No abstract

Reinforcement learning (RL) has the advantage of interaction with an environment over time, which is helpful in cognitive jamming research, especially in an electronic warfare-type scenario, in which the communication parameters and jamming effect are unknown to a jammer. In this paper, an algorithm for a jamming strategy using orthogonal matching pursuit (OMP) and multi-armed bandit (MAB) is proposed. We construct a dictionary in which each atom represents a symbol error rate (SER) curve and can be obtained with known noise distribution and deterministic parameters. By reconnoitering, the jammer counts acknowledge/not acknowledge (ACK/NACK) frames to calculate the SER, which is also regarded as samples that are sampled from the real SER curve using an MAB. When we obtain the sampled sequence and the constructed dictionary, the OMP algorithm is used to search and locate atoms and its corresponding coefficients. With the searching results, the jammer can construct an SER curve that is similar to the real SER curve. The experimental results demonstrate that the proposed algorithm can learn an optimal jamming strategy with three interactions, which converges substantially faster than the state of the art.

In an electronic warfare-type scenario with a partially known environment, the advantages of trial and error in the reinforcement learning algorithm adopted by a cognitive jammer can disrupt communications between transmitter-receiver pairs adaptively and optimally. However, complete information regarding the Markov decision process and its thousands of interactions seem to be unreliable, especially in applications that require timeliness, such as cognitive jamming.Faced with uncooperative or hostile transmitter-receiver pairs, it is a challenging control problem for the jammer to select the proper actions to realize communication denials. This paper addresses apprenticeship learning in cognitive jamming, where inverse reinforcement learning is used by the jammer to acquire skilled behaviors from expert demonstrations. Without a known prior reward function, the jammer can obtain high jamming performance under the guidance of the given expert strategy. More specifically, the number of iterations needed in apprenticeship learning is much less than that needed in Q learning, which is a vital advantage in a fast-changing environment. Numerous results demonstrate that it is feasible and realistic to use apprenticeship learning to learn the jamming strategy since its performance meets or exceeds those of existing methods. KEYWORDScognitive jamming, inverse reinforcement learning, jamming strategy, Markov decision process, reinforcement learning Optim Control Appl Meth. 2019;40:647-658.wileyonlinelibrary.com/journal/oca

This paper studies the multi-sensor resource management and intelligent tracking strategy generation for different targets. Firstly, the multi-sensor resource management methods are analyzed and compared with each other to specify the planning and information theories as the basis for this research. Then, a functional model is established according to the sensor resource updates, target visibility analysis, sensor performance analysis and sensor-to-target assignments. And the multi-sensor intelligent target tracking workflow is designed based on the function model. Thirdly, the corresponding multi-sensor intelligent target tracking strategy generation is designed based on ant colony algorithm. Finally, the evaluation analysis on the strategy generation is completed. This study provides a reference for the future studies on multi-sensor intelligent target tracking and multi-sensor system design.

It is generally believed that jamming signals similar to communication signals tend to demonstrate better jamming effects. We believe that the above conclusion only works in certain situations. To select the correct jamming scheme for a multi-level quadrature amplitude modulation (MQAM) signal in a complex environment, an optimal jamming method based on orthogonal decomposition (OD) is proposed. The method solves the jamming problem from the perspective of the in-phase dimension and quadrature dimension and exhibits a better jamming effect than normal methods. The method can construct various unconventional jamming schemes to cope with a complex environment and verify the existing jamming schemes. The Experimental results demonstrate that when the jammer ideally knows the received power at the receiver, the proposed method will always have the optimal jamming effects, and the constructed unconventional jamming scheme has an excellent jamming effect compared with normal schemes in the case of a constellation distortion.