This paper studies the difficult task of designing federated learning algorithms tailored for wireless ad hoc networks. Federated learning in such networks presents numerous challenges, including signal interference, decentralized infrastructure, dynamic changes in network topology, heterogeneous devices, and diverse data statistics. In response to these challenges, this paper proposes a fully distributed and personalized federated learning algorithm specifically designed for wireless ad hoc networks, named ADDPFed. ADDPFed tackles the issue of wireless interference by leveraging non-orthogonal multiple access technology and successive interference cancellation for enhancing overall communication efficiency. Another key strength of ADDPFed lies in its ability to enable direct local model exchanges among neighboring clients, eliminating the need for central server coordination for model aggregation. For the model aggregation, ADDPFed first calculates the distance between local models using the Tonimoto coefficient, and then assigns suited aggregation weights to these models. This approach emphasizes the contribution of similar models to the global model, effectively tackling the issue of data statistical heterogeneity. Extensive experiments validate the effectiveness of ADDPFed, paving the way for enhanced collaborative and distributed learning paradigms in wireless ad hoc networks.