Recently, interrupted sampling repeater jamming (ISRJ) based on digital radio frequency memory (DRFM), which could easily form high-power false targets, has severely impacted radar detection capabilities. Conventional anti-ISRJ techniques ignore the operating characteristics of the jammer. Once the anti-ISRJ algorithm and the features of the jammer are mismatched, undesirable jamming residues or signal loss appear. In order to avoid these unwanted performance losses, an algorithm jointing the jam perception and the adaptive waveform is developed in this paper. First, a bi-directional Constant False Alarm Rate (CFAR) detector is introduced to locate and estimate the jamming component in radar echo. An ISRJ feature vector is then built to reconstruct and cancel jamming components for accurate detection under ISRJ. Second, an adaptive anti-ISRJ waveform is developed based on the vector, leading the DRFM to retransmit signals orthogonal to the real detection signal. Besides, the particle swarm optimization (PSO) algorithm efficiently works out the optimal waveform parameters to improve the suppression performance. As radar switches to the adaptive anti-ISRJ waveform, long-term suppression of ISRJ is achieved without locating, estimating, and suppressing the ISRJ components in each echo. Simulated experiments demonstrate that the suggested algorithm can accurately detect radar targets under ISRJ.

Frequency shift jamming (FSJ) significantly challenges the detection performance of radar with the linear frequency modulation (LFM) waveform, since jamming signals keep LFM coherence with only frequency shift modulation. In this paper, an adaptive time-frequency filtering algorithm is developed, which exploits the different time-frequency relationships between FSJ signals and true targets to construct the adaptive suppression filters. The difference in the filtering energy loss ratio is measured by clustering technique to determine the FSJ targets. Extensive simulation experiments show that the proposal performs effectively and stably for FSJ suppression.

Frequency-agile (FA) transmission strategy plays a crucial role in radar anti-jamming applications. This strategy is usually designed or trained offline, which would lose the advantage of adaptability and flexibility when facing diverse jamming patterns and non-stationary target echoes. Considering the scenarios where a radar detects a target with strong non-stationary scattering characteristics under fast-variant interference, the radar is required to immediately adjust the FA transmission strategy to react to the variation of both jamming signals and target echoes quickly. In order to enhance radar transmission strategy in both generalization and flexibility aspects, an online FA strategy, called Combinatorial Discounted Thompson Sampling (CDTS), is developed for the anti-jamming by exploiting the target's scattering change with the Multi-Armed Bandit (MAB) model. With the advantages of both optimal exploration and fast convergence, the proposed algorithm can efficiently adapt to the scattering fluctuation under dynamic frequency jamming emissions. Experimental comparisons with conventional Deep Reinforcement Learning (DRL) demonstrate the proposed algorithm's superiority for FA transmission strategy learning to boost radar detection performance under frequency response dynamics when avoiding frequency jamming.

With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work.

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is of the capability to achieve unambiguous two-dimensional images in the forward-looking slight direction. FLMC-SAR imagery usually suffers from relatively low spatial resolution as only limited Doppler diversity can be generated from the synthetic aperture. In this paper, a sparsity-driven resolution enhancement algorithm is proposed to improve the resolution FLMC-SAR image of the forward-looking area. Different from conventional beamforming processing to resolve the FLMC-SAR left-right ambiguity, a Bayesian sparsity reconstruction optimization is developed for jointly ambiguity resolving and resolution enhancement in the azimuth angle image domain. The spatial structure of the target in preliminary image domain is used as the signal sparsity with prior information to solve the constrained optimization problem for FLMC-SAR image resolution enhancement. A local least square estimator of the prior noise and signal statistics in the FLMC-SAR non-isotropic image is established in terms of determining the sparsity weight parameter. Extensive simulation and real FLMC-SAR data experiments confirm that the proposed algorithm is capable of achieving the unambiguous and resolution enhanced FLMC-SAR image.

Dynamic analysis of in-orbit satellites is of interest for space situation applications. Some exploratory methods have been presented to analyze the state of attitude-steadied satellites in the complex space situation, but effectively estimating the dynamic parameters of spinning satellites remains a significant and interesting challenge nowadays. With decoupling target spin estimation from its trajectory motion, this paper proposes a novel dynamic estimation approach by using multiple-station inverse synthetic aperture radar (ISAR) images. In brief, quadratic phase characteristic, which is usually regarded as a negative factor for ISAR imagery in conventional algorithms, is now directly exploited to build an explicit expression of the relative motion between the spin in-orbit satellite and radar. Then, based on the developed image refocusing algorithm, these phase coefficients are extracted from the multiple-station images and substituted into the optimization of target dynamic estimation. In the end, target dynamic parameters, including its instantaneous attitude vector and spin speed, are estimated through particle swarm optimization. Experiment results illustrate the feasibility of the proposed algorithm. To some degree, this work also reflects the advantages of multiple-station ISAR system in some space applications.