# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 4 Adaptive Clutter Intelligent Suppression Method Based on DRL
### Reinforcement Learning Element Design
At the moment of t, the evaluation and reward mechanism of the SCNR of the whole echo data \(\text{SCNR}_{t}\) and the clutter attenuation \(\text{CA}_{t}\) of the range cell where the agent is located are as follows: when \(\text{SCNR}_{t}{>}\text{SCNR}_{\text{peak}}\) and \(\text{CA}_{t}{>}\text{CA}_{\text{peak}}\), the overall performance of the system and the partial clutter suppression performance of each agent have reached the highest level, giving a large positive reward with the reward value of 100 and updating the highest values of \(\text{SCNR}_{\text{peak}}{=}\text{SCNR}_{t}\) and \(\text{CA}_{\text{peak}}{=}\text{CA}_{t}\); when \(\text{SCNR}_{t}\leq\text{SCNR}_{\text{peak}}\) and \(\text{CA}_{t}{>}\text{CA}_{\text{peak}}\), it indicates that the filter parameter tuning measures of agents have to some extent improved the clutter suppression performance, although the overall performance is not ideal, but the partial clutter suppression effect is good, the reward value is 30, and \(\text{CA}_{\text{peak}}{=}\text{CA}_{t}\); when \(\text{SCNR}_{t}{>}\text{SCNR}_{\text{peak}}\) and \(\text{CA}_{t}\leq\text{CA}_{\text{peak}}\), it indicates that the overall clutter suppression effect is good, but the local clutter suppression effect is poor, and it is necessary to further optimize the local performance, the reward value is \(-1\), and \(\text{SCNR}_{\text{peak}}{=}\text{SCNR}_{t}\); when \(\text{SCNR}_{t}\leq\text{SCNR}_{\text{peak}}\) and \(\text{CA}_{t}\leq\text{CA}_{\text{peak}}\), both the overall performance and partial performance are unsatisfactory, indicating that the agent's parameter adjustment at this time fails to effectively improve the clutter suppression effect, and the reward value is \(-100\). Through these reward and update mechanisms, the agent can continuously learn and optimize its behavior in complex environments, so as to achieve more efficient parameter adjustment and clutter suppression effect. This section integrates DL network and RL elements, uses the deep Q network to build a filter parameter decision model, regards each range cell to be detected as an agent, introduces the intelligent environment perception method of DL, and enables each agent to have online perception learning ability of complex environments. In the design of the action space, the filter order, spectrum width, and spectrum center are used as the adjustment parameters of the action space, and the agent dynamically adjusts the filter parameters according to the observed environmental data without manual intervention. The designed filter depression and notch width can be highly matched with the clutter, thus reducing the need for prior knowledge. The greedy strategy of exploration and exploitation ensures that the agent can widely explore the action space in the early learning period and gradually turn to the use of high-reward actions with the accumulation of experience, effectively improving the convergence speed and stability of the algorithm. The reward mechanism combining overall and partial effectively balances the global performance and partial clutter suppression effect, avoiding the problem of the agent falling into the local optimal solution. Combining the above definitions of state space, action space, and reward function, the workflow for the filter parameter decision based on DQN is as shown in Algorithm 1. ```
1: Initialize the evaluation value Q network and target value Q network with a random parameter \(\theta\) and \(\theta^{-}=\theta\) respectively. 2: Initialize the experience replay buffer D, the filter parameter strategy, the discount factor \(\gamma\), the learning rate \(\alpha\), the greedy factor \(\varepsilon\), the update frequency \(\tau\) of the target network parameters and other parameters. 3: Deploy an equal number of Agents based on the number of range cells in the input radar echo data matrix. 4:for episode = 1, 2, 3...M, do:
5: Initialize the environment and state space \(s_{t}\). 6: According to the \(\varepsilon-greedy\) principle, agent in each range cell selects the action.