Considering the dynamic RCS characteristics of stealthy UAVs, we proposed a stealthy UAV path planning algorithm based on the Double Deep Q-Network (DDQN). By introducing the reinforcement learning model that can interact with the environment, the stealth UAV adjusts the path planning strategy through the rewards obtained from the environment to design the optimal path in real-time. Specifically, by considering the effect of RCS from different angles on the detection probability of the air defense radar, the stealth UAV realizes the iterative optimization of the path planning scheme to improve the reliability of the penetration path. Under the guidance of a goal-directed composite reward function proposed, the convergence speed of the stealth UAV path planning algorithm is improved. The simulation results show that the stealth UAV can reach the target position with the optimal path while avoiding the threat zone.

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment.

A restricted 2D necklace theory is proposed to extend the application of Flower constellations theory with regard to Earth discontinuous coverage. Firstly, the 2D lattice theory, the restricted Flower constellations, as well as the necklace theory are introduced and combined to deduce the restricted 2D necklace theory. Then, the design process of Flower constellations using the restricted 2D necklace theory is presented. Finally, numerical examples are implemented. Results indicate that the proposed restricted 2D necklace theory can be an extension of the traditional Flower constellation theory and generate a large number of configurations. Also, the superiority analysis shows that aiming at the Earth discontinuous coverage issues, the restricted 2D necklace Flower constellations perform better than the traditional Walker constellations in most of the design cases. This research can be instrumental in the evolution of the Flower constellation design theory for Earth discontinuous coverage.

The random fluctuations in inlet flow represent a common uncertainty in aero-engine compressors, necessitating the control of its effects through blade optimization design. To account for the impact of inlet flow fluctuations on performance in blade design optimization, an efficient multi-objective adaptive robust aerodynamic design optimization (ARADO) method is proposed. The optimization method employs a novel sparse polynomial chaos expansion (PCE) and the advanced noisy Gaussian process regression (NGPR) technique is used to establish an initial stochastic surrogate model (SSM) containing statistical moments of aerodynamic performance. By introducing advanced sparse signal processing concepts, the sparce PCE significantly enhances the efficiency of acquiring each training sample for SSM. During the optimization process, the initial SSM autonomously updates based on historical optimization data, without requiring high precision across the entire design space. Compared to traditional model-based aerodynamic robust optimizations, the proposed ARADO method exhibits a faster convergence speed and achieves a superior average level of the optimal solution set. It also better balances various optimization objectives, concentrating the space distribution of optimal solutions closer to the average level. Ultimately, the ARADO is applied to the aerodynamic robust design of a high-load compressor airfoil across all operating incidences. The optimization results enhance aerodynamic performance while reducing performance diversity, thus aligning more closely with practical engineering requirements. Through data analysis of the optimal solutions, robust design guidelines for blade aerodynamic shapes are obtained, along with insights into the flow mechanisms that enhance aerodynamic robustness.

To simultaneously enhance the low interception and target detection performance, we proposed a robust method with low interception and detection shared waveform design based on the frequency diverse array multiple input multiple output (FDA-MIMO). We introduced an encoding-decoding-encoding to process the received signal. Considering the constant modulus constraint and the potential inaccuracies in angle estimation, we formulated an optimization problem involving the phase code waveform. The objective function was to minimize the transmitted gain while maximizing the received gain, thereby reducing the interception factor and enhancing the signal-to-noise ratio (SNR). To solve this fractional optimization problem, we introduced the auxiliary variables to transform the fractional problem into a difference problem. Taylor expansion and sequential convex programming (SCP) are utilized to convert the nonconvex problem into a series of convex sub-problems. Numerical results demonstrate the superiority of the proposed shared waveform.

The transmitted beam of frequency diversity array (FDA) has the range–angle–time coupling property, which has essential applicative potential in angle deception and active anti-jamming. In this paper, the concept of time index within pulse is introduced. Firstly, the phase characteristics of FDA-transmitted signals based on the time index within pulse concept are studied. Then, the deceptive angle performance of FDA-transmitted signals is discussed. The theoretical analysis and simulation results show that the phase characteristics of the FDA signal are not related to the range, but to the time index within pulse. With the phase center as the reference point, the phase is equal as long as the time index within the pulse is the same. Angle deception and active anti-jamming can be achieved using the optimized frequency increment of each FDA.