|Abstract:||GPS signals are weak in nature and are often a target of electronic attacks that aim to manipulate or disable navigation systems. As unmanned aerial vehicles (UAVs) become more ubiquitous, the manipulation of GPS capable UAVs is becoming a greater threat to their use. One method of mitigating these attacks employs the use of multiple antenna arrays. These arrays are utilized in many GPS dependent systems. These GPS capable UAVs using multiple antennas are restricted to a short baseline multi-antenna system similar to Controlled Reception Pattern Arrays (CRPA) sizes. Differential GPS measurements are used for Angle of Arrival (AOA) calculations which are used to locate the threat in question. The AOA estimations and attitude are a function of the baseline of the multi-antenna array in use. This paper explores the analysis of multiple antenna baselines and the impact the chosen baseline length has on the estimation of the electronic threat location. Previously, threat locations have been estimated as shown in work by Gray, Thompson, Mahmood and others. Numerous papers focusing on geolocation of threats show results of long (defined as greater than carrier phase wavelength) baseline geolocation. Gray’s work focuses on AOA techniques involving a larger baseline array, which is not feasible on a compact UAV. The work done by Gray also focuses on a 2-dimensional system and does not reference any elevation change between emitter and receiver. Work done by Costa focuses on an aerial platform but uses a large baseline. Costa’s AOA estimation methods are evaluated with a system with one wavelength spacing between antennas. Ina addition to publications on large baseline arrays, there have been several publications focusing on uniform linear arrays and geolocation accuracy, as from Zahemia and Huang, but these do not consider the three dimensional geolocation. Prior work by Corderio shows different forms of UAV attitude estimation on a short baseline array but also includes additional sensors in the solution for the system. While different baselines are considered in Corderio’s work, his solutions are focused on the fusion of multiple sensors and not the impact the baseline has. A primary motivator for this paper is the fact that UAVs typically have a very limited capacity for additional electronics. As weight is added, power consumption of the UAV increases as flight times and speeds decrease. Keeping these limitations in mind, a multi-antenna system can become constrained by size and weight limitations on the given platform. This motivates using as few antennas as possible to minimize weight and explore the impact of baseline on the geolocation of threats. In addition to the effects of the baseline, UAVs using pathfinding designed to avoid electronic threats can still fall victim to the attack if the uncertainty of the geolocation is not considered. In addition to commercial use, this geolocation system could be law enforcement searching for threats. The uncertainty of the location of the threat is vital for law enforcement’s ability to locate and disable the source of the interference. This paper explores the impact the array has on geolocation. An antenna array capable of GPS attitude solutions is the only form of navigation sensor used. For the purpose of the paper, the baseline from antenna to antenna is limited from ten percent of the GPS L1 wavelength to just under the GPS L1 wavelength. The carrier phase differences between antenna pairs are the measurements used for the attitude to keep the attitude solution dependent on the same measurements. The attitude solution is solved in a least squares estimation resulting in the relative rotation of Euler angles from the body frame to the East North Up frame (ENU). The attitude of the UAV is solved after each GPS update. After producing an attitude solution, AOA estimation methods are used on the antenna array signals to produce AOAs for the observed signals. An assumption made here is the detection of the invalid signal has been done successfully. The invalid signal is then assumed as being excluded from the position and attitude solutions returning the system to a similar state before the threat is introduced. The AOA estimation is produced in the body frame of the UAV. The attitude estimation previously solved is combined with the AOA estimation and transformed to the global frame to be used in a least squares estimation across multiple time steps. The position, attitude and AOA errors are combined to create a threat geolocation error as a function of antenna baseline. The results of the geolocation confidence should be quantified by the standard deviation of the combined results. This standard deviation should be shown as a function of the antenna baselines. This information can be used in system design for autonomous UAVs and threat location devices to find the capabilities and limitations of the systems being designed.|
Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
|Pages:||2588 - 2597|
|Cite this article:||
Carter, Patrick R., Starling, Josh, Martin, Scott, Bevly, David, "Baseline Impact on Geolocation," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 2588-2597.
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