Distributed precision jamming (DPJ) is a novel blanket jamming concept in electronic warfare, which delivers the jamming resource to the opponent equipment precisely and ensures that friendly devices are not affected. Robust jamming performance and low hardware burden on the jammers are crucial for practical DPJ implementation. To achieve these goals, we study the robust design of wideband constant modulus (CM) discrete phase waveform for DPJ, where the worst-case combined power spectrum (CPS) of both the opponent and friendly devices is considered in the objective function, and the CM discrete phase constraints are used to design the wideband waveform. Specifically, the resultant mathematical model is a large-scale minimax multi-objective optimization problem (MOP) with CM and discrete phase constraints. To tackle the challenging MOP, we transform it into a single-objective minimization problem using the L p -norm and Pareto framework. For the approximation problem, we propose the Riemannian conjugate gradient for CM discrete phase constraints (RCG-CMDPC) algorithm with low computational complexity, which leverages the complex circle manifold and a projection method to satisfy the CM discrete phase constraints within the RCG framework. Numerical examples demonstrate the superior robust DPJ effectiveness and computational efficiency compared to other competing algorithms.

This paper considers the joint design of the transmit waveform and receive filter for airborne multiple-input multiple-output (MIMO) radar under spectral constraint in signal-dependent clutter. The spatial-frequency spectral compatibility constraint is imposed in the joint design problem. To tackle the non-convex joint design problem, we develop an iterative algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). The proposed algorithm can handle the non-convex terms by the convex approximation. Simulation results demonstrate the superiority of the proposed algorithm in terms of better signal-to-interference-plus-noise ratio (SINR) and better spectral compatibility ability.

Abstract In recent years, precision electronic warfare (PREW), an energy‐focussed delivery technology, has been used to solve a series of problems that currently limit traditional electronic warfare. A super‐sparse array of transmitters is used to concentrate the jamming energy as much as possible in the target area and reduce the jamming energy as much as possible in other specific protected areas. However, determining how to design jamming waveforms with jamming characteristics, such as covering the target work bandwidth, while ensuring that the energy transmission meets the requirements, is still an urgent problem. For the first time, this paper considers the task characteristics of PREW applications as constraint conditions. Through a first‐order Taylor expansion, the optimisation problem is approximately transformed into a convex sequential cone programming problem, and jamming waveforms are obtained through an iterative solution method. Experiments showed that the jamming waveforms designed by this algorithm meet the energy distribution requirements of PREW scenarios, and at the same time, the jamming waveforms emitted by each transmitter are superimposed in the jamming area to produce a signal that can cover the target work bandwidth. This method can achieve energy focussing on the space and frequency domains.

This letter deals with the joint design of transmit waveform and receive filter bank for airborne multiple-input multiple-output (MIMO) radar under the target uncertainties. Assuming that the spatial angle and the Doppler frequency of the target are unknown, we formulate the maximin joint design problem by maximizing the worst-case signal-to-interference-plus-noise ratio (SINR) under the energy constraint, flexible modulus constraint, and similarity constraint on the transmit waveform. To tackle this problem, we develop a computationally efficient algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm.