This project aims to develop a comprehensive approach for characterization of RF environment in terms of spatial and temporal features including: i) the number of active transmitters, ii) their power, direction of arrival and location, and iii) modulation class. Spatio-temporal spectrum sensing requires novel multi-dimensional parameter estimation algorithms as opposed to conventional spectrum sensing that uses hypothesis testing based detection algorithms. In this work, the Bayesian estimation approach using angle-of-arrival measurements is applied to create the probabilistic map of the transmitter presence in a given region taking into account measurement noise and uncertainties. The tools from random matrix theory are applied to perform joint detection and parameter estimation, and analytically derive performance bounds. The methods for modulation classification are based on goodness-of-fit statistical tests with reduced sampling complexity, and provide the unified classification framework for a wide range of modulation classes. The results of this research will impact the design of novel medium access and routing protocols that manage the interference through awareness of the location and link quality of other transmitters in the region. The developed technologies could potentially apply for monitoring of RF transmissions within wireless infrastructure, and for the defense and national security applications.