A Hybrid Model-Based and Model-Free Framework for Active Multi-View Viewpoint Optimization in Sonar Target Recognition
For sonar-based target recognition, this work offers a practical solution to reduce computational cost and sensing effort, though the improvements are incremental over existing methods.
This paper introduces a hybrid framework combining model-based and model-free methods for active multi-view target recognition with forward-looking sonar. The approach achieves competitive recognition accuracy while reducing sensing steps and motion cost compared to model-based baselines on a marine-debris dataset.
This paper presents a hybrid model-based and model-free framework for active multi-view target recognition using forward-looking sonar. A convolutional neural network (CNN) provides data-driven observation likelihoods, while Radon-based orientation estimation enables viewpoint-aware sensing without requiring angle annotations. During training, an information-gain-based reward guides a Proximal Policy Optimization (PPO) agent to learn a belief-aware viewpoint selection policy offline. At deployment, the learned policy performs real-time viewpoint selection using only CNN-based belief updates, eliminating the need for computationally expensive online POMDP tree search. Experiments on a marine-debris forward-looking sonar dataset demonstrate that the proposed approach achieves competitive recognition accuracy while reducing sensing steps and motion cost compared to model-based baselines.