Temporally adjusted complex ambiguity function mapping algorithm for geolocating radio frequency signals

Abstract

The Complex Ambiguity Function (CAF) allows simultaneous estimates of the Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) for two received signals. The Complex Ambiguity Function Geo-Mapping (CAFMAP) algorithm then directly maps the CAF to geographic coordinates to provide a direct estimation of the emitter’s position. The CAFMAP can only use short-duration CAFs, however, because the collector motion changes the system geometry over time. In an attempt to mitigate this shortfall and improve geolocation accuracy, the CAFMAP takes multiple CAF snapshots and sums the amplitudes of each. Unfortunately, this method does not provide the expected accuracy improvement, and a new method is sought. This thesis reformulates the equations used in computing the CAF, in order to account for the collector’s motion, and uses the results to derive a new CAFMAP algorithm. This new algorithm is implemented in MATLAB, and its results and characteristics analyzed. The conclusions are as follows: the new algorithm functions as intended, removes the accuracy limitations of the original method, and merits further investigation. Immediate future work should focus on ways to reduce its computation time and modifying the algorithm to account for 3-Dimensional reality, non-linear motion of collectors, and motion of the emitter.