverse application scenarios.

Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets. A low sidelobe waveform design method based on complementary amplitude coding (CAC) is proposed in this paper, which can be used to reduce the sidelobe level of multiple waveforms. First, the CAC model is constructed. Then, the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR). Finally, genetic algorithm (GA) is used to solve the optimization problem to get the best CAC waveforms. Simulations and experiments are conducted to verify the effectiveness of the proposed method.

The time-varying phase-switched screen (PSS) has demonstrated a significant potential for modulating radar target features, due to its exceptional capabilities in the harmonic generation and control. However, most existing studies focus on single-dimensional target features, with limited investigation into joint modulation of 2-D or higher dimensional features. In this article, a time-domain digital coding PSS system is proposed. By applying 1-bit periodic phase modulation, the spectral energy of the incident electromagnetic (EM) wave is redistributed across multiple harmonics, thereby simultaneously influencing the detection of range and velocity features by linear frequency modulated (LFM) pulse-Doppler (PD) radar. Through the precise control of the energy and frequency-domain distribution of these harmonics, radar false targets exhibiting coupled range-velocity features can be synthesized in the feature space. Leveraging this coupling characteristic, the method enables flexible feature modulation, including EM cloaking, range modulation, Doppler modulation, and range-Doppler (RD) 2-D joint modulation. Simulations demonstrate the effectiveness of the approach, revealing that the primary false targets remain detectable even at low signal-to-noise ratios (SNRs), despite a reduction in the number of effective false targets. Finally, a practical countermeasure experiment conducted in a microwave anechoic chamber further validates the feasibility and effectiveness of the proposed method.

To address the challenges of high bit error rate (BER) and false target peaks in high-resolution range profile (HRRP) within dual-functional radar-communication (DFRC) systems, this study proposes innovative processing methods for linear frequency modulation (LFM) waveform modulated by amplitude codes, alternatively referred to as LFM-ASK waveform. The characteristics of the LFM-ASK waveform are investigated. For communication function, a reference signal is constructed based on the segment summation of the LFM signal, and the amplitude codes are recovered by sampling the reference signal's matched filter output at each code beginning and applying a threshold-based decision. For radar function, a pair of complement codes is employed to generate two LFM-ASK signals. The same order false target peaks in the two HRRPs of the echoes have opposite phases, and the false targets can be effectively eliminated by summing the two HRRPs. Comprehensive simulations and experiments have been conducted, demonstrating the effectiveness of the proposed processing methods. The results indicate that the proposed demodulation method decreases the BER by 36 % and 16 % compared to envelope demodulation and coherent demodulation at 0 dB signal-to-noise ratio (SNR). Additionally, the proposed elimination method not only suppresses signal-induced false targets but also reduces the number of noise-induced false targets from over 3 to 0 compared to the compressive sensing method.

Micromotion frequency is one of the key features to distinguish space targets from decoys. The radar echoes scattered from multiple space targets overlapped in the time and frequency domain, which brings challenges to the micromotion frequency estimation. In this article, the concept of the range-cadence-Doppler (RCD) spectrum is defined based on high-resolution range profiles and cadence velocity diagrams. Then, a multitarget micromotion frequency estimation algorithm based on the RCD spectrum is proposed. Compared with traditional algorithms, the RCD algorithm is suitable for both narrowband and wideband situations and does not need to preset the number of targets and thresholds. Consequently, it has better robustness. Computational efficiency is also better due to no iterative operations. Finally, the performance of the RCD algorithm is verified by the anechoic chamber measurement data.

The slice repeater jamming (SRJ) is coherent with the transmitted waveform and can produce many false targets after pulse compression, making it difficult for radars to detect real targets. To suppress the SRJ, an SRJ recognition and suppression method based on the coded waveform is proposed in this article. First, the design principle of the coded waveform is introduced, and the SRJ characteristics and target characteristics of the coded waveform are revealed by theoretical derivation. By adjusting the coded waveform parameters, the target peaks in the range profile may disappear so that the range profile of the coded waveform is dominated by SRJ. In addition, when subjected to the same SRJ, the positions of false targets in the coded waveform range profile are the same as those in the linear frequency modulation (LFM) waveform range profile, and this property can be used to recognize the false targets in the LFM range profile. Then, based on the relationship between the false targets in the range profiles of the two waveforms, the false targets in the LFM waveform range profile are reconstructed and suppressed. Finally, simulations are conducted to demonstrate that the proposed method is effective against different kinds of SRJ. Compared with the existing methods, the proposed method shows better jamming suppression ability in different signal-to-noise ratio and signal-to-jamming ratio scenarios.

Nonperiodic interrupted sampling repeater jamming (ISRJ) against inverse synthetic aperture radar (ISAR) can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation, which is obviously different from the conventional multi-false-target deception jamming. In this paper, a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory. By utilizing the discontinuous property of the jamming in slow time domain, the unjammed pulse is separated using the intra-pulse energy function difference. Based on this, the two-dimensional orthogonal matching pursuit (2D-OMP) algorithm is proposed. Further, it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence. The validity of the proposed method is demonstrated via the Yake-42 plane data simulations.

A synthetic aperture radar (SAR) image transform method is proposed based on random period amplitude and frequency shifting joint modulation. The random period amplitude sequence is designed to modulate the radar pulse signal so that the sidelobe of range profile is disordered with high level. Based on the spectrum property of random period amplitude sequence, the frequency shifting value can be obtained to conduct the joint modulation. Subsequently, frequency shifting is applied to move the high-level sidelobe of range profile to the position of the target of interest (TOI). As a result, the TOI can be protected and a false target image emerges, which shows both blanket and deception properties. By utilizing the joint modulation in fast time and amplitude modulation in slow time, the image of TOI is covered after SAR imaging. At the same time, the false images are generated in the range and azimuth directions. Finally, simulations and SAR data experiments are conducted to obtain the transformed SAR image with proper amplitude sequence and frequency shifting parameters. The correlation coefficient between the transformed image and the real target image is calculated, which indicates the validity of the proposed method.

This paper proposes a periodic cyclic coding-based interrupted sampling modulation method, providing a novel technical approach for flexible modulation and multi-target simulation of Stepped-Frequency Linear Frequency Modulation (SF-LFM) signals. This approach employs periodic cyclic coding sequences to regulate the interrupted sampling and forwarding processes of sub-pulses, through which controllable timefrequency modulation characteristics are systematically introduced into synthesized wideband signals. The modulated signals are thereby enabled to generate non-uniformly distributed, high-density false characteristics in their modulation patterns, achieved through precisely controlled temporal and spectral manipulations. The effectiveness of the proposed method is validated through simulation experiments.

The proliferation of space objects necessitates efficient multi-target monitoring under constrained radar resources. This paper proposes a novel two-stage inverse synthetic aperture radar (ISAR) imaging framework that synergizes full-aperture (FA) characterization with sparse-aperture (SA) compressive sensing. The method operates through two cascaded stages: In the initial stage, multiple space targets are independently processed through FA radar operations to generate baseline imagery via fast Fourier transform (FFT) techniques. This stage also involves extracting the signal support domain and creating a weighting function. The subsequent stage implements SA imaging through a weighted two-dimensional orthogonal matching pursuit (2D-OMP) algorithm, where the derived weighting matrices impose solution space constraints during iterative reconstruction. This constraint reduces the scope of the solution space, thereby enhancing the image quality of the reconstruction result and improving the robustness of imaging. Rigorous validation through two distinct satellite models quantitatively demonstrates the efficacy of the method. Simulation experiments have revealed that even at a sparsity level of 20%, the reconstruction algorithm enhanced with support domain weighting is capable of producing imaging outcomes of considerable quality, with a two-dimensional correlation coefficient metric of 40%. This demonstrates that the proposed method can successfully reconstruct images for at least four targets simultaneously while maintaining high image quality.

In this article, an inverse synthetic aperture radar (ISAR) image transform method is proposed by utilizing the unique property of joint intrapulse and interpulse periodic-coded phase modulation. By extending the periodic-coded phase modulation to the azimuth dimension, the multiple false targets spread along both the range and azimuth directions, then make the target of interest (TOI) indistinguishable and thus get effectively protected. On this basis, the image transform properties and parameter design principles are further discussed. Finally, both numerical and measured Yak-42 aircraft data simulations are conducted to demonstrate the validity of the proposed method.

Inverse Synthetic Aperture Radar (ISAR) serves as a valuable instrument for surveillance of space targets. There has been a great deal of research on space target identification using ISAR. However, the polarization characteristics of space target components are rarely studied. Polarimetric Inverse Synthetic Aperture Radar (PolISAR) comprises two information dimensions, namely, polarization and image, enabling a more comprehensive understanding of target structures. This paper proposes a space target structure polarization interpretation method based on component decomposition and PolISAR feature extraction. The proposed method divides the target into components at the stage of modeling. Subsequently, electromagnetic calculations are performed for each component. The names of these components are used to label the dataset. Multiple polarization decomposition techniques are applied and many polarization features are obtained. The mapping correlations between the interpreted results and authentic target structures are improved through preferential selection of polarization features. Ultimately, the method is validated through analysis of simulation and anechoic chamber measurement data. The results show that the proposed method exhibits a more intuitive correlation with the authentic target structures compared to traditional polarized interpretation methods based on Cameron decomposition.

In this letter, a synthetic aperture radar (SAR) image transform method is proposed using the periodic-coded phase modulation. By modulating the intercepted radar signal with the periodic-coded phase pulse, multiple false targets can be formed along the range direction in the radar image. Then, the target of interest (TOI) can get protected by the deception jamming effect. On this basis, the jamming property and parameter design principles are further discussed. Finally, the mini-SAR data experiment indicates that multiple false targets approximately from 15 to 30 dB higher than the low-level background are generated in the SAR image and then demonstrates the validity of the proposed image transform method.

Range gate pull-off (RGPO) Jamming disrupts tracking capability of radar system by strategically increasing the latency of radar signal. Hence this kind of active deception jamming is widely adopted. The quantitative assessment and evaluation of RGPO Jamming effects on radar systems is of great significance for enhancing radar anti-jamming capabilities. However, difficulties exist in establishing a mathematical model for analyzing the jamming effects through theoretical analyses. Both the complexity of different radar systems and randomness of radar tracking processes add to this challenge. These factors result in substantial workload to analyze jamming effects. Considering these challenges, a modelling method for RGPO effects on radar based on digital simulation results is introduced in this study. Firstly, the radar range tracking model was established. Through the qualitative analyses of the process of RGPO jamming effect and the principle of affecting the tracking loop, it was found that the rate of a successful pulling off would show a precipitous drop after the pulling speed exceeds a certain threshold. This conclusion was verified in the simulation results of the RGPO effect simulation system. On this basis, to build the relationship between the maximum pull-off speed and the Jamming-to-Signal Ratio (JSR), a modelling method based on spline interpolation and least squares fitting was proposed in this study. The work contributes to a deeper understanding of the impact of RGPO Jamming on radar tracking, and provides valuable insights for bolstering radar countermeasures.