Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method.

Adaptable Invisibility Management Using Kirigami-Inspired Transformable Metamaterials

Many real-world applications, including adaptive radar scanning and smart stealth, require reconfigurable multifunctional devices to simultaneously manipulate multiple degrees of freedom of electromagnetic (EM) waves in an on-demand manner. Recently, kirigami technique, affording versatile and unconventional structural transformation, has been introduced to endow metamaterials with the capability of controlling EM waves in a reconfigurable manner. Here, we report for a kirigami-inspired sparse meta-architecture, with structural density of 1.5% in terms of the occupation space, for adaptive invisibility based on independent operations of frequency, bandwidth, and amplitude. Based on the general principle of dipolar management via structural reconstruction of kirigami-inspired meta-architectures, we demonstrate reconfigurable invisibility management with abundant EM functions and a wide tuning range using three enantiomers (A, B, and C) of different geometries characterized by the folding angle β. Our strategy circumvents issues of limited abilities, narrow tuning range, extreme condition, and high cost raised by available reconfigurable metamaterials, providing a new avenue toward multifunctional smart devices.

Adaptive Radar Waveform Design Based on Weighted MI and the Difference of Two Mutual Information Metrics

This study deals with the problem of radar waveform design based on the weighted mutual information (MI) and the difference of two mutual information metrics (DMI) in signal-dependent interference. Since the target and clutter information are included in the received signal at the beginning of the design, DMI-based waveform is designed according to the following criterion: maximizing the MI between the received signal and target impulse response while minimizing the MI between the received signal and the clutter impulse response. This criterion is equivalent to maximizing the difference between the first MI and the second MI. Then maximizing the difference of two types of MI is used as the objective function, and the optimization model with the transmitted waveform energy constraint is established. In order to solve it, we resort to maximum marginal allocation (MMA) method to find the DMI-based waveform. Since DMI-based waveform does not allocate energy to the frequency band where the clutter power spectral density (PSD) is greater than the target PSD, we propose to weight the MI-based waveform and DMI-based waveform to synthesize the final optimal waveform. It could provide different trade-offs between two types of MI. Simulation results show the proposed algorithm is valid.