Lee Burchett

Angle of arrival geolocation using non-linear optimization

This paper addresses the problem of object localization using only angle-of-arrival (AoA) data from satellites. Traditionally, this is performed by a triangulation algorithm (TA) that minimizes the distances between the estimated object location to all lines-of-sight representing measurements of the object. However, when observing objects from satellites, the differences in distance from each satellite to the object can be significant. The error this induces in measurements from farther-distant satellites results in an inordinate impact on the geo-location error. To overcome this problem, we introduce a non-linear optimization (NLO) approach that models the measurement error at each satellites as a probability density function. By finding the most-probable geo-location of the object, this systematic error is eliminated. We found that the NLO provides a more accurate estimate of the objects location than the TA in 93% of instances. In addition, we analyze the uncertainty estimates generated by both the TA and NLO approaches. The NLO estimates of uncertainty are also considerably more accurate than the TA estimates in all cases.

Wavelength correction of refractivity variation measurements

The index of refraction structure constant, Cn2 indicates how strongly the index of refraction varies in a region of the atmosphere. These variations usually arise through turbulent motions, creating an inhomogeneous distribution of species, density, temperature and pressure. Because the index of refraction also depends on wavelength, the measured value of Cn2 will depend on wavelength. This Cn2 difference generally becomes more pronounced as the difference in wavelength increases. This paper describes a technique for converting between measurements of Cn2 at different wavelengths, and gives an example for converting from centimeter to visible and near IR wavelengths.

The instagram: A novel sounding technique for enhanced HF propagation advice

Modern OTHR systems make extensive use of propagation support information for parameter setup advice. A novel method for increasing dimensionality and temporal resolution of this advice is demonstrated here using an instantaneously wideband waveform on a one-way path. A composite of pseudo-randomly phased, discretized, massively multi-channel signals is synthesized through a simple summing scheme. Upon reception the composite is rapidly processed in the frequency domain to produce channel scattering information simultaneously across the band. The channels may be collapsed in the Doppler domain to reduce to conventional oblique ionograms. Total integration time required to produce the full Doppler ionograms, even with low transmit powers, is reduced over conventional methods by up to three orders of magnitude leading to the term ‘Instagram’. The technique is implemented on an oblique sounding system that provides the necessary direct digital arbitrary waveform generation and reception capability. A result from the initial field trial is provided.

Geolocation of Fast-Moving Objects From Satellite-Based Angle-of-Arrival Measurements

Recently, satellite-based systems have been introduced that utilize angle-of-arrival (AOA) measurements to geo-locate objects of interest. In the previous work, we considered the application of nonlinear optimization to AoA-based geolocation to these systems. This previous work, however, assumed that all noise sources were independent. In the case of fast-moving objects, however, there is a significant source of error due to the propagation time inherent in satellite-based observation of objects due to the difference between the location of the object when it is observed by a satellite, and the location of the object when it emitted the signal that is being measured. This introduces a systematic error into the system that cannot be resolved by the system proposed by Burchett et al. In this paper, we extend our prior work to account for the time-delay inherent in satellite-based geolocation systems, making this system accurate for fast-movers as well as fixed or slow-moving objects. Results demonstrating significant improvement in geolocation performance both in terms of accuracy and estimated error bounds are presented.

Structural and electromagnetic design considerations for very large space antennas

This paper describes the design and analysis of a 24-element dipole array which serves as an optimized feed structure for a 150-meter cruciform reflector antenna for satellite communications in the 1–2 GHz range. The radiation pattern for this structure was calculated using a Method of Moments based computational electromagnetics code yielding an overall 47 dBi reflector gain. The effect of incorporating this optimized feed is an overall performance improvement on an initial cruciform reflector antenna design by minimizing spillover and undesirable side lobes. Performance versus size-and-weight tradeoffs are discussed as is a detailed description of the cruciform antennas mechanical design and its resultant RF characteristics.

Automated aerial refueling: Parallelized 3D iterative closest point: Subject area: Guidance and control

Vision-based automated aerial refueling requires a relative positioning solution capable of real time updates between the tanker and receiver. The processing time required for modern approaches are dominated by an iterative point alignment phase. This work presents an accelerated alignment variant which utilizes parallelization and Delaunay Triangulation to achieve real time estimates.

Parallelized Iterative Closest Point for Autonomous Aerial Refueling

The Iterative Closest Point algorithm is a widely used approach to aligning the geometry between two 3 dimensional objects. The capability of aligning two geometries in real time on low-cost hardware will enable the creation of new applications in Computer Vision and Graphics. The execution time of many modern approaches are dominated by either the k nearest neighbor search (kNN) or the point alignment phase. This work presents an accelerated alignment variant which utilizes parallelization on a Graphics Processing Unit (GPU) of multiple kNN approaches augmented with a novel Delaunay Traversal to achieve real time estimates.

Comparison of the path-weighted Cn2 derived from time-lapse imagery and weather radar

Irradiance based techniques are not suitable for profiling over long, nearly horizontal paths through atmospheric turbulence since they suffer from saturation effects. Alternate techniques that can provide reliable turbulence information over strong turbulence paths are currently being investigated. Two such approaches are introduced here. The first approach is a phase-based technique that uses the turbulence induced random motion in time-lapse images of a distant scene to estimate the path-weighted Cn2. An imaging experiment was conducted at the Air Force Institute of Technology to demonstrate this approach. A tripod-mounted digital camera captured images of a distant building every minute. Two different components of motion were apparent in the imagery: the random, faster motion due to atmospheric turbulence and the slower, vertical motion due to changes in the average refractive index gradient along the path. A correlation algorithm was used to measure the image shifts. The technique uses a derived set of path weighting functions that depend on the size of the imaging aperture and the patch size in the image whose motion is being tracked. The second method estimates Cn2 values at different locations along a path using a combination of weather (NEXRAD) radar and numerical weather prediction (NWP) data. Two techniques have been used to derive the estimates. The first uses radar doppler information to determine the eddy thermal dissipation rate and combining this with NWP gradients to form a Cn2 estimate. The second technique attempts to correct Cn2 derived from radar clear-air reflectivity for noise and clutter and wavelength. The weights derived in the time-lapse imaging method have been applied to the noise and wavelength corrected NWP-radar derived Cn2 profile to obtain the path-weighted value. The path-weighted estimates obtained using the optically-based, time-lapse imaging and weather radar-based methods have been compared. Both approaches show great potential in estimating turbulence strengths over strong turbulence paths.

Improved Parametrization for Estimating C2n from Numerical Weather Prediction

Optical Turbulence estimates made from Numerical Weather Prediction using a modified approach based on Ciddor’s refractivity parametrization with temperature and hydrometer contributions is shown to predict measured 880nm scintillation better than a standard approach.

Vertical Scaling of C n 2 in Volumetric Weather Radar Measurements

Paper describes NEXRAD observations of vertical Cn2 profiles which are used to derive vertical gradients of passive additives potential temperature and potential vapor pressure. These gradients can be used to estimate Cn2 for optical systems.