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Jamming recognition is a prerequisite for radar antijamming and actual radar deception jamming recognition; however, there is a problem of insufficient samples. To address this issue, we propose a multimodal radar active deception jamming recognition method based on small samples in this paper. This method is based on two modal information—feature parameters and time-frequency images extracted from radar signals—and utilizes prototype networks to train multimodal features. Furthermore, the model adopts the image denoising method and weighted Euclidean distance to improve the recognition performance at low signal-to-noise ratios. Thus, radar deception jamming recognition can be achieved under small sample conditions. Simulation results reveal that the proposed method achieves an average recognition accuracy of over 97% across 10 types of radar deception jamming when the jamming-to-signal ratio is 3 dB. Moreover, the test results from the simulator data verify the good generalization performance of the proposed method.

Due to the range dependence and time-varying array factor of Frequency Diverse Array (FDA) radar, it can overcome the miss of range variable in traditional phased-array factor and gain loss of Multiple-Input Multiple-Output (MIMO) radar array. In recent years, FDA radar techniques have attracted more and more attention of researches and institutions. Nevertheless, there are still many open problems to be solved in FDA radar system theory, signal processing and application implementation. In this overviewing paper, we introduced the FDA concepts, motivation and extending techniques. The latest research advances on FDA radars and their applications are comprehensively reviewed, and the typical application prospects of FDA in jamming radar and radar anti-jamming, ambiguous clutter suppression and blind velocity target detection together with localization deception are discussed. Finally, several key research problems that need to be solved in future work are pointed out.

When Frequency Diverse Array and Multiple-Input Multiple-Output (FDA-MIMO) radar detects moving targets, the frequency offset between transmitting arrays is coupled with the target velocity, resulting in severe Doppler spread in the slow time dimension. Further, the signal energy of each receiving channel cannot be coherently accumulated, thereby greatly reducing the detection performance of the system. To address this problem, a method of FDA-MIMO radar moving target detection based on Doppler spread compensation is proposed. Firstly, the moving target echo model of FDA-MIMO radar is established, and the Doppler spread caused by the frequency offset is analyzed. After establishing the maximum likelihood receiver model, a resampling method based on an interpolation filter is proposed to compensate for the Doppler spread caused by FDA-MIMO radar during the detection of moving targets. Simulation results reveal that the proposed method can suppress the Doppler spread, compensate for the cross-cell migration of subtarget echo in the range dimension, and realize the coherent accumulation of signal energy.

The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly. Thus, this study proposed a localization method via multi-dictionaries and hierarchical block sparse Bayesian framework. Herein, the emitter location problem is transformed into recovering signals from different dictionaries but with shared sparsity, and the emitter location with channel attenuation is solved by a multi-dictionary combination. Simulation results revealed that the algorithm has better performance than the traditional Sparse Bayesian Learning (SBL) method and Direct Position Determination (DPD) method under the condition of low signal-to-noise ratio and a few snapshots.

Conventional multitarget-tracking data association algorithms must have prior information, such as the target motion model and clutter density. However, such prior information cannot be obtained timely and accurately before tracking. To address this issue, a data association algorithm for multitarget tracking based on a transformer network is proposed. First, considering that the radar may not perform accurate detected the target, virtual measurements are performed to re-establish the data association model. Thus, a data association method based on the transformer network is proposed to solve the matching problem of multitargets and multimeasurements. Moreover, a loss function combining Masked Cross entropy loss and Dice (MCD) loss is designed to optimize the network parameters. Simulation data and real measurement data results show that the proposed algorithm outperforms classic data association algorithms and algorithms based on bidirectional long short-term memory network under varying detection probability conditions.

Passive localization technology is an integral part of electronic warfare. However, most methods for countering passive localization systems, such as radio frequency stealth and electronic interference, have limitations. This paper proposes a new passive localization countermeasure method based on Frequency Diverse Array (FDA). The unique beam scanning property reduces dwell time at a certain azimuth direction, making it difficult for a passive localization system to intercept FDA signals for a long time. In contrast, the time-varying characteristics of the FDA considerably reduce the signal-to-noise ratio of the received signal, increasing the difficulty in accurately detecting the localization information of the radiation source. Thus, using this new technology, the electronic system platform can perceive the external environment through the signals radiated by the FDA antenna while deceiving the enemy’s passive localization system. Both theoretical analysis and numerical results showed that FDA transmitted signal achieved significantly better localization countermeasure performance in direction finding by an interferometer, frequency difference of arrival, and time difference of arrival, which are particularly useful for a new generation of electronic systems with reconnaissance detection and passive localization countermeasures capability.

Most of the existing deception jamming techniques focus on designing effective jamming signals for static targets or scenes. This paper proposes a multi-scene deception jamming against synthetic aperture radar (SAR) imaging with frequency diverse array (FDA) antenna. Different from traditional single-element and phased-array antennas, FDA antenna employs a small frequency offset across the array elements to produce angle-, range- and even time-dependent transmit beampattern, which brings bright prospect for the application of new jamming techniques against SAR imaging. Such a new method will generate many false targets or multiple deception scenes for the jammed SAR, which helps jam better on the static scene and moving target. Moreover, the location and number of false targets can be manipulated. This paper will illustrate the FDA-based deception jamming against SAR imaging, specially designed waveform and the relationship between the number of false targets or scenes and FDA parameters. The relationship between the location of false targets and the position of jammer will also be described in detail. All proposed methods are validated by extensive simulation results.

Inspired by recent advances in frequency diverse array (FDA) and multiple-input-multiple-output (MIMO) radars, this paper investigates the target detection performance of FDA-MIMO radar, which jointly utilizes the advantages of FDA radar and MIMO radar, for single- and multiple- coherent pulses, respectively. Firstly, the signal models in target detection are formulated for FDA-MIMO, standard FDA, phased-array and MIMO radars, respectively. We present a unified framework detector to comparatively analyze their target detection performance in the Neyman-Pearson sense. Furthermore, the deflection coefficient and signal-to-interference-plus-noise ratio (SINR) are also adopted as a performance metric to compare their performance. Numerical results show that FDA-MIMO radar achieves better target detection performance as compared to other conventional radar systems, specially when multiple coherent pulses are utilized.

In conventional synthetic aperture radar (SAR) processing algorithms, it is generally assumed that the platform performs a uniform linear motion. However, if there is a three-dimensional acceleration during the flight, the motion trajectory will greatly increase the image processing complexity, especially in highly squinted scenarios. This paper proposes a high-precision imaging algorithm for highly squinted SAR with 3D acceleration. Firstly, the range history is extended by the Taylor series, and then a two-dimensional non-uniform fast Fourier transform (2D-NUFFT) is presented to focus the SAR image in range velocity domain. Moreover, as terrain matching is required in practice, the range-velocity image is further transformed into range-azimuth domain. The effectiveness of the proposed algorithm for highly squinted SAR imaging is verified with both simulation data for multi-point targets and experimental data for surface targets.

Compared with ground radars, space-borne bistatic radar shows great advantages in higher signal-to-noise ratio (SNR) and robuster security. Firstly, this study establishes space-borne geosynchronous satellite-low orbiting satellite (GEO-LEO) space–time two-dimensional model considering the influence of the rotation of the earth together with the curvature of the earth surface. Then the Doppler trajectory distance dependence of the clutter angle is analysed. Finally, the characteristics of the space-borne GEO-LEO model are proved to be beneficial to clutter suppression and ground-moving targets indication. Simulation results show that the space-borne GEO-LEO model has good performance.

Phased-array antennas are known for their capability to electronically steer a beam with high effectiveness, but beam steering is fixed in an angle for all range cells. Different from that, frequency diverse array (FDA) offers a range-angle-dependent beam-pattern. The use of a small frequency increment across its array elements to generate an array factor that is a function of the angle, time, and range, allowing the FDA antenna to form focusing beampattern to the desired range and angle region. Aiming to resolve the problem that traditional phased-array radars can be easy detected and localised by undesired reconnaissance receiver, the use of FDA instead of phased-array provides potential radio frequency (RF) stealth capability. Here, the authors analyse the RF localisation deception performance under interferometry reconnaissance for undesired receivers, which RF stealth capability is validated by the numerical results.

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current passive radar imaging techniques. However, a large rotation angle demands a long observation time, which cannot be implemented for actual passive radar system. To overcome this disadvantage, this paper proposes a super-resolution passive radar imaging framework with a sparsity-inducing compressed sensing (CS) technique, which allows for fewer IOs and a smaller rotation angle. In the proposed imaging framework, the sparsity-based passive radar imaging is modeled mathematically, and the spatial frequencies and amplitudes of different scatterers on the target are recovered by the log-sum penalty function-based CS reconstruction algorithm. In doing so, a super-resolution passive radar imagery is obtained by the frequency searching approach. Simulation results not only validate that the proposed method outperforms existing super-resolution algorithms, such as ESPRIT and RELAX, especially in the cases with low signal-to-noise ratio (SNR) and limited number of measurements, but also have shown that our proposed method can perform robust reconstruction no matter if the target is on grid or not.