A unified approach to cooperative and non-cooperative Detect-and-Avoid (DAA) is a key enabler for Remotely Piloted Aircraft System (RPAS) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative DAA sensor/system technologies for manned aircraft and RPAS are reviewed and the associated multi-sensor data fusion techniques are discussed. A DAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the DAA system processes, the key mathematical models associated with both non-cooperative and cooperative DAA functions are presented. The Interacting Multiple Model (IMM) algorithm is adopted to estimate the state vector of the intruders and this is propagated to predict the future trajectories using a probabilistic model. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are outlined. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative DAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed DAA approach on representative host platforms (AEROSONDE RPAS and typical commercial airliners) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy DAA capability and a pathway for manned/unmanned aircraft coexistence in all classes of airspace.