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Full-Text Articles in Signal Processing
Radiometric Analysis Of Daytime Satellite Detection, Katherine B. Lilevjen
Radiometric Analysis Of Daytime Satellite Detection, Katherine B. Lilevjen
Theses and Dissertations
A radiometric model for daylight satellite detection is developed and used to evaluate the effects of various parameters on signal-to-noise ratio (SNR). Detection of reflected sunlight from a low-earth orbit, diffuse, planar satellite by a single-pixel infrared photovoltaic detector is considered. Noise considered includes photon noise from the background and signal, as well as thermal noise. Parameters considered include atmospheric conditions, optical parameters, and detector parameters. The Phillips Laboratory Expert-assisted User System, an atmospheric modeling tool that employs the MODTRAN and FASCODE transmission codes, is used to model wavelength-dependent atmospheric transmission and background radiance. The SNR is found to increase …
Daytime Detection Of Space Objects, Alistair D. Funge
Daytime Detection Of Space Objects, Alistair D. Funge
Theses and Dissertations
Space Situational Awareness (SSA) requires repeated object updates for orbit accuracy. Detection of unknown objects is critical. A daytime model was developed that evaluated sun flares and assessed thermal emissions from space objects. Iridium satellites generate predictable sun glints. These were used as a model baseline for daytime detections. Flares and space object thermal emissions were examined for daytime detection. A variety of geometric, material and atmospheric characteristics affected this daytime detection capability. In a photon noise limited mode, simulated Iridium flares were detected. The peak Signal-to- Noise Ratios (SNR) were 6.05e18, 9.63e5, and 1.65e7 for the nighttime, daytime and …
Space Object Identification Using Feature Space Trajectory Neural Networks, Neal W. Bruegger
Space Object Identification Using Feature Space Trajectory Neural Networks, Neal W. Bruegger
Theses and Dissertations
The Feature Space Trajectory Neural Network (FSTNN) is a simple yet powerful pattern recognition tool developed by Neiberg and Casasent for use in an Automatic Target Recognition System. Since the FSTNN was developed, it has been used on various problems including speaker identification and space object identification. However, in these types of problems, the test set represents time series data rather than an independent set of points. Since the distance metric of the standard FSTNN treats each test point independently without regard to its position in the sequence, the FSTNN can yield less than optimal results in these problems. Two …
Space Object Identification Using Spatio-Temporal Pattern Recognition, Gary W. Brandstrom
Space Object Identification Using Spatio-Temporal Pattern Recognition, Gary W. Brandstrom
Theses and Dissertations
This thesis is part of a research effort to automate the task of characterizing space objects or satellites based on a sequence of images. The goal is to detect space object anomalies. Two algorithms are considered - the feature space trajectory neural network (FST NN) and hidden Markov model (HMM) classifier. The FST NN was first presented by Leonard Neiberg and David P. Casasent in 1994 as a target identification tool. Kenneth H. Fielding and Dennis W. Ruck recently applied the hidden Markov model classifier to a 3D moving light display identification problem and a target recognition problem, using time …