Biological vision systems can perform target selection, pattern recognition, and dynamic range adaptation at capability levels far beyond that of human-designed methods. This paper applies a two-stage Biologically-Inspired Vision (BIV) model for image pre-processing and infrared tone remapping, derived from the visual pipeline of the hover y. The first stage performs spatially invariant, pixel-wise, intensity normalization, to intelligently compress scene dynamic range and enhance local contrasts using an adaptive gain control mechanism. The second stage of the model applies adaptive spatio-temporal filtering to reduce redundancy within image sequences. Our experiments demonstrate the strengths of the model on four practical tasks. For large targets, the model acts as a sophisticated edge extractor. The examples show the ability to retrieve the complete structure of a boat from sea clutter, increasing the global contrast factor by 165%. Secondly and thirdly, for small and weak-signature targets, segmentation is demonstrated. A filter is applied to track a 2x2 pixel dragon y without interruption, and a small maritime vessel, extracted as it passes in front of a larger vessel of similar emissivity. Finally, the power of the BIV model to rapidly compress dynamic range and normalize sudden changes in scene luminance is validated by means of incandescent pyrotechnic pellets launched from an aerial platform.
This paper presents a method for tomographically reconstructing atmospheric temperature profiles and wind velocity fields based on acoustic travel time measurements between two or more Unmanned Aerial Vehicles (UAVs). The technique offers mobility and the capacity to monitor hazardous atmospheric environments, otherwise not justifiable on the basis of cost or risk. Simulations, in which the parametric fields of the atmosphere are modelled as a weighted sum of Radial Basis Functions, demonstrate the technique’s potential performance envelope. The approach also allows local meteorological measurements made at the UAVs to supplement any time delay observations. This increases the accuracy of the technique, which has potential for practical applications in boundary layer meteorology, the theory of atmospheric turbulence, and wave propagation through a turbulent atmosphere.
KEYWORDS: Unmanned aerial vehicles, Acoustics, Atmospheric propagation, Tomography, Receivers, Error analysis, 3D metrology, Signal to noise ratio, Sensors, Global Positioning System
This paper presents a method for tomographically reconstructing spatially varying 3D atmospheric temperature profiles and wind velocity fields based. Measurements of the acoustic signature measured onboard a small Unmanned Aerial Vehicle (UAV) are compared to ground-based observations of the same signals. The frequency-shifted signal variations are then used to estimate the acoustic propagation delay between the UAV and the ground microphones, which are also affected by atmospheric temperature and wind speed vectors along each sound ray path. The wind and temperature profiles are modelled as the weighted sum of Radial Basis Functions (RBFs), which also allow local meteorological measurements made at the UAV and ground receivers to supplement any acoustic observations. Tomography is used to provide a full 3D reconstruction/visualisation of the observed atmosphere. The technique offers observational mobility under direct user control and the capacity to monitor hazardous atmospheric environments, otherwise not justifiable on the basis of cost or risk. This paper summarises the tomographic technique and reports on the results of simulations and initial field trials. The technique has practical applications for atmospheric research, sound propagation studies, boundary layer meteorology, air pollution measurements, analysis of wind shear, and wind farm surveys.
The Australian Defence Force is a small force, dependent upon a few high value assets that act as force multipliers. Consequently, it cannot afford to sustain high attrition. The current Concept of Operations for these platforms is to operate them outside the threat envelope. Organic sensors and data links are used to maintain Situational Awareness, Combat Air Patrol is used to intercept hostile missile launch platforms, and Electronic Warfare self-protection is used as a last resort. Unfortunately, it is common for such high value assets to be slowly, non-stealthy, low agility, physically large platforms that follow predictable trajectories. Consequently, they are easy to target and track from a long range and have a high `sitting duck' factor.
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