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4 results on '"David Petrizze"'

2. Using Trajectory Smoothness Metrics to Identify Drones in Radar Track Data

Author

Sandip Roy, David Petrizze, Logan Dihel, Mengran Xue, Chester Dolph, and Henry Holbrook

Subjects
Communications And Radar, Aircraft Design, Testing And Performance
Abstract

The identification of unmanned aircraft systems (UAS) using trajectory data is considered. Specifically, a number of smoothness metrics are proposed, which can be used to distinguish UAS from other aerial objects even when they are engaged in accelerative maneuvers (non-constant-velocity flight). The metrics are evaluated on a data set from a UAS sense-and-avoid field test, which contains track data of aerial objects recorded by a vehicle-board radar system during a flight test. The metrics are found to effectively differentiate UAS from other objects such as birds for this data set. In addition, an initial statistical performance analysis of one of the smoothness metrics is undertaken, using 15 data sets deriving from multiple flight tests. The smoothness metric is shown to identify the target UAS with 95% accuracy (95% true positive rate), while achieving a false positive rate of less than 9%.

Published
2022

4. Distinguishing Aerial Intruders from Trajectory Data: A Model-Based Hypothesis-Testing Approach

Author

Mengran Xue, Sandip Roy, Kasra Koorehdavoudi, and David Petrizze

Subjects
Parameter identification problem, Stochastic process, Computer science, Computation, Trajectory, Maximum a posteriori estimation, Law of total probability, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Algorithm, Data modeling, Statistical hypothesis testing
Abstract

Motivated by security needs in unmanned aerial system (UAS) operations, an algorithm for identifying airspace intruders (e.g., birds vs. drones) is developed. The algorithm is structured to use sensed intruder velocity data from Internet-of-Things platforms together with limited knowledge of physical models. The identification problem is posed as a statistical hypothesis testing or detection problem, wherein inertial feedback-controlled objects subject to stochastic actuation must be distinguished by speed data. The maximum a posteriori probability detector is obtained, and then is simplified to an explicit computation based on two points in the sample autocorrelation of the data. The simplified form allows computationally-friendly implementation of the algorithm, and simplified learning from archived data. Also, the total probability of error of the detector is computed and characterized. Simulations based on synthesized data are presented to illustrate and supplement the formal analyses.

Published
2021
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