Location information is vital for any type of aircraft and even more crucial for Unmanned Aerial Systems (UAS). GPS is a readily available solution but signals can easily be jammed or lost. In this thesis, radar is explored as a backup system for self-localization when GPS signals are not available. The method proposed requires that an area be pre mapped by collecting radar data with known latitude and longitude coordinates. New radar data is then collected and compared to previously stored values. Channel matrices are stored at each point and are used as the basis for location comparisons. Various methods of matrix comparison are used and both simulation as well as experimental results are shown. The main results of this thesis show that position can be determined using channel matrices if the sensor is within a certain radius of previously stored locations. This radius is on the order of a wavelength or less. Using correlation matrix comparisons the radius of localization is broadened. A novel method using multiple channel and multiple frequency data proves to be successful and determines the position of an octorotor UAS with a mean position error of less than three meters. The design of a low-cost, compact, and light-weight FMCW radar for two applications is also presented. The first application is a novel radar based positioning system that utilizes multiple channel and multiple frequency information to determine position. The second application is a UAS sense and avoid system using a monopulse configuration. Without connectors or antennas, the radar weighs 45.7 grams, is 7.5 cm x 5 cm x 3 cm in size, and costs less than $100 when built in quantities of 100 or more (excludes antennas and connectors). It is tested using delay lines and corner reflectors and accurately determines the distance to close range targets.