The integration of Cognitive Radio (CR) with Unmanned Aerial Vehicles (UAVs) is an effective step towards relieving the spectrum scarcity and empowering the UAV with a high degree of intelligence. The dynamic nature of CR and the dominant line-of-sight links of UAVs poses serious security challenges and make the CR-UAV prone to a variety of attacks as malicious jamming. Joint jammer detection and automatic jammer classification is a powerful approach against the physical layer threats by identifying multiple jammers attacking the network that realize a crucial stage towards efficient interference management. This paper proposes a novel method for joint detection and automatic classification of multiple jammers attacking with different modulation schemes. The method is based on learning a representation of the radio environment encoded in a Generalized Dynamic Bayesian Network (GDBN) whilst multiple GDBN models represent various jamming signals under different modulation schemes. The CR-UAV performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best-fit model that explains the detected jammer and recognize the modulation scheme accordingly. The simulated results demonstrate that the proposed GDBN-based method outperforms Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE) in terms of classification accuracy and achieves a higher degree of explainability of its own decisions by interpreting causes and effects at hierarchical levels under the Bayesian learning and reasoning processes.