The advances in aircraft autonomy have led to an increased demand for robust sensor and actuator fault detection and estimation methods in challenging situations including the onset of ambiguous faults. In this paper, we consider potential simultaneous fault on sensors and actuators of an Unmanned Aerial Vehicle. The faults are estimated using a Jump-Markov Regularized Particle Filter. The jump Markov decision process is used within a regularized particle filter structure to drive a small subset of particles to test the likelihood of the alternate hypothesis to the current fault mode. A prior distribution of the fault is updated using innovations based on predicted control and measurements. Fault scenarios were focused on cases when the impacts of the actuator and sensor faults are similar. Monte Carlo simulations illustrate the ability of the approach to discriminate between the two types of faults and to accurately and rapidly estimate them. The states are also accurately estimated.