GNSS RFI Localization Through Fusion of PDOA, TDOA, and AoA Measurements

Threats to Global Navigation Satellite Systems (GNSS) are becoming more prevalent as devices capable of producing interference become more readily available in the civil space and more effective in military applications. Hardware solutions and positioning via signals of opportunity have shown exceptional promise in providing ways to mitigate and circumvent interference but these solutions do not directly address the problematic emitter. Using radio localization techniques and a nonlinear least-squares (NLS) estimator, a hybrid solution can be obtained which localizes the malicious emitter restoring healthy GNSS operations to impacted regions. Expanding upon previous work which used time difference of arrival (TDOA) and power difference of arrival (PDOA) this paper discusses the inclusion and potential of angle of arrival (AoA), specifically to deal with the continuous wave case TDOA struggles to solve, the effects of moving from a grid based to an iterative GaussNewton NLS, and how each of the radio localization techniques performs individually. For signals other than continuous wave, accuracies under 5 meters are possible as the high potential accuracy of TDOA dominates the solution. For continuous wave signals, PDOA can provide solutions with an accuracy of about 168 meters while AoA solutions fall under 25 meters. Additionally, as anticipated, the iterative NLS significantly reduces the computation time across all test cases when compared to the grid based approach. It was discovered though that the iterative NLS AoA solutions are sensitive to the chosen initial condition, so a solution which leverages the PDOA only estimate as the initial condition of an AoA only filter averts this initial condition problem while providing AoA accuracy levels. Future work exploring more nonlinear estimation techniques, the PDOA measurement model, and the array used to obtain the AoA measurements may prove insightful in further developing the tools to localize malicious emissions.