Linear model predictive control for vision-based UAV pursuit

This article develops and implements a vision-based unmanned aerial vehicle (UAV)-to-UAV pursuit system using a commercial off-the-shelf Parrot AR.Drone 2.0 quadrotor. This technology is intended as a countermeasure to rogue drones carrying out activities such as flying in restricted airspace, performing unauthorized aerial videography, transporting contraband and other criminal activities, or being used as improvised weapons. The proposed approach offers benefits over other current solutions, such as wide-area radio-frequency jamming that interferes with regular communication devices or high-energy military laser systems that are expensive and time consuming to set up. A linear dynamics model of the AR.Drone 2.0 vehicle stabilized by its onboard feedback control system is derived, and its parameters are experimentally identified. A linear model predictive control is developed to track specified flight trajectories, then implemented and validated in hardware flight tests. Detection and ranging of the target UAV from the pursuer UAV’s onboard monocular camera are performed using the YOLO v2 convolutional neural network algorithm. The combined control and vision design is implemented in hardware and tested quantitatively in flight experiments.

Characterizing Variability of the Urban Physical Environment for a Suite of Cities in Rondônia, Brazil

Urban environments are characterized by high spectral and spatial heterogeneity and, as a consequence, most urban pixels in moderate-resolution imagery contain multiple land-cover materials. Despite these complexities, virtually all urban land cover can be generalized as a combination of vegetation, impervious surfaces, and soil (V–I–S components), in addition to water. Previous work has demonstrated the potential of multiple endmember spectral mixture analysis (MESMA) to model the subpixel abundance of V–I–S components. Here, the authors test whether the technique is sufficiently robust to map V–I–S components for a diverse set of cities, selecting 10 urban centers in the state of Rondônia, Brazil, to represent a range of populations, development histories, and economic activities. For each urban sample, a 20 km × 20 km region centered over the built-up area was subset from Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. MESMA was applied to all subscenes using the same spectral library, model constraints, and selection rules. Accuracy of the modeled V–I–S fractions was assessed using high-resolution images mosaicked from digital aerial videography. Modeled fractions and reference fractions were highly correlated, with R2 values exceeding 0.75 for all materials in multiple cities across a region. Model complexity, or the number of endmembers required to accurately model each pixel, was correlated with the degree of human impact on the landscape. Built-up areas, as delineated by model complexity, exhibited a strong fit to the well-established relationship between the built-up area of a settlement and its population. Finally, this work demonstrates that the V–I–S components as modeled by MESMA can capture both inter- and intraurban variability, suggesting that these data products could contribute to comparative studies of urbanizing areas through time and across regions.