A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar

Sun, Zongzheng; Quan, Yinghui; Liu, Zhixing

doi:10.3390/rs15071851

Cited by 6 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Radars of different regimes have many counterpart anti-jamming measures to traditional jamming methods. We note that the use of many new types of jamming usually achieves unexpected results [52][53][54][55]. (5) With the development of a cognitive radar, radar jamming decisions based on fixed jamming methods still pose a significant battlefield threat.…”

Section: Discussionmentioning

confidence: 99%

A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm

et al. 2023

View full text Add to dashboard Cite

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence.

show abstract

Section: Discussionmentioning

confidence: 99%

A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Remote Sens. 2023, 15,5015 From Equation (12), it can be seen that the scatterer coordinates of the sub-Nyquist jamming signal have appeared periodically along the range direction and the azimuth direction with the intervals c f sr 2K r and λ f sa 2ω ,respectively, and the positions of the different false targets are controlled by l r and l a , respectively.…”

Section: Sub-nyquist Isar Imaging Modelmentioning

confidence: 99%

“…Interrupted-sampling repeater jamming (ISRJ) is a method based on DRFM that can generate dense false targets or striped targets for jamming [12][13][14][15]. In [12], the principles and the ways of achieving ISRJ are shown to generate multiple false targets for dechirping radar.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], a non-uniform periodic repeater jamming method is shown to generate many false targets with different amplitudes and intervals. In [15], a constraints parameter ISRJ method is proposed for use against intra-pulse-frequency-agile waveforms. The constraints parameter is calculated by timefrequency ridge extraction and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multiple-False-Target ISAR Shape Deception Jamming Method Based on Improved Template Multiplication Modulated Time-Delay Algorithm and Sub-Nyquist Sampling

Liu,

Zhang,

Xiong

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

In recent decades, the deception jamming approach based on digital radio frequency memory (DRFM) for inverse synthetic aperture radar (ISAR) has been a well-researched topic. Various types of jamming signals based on interrupted-sampling repeater jamming (ISRJ) can induce one-dimensional multiple high-resolution range profile (HRRP) false targets or two-dimensional realistic multiple ISAR false targets for deception. However, these existing methods generate false targets that are identical to the real target. The ISRJ false target also generates a main false target whose energy is much higher than other sub-false targets. Thus, it is easy to discover that the radar has suffered jamming. In order to generate better, more confusing jamming signals, this paper proposes a jamming method based on sub-Nyquist sampling jamming to induce realistic, multiple false targets on ISAR images. It improves a template multiplication modulated time-delay method to eliminate and add scatterers on the selected false target to change its ISAR shape. The frequency-shift parameters of the template jamming signal are analyzed and derived in detail for eliminating and adding scatterers at the specified location. Thus, multiple false targets with different ISAR shapes and similar energy are generated, which can create better deception effects. Meanwhile, this method can adjust the number of false targets and the location of changed-shape false targets. Furthermore, the resolution of the false target can adaptively change with the radar pulse width and the accumulated pulse number. The simulation results show that the proposed deception jamming strategy works.

show abstract

Knowledge-Aided Model-Based Reinforcement Learning for Anti-Jamming Strategy Learning

Li,

Liu,

Jiu

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar

Cited by 6 publications

References 23 publications

A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm

A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm

A Multiple-False-Target ISAR Shape Deception Jamming Method Based on Improved Template Multiplication Modulated Time-Delay Algorithm and Sub-Nyquist Sampling

Knowledge-Aided Model-Based Reinforcement Learning for Anti-Jamming Strategy Learning

Contact Info

Product

Resources

About