Concepts for Automatic Positioning of UAVs to Optimize TDOA Geolocation Performance, 16-9375

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Principal Investigators
Brad Brown
Ben Abbott

Inclusive Dates: 01/01/03 - Current

Background - Unmanned Aerial Vehicles (UAVs) are currently in use by various U.S. military and DoD agencies. Their uses range from surveillance to weapons platforms, and the size and flight characteristics of the various platforms vary greatly. Some UAVs resemble typical fixed-wing aircraft, while others resemble hovercraft and parafoils. Most UAV platforms are relatively small, and have fairly severe weight and power restrictions on any payload. However, this disadvantage is countered by the ability of the UAV to loiter in hostile areas that are inaccessible to conventional aircraft. This is especially useful for signal-intelligence applications where the aircraft may need to remain on station for hours to collect the required information. In the past, most UAVs have flown pre-programmed flight paths or have allowed for limited flight path changes via ground-station controllers. However, ground-station controllers are not ideally suited for positioning UAVs to optimize geolocation performance. This IR explores the processing algorithm requirements for automatically repositioning UAV assets to optimize geolocation accuracy via Time-Difference-of-Arrival (TDOA) methods.

Approach - The approach to this problem consists of four main efforts. The first effort is to define exactly what accuracy is required for various scenarios and determine what timing accuracy is required to support these scenarios. The second effort is to develop algorithms to determine where the platforms should be positioned for each successive TDOA measurement. The third effort is numerical simulation of the aforementioned algorithms. The fourth part of the effort is a limited analysis of actual data collected by UAV platforms against a target transmitter.

Accomplishments - Initial efforts on the project have focused on defining the measurement accuracies required to support useful TDOA results and on simulations to prove that movement of assets leads to improved TDOA results. Current efforts are focused on collecting real-world data and development of the automatic positioning algorithms.

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