Abstract
To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer.
Highlights
Main lobe jamming is one of the most challenging jamming types because the jammer and the target are close enough that both are in the main beam of the radar
An reinforcement learning (RL) problem can be formulated within the framework of the Markov decision process (MDP), which consists of a five-tuple hS, A, P, R, γi [21], where S is the set of states, A is the set of actions, P(st+1 |st, at ) describes the probability of transition from the current state st to the state st+1 with the chosen action at, R(s, a) provides a scalar reward given a state s and action a, and γ ∈ [0, 1] is a discount factor
In Wasserstein robust reinforcement learning (WR2 L), the agent formulates robust reinforcement learning as a minmax game, where the agent aims to improve the performance by optimizing its policy, while the environment tries to worsen the performance by changing the dynamic parameters
Summary
Main lobe jamming is one of the most challenging jamming types because the jammer and the target are close enough that both are in the main beam of the radar. Common strategies to combat main lobe jamming involve identifying and eliminating jamming signals after the radar is jammed [1,2,3], which can be regarded as passive suppression methods These methods usually require the jammer and the direction-of-look to be separable in angular space. Among the above-mentioned agile actions, frequency agility in transmission is considered one effective way to combat main lobe jamming because frequency-agile (FA) radar can actively change its carrier frequency in a random manner This makes it difficult for the jammer to intercept and jam the radar [5,8,9]. To overcome the uncertainties in both the radar and jammer, a robust antijamming strategy design method for FA radar is proposed in this paper.
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