Radar cross-section data encoding based on parametric spectral estimation techniques
Parametric modeling has many applications. These applications include data estimation and interpolation, modern spectral estimation, and data encoding. This research applies parametric modeling to radar cross section data in an attempt to encode it as well as preserve its spectrum. Traditionally, ra...
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| Format: | Others |
| Language: | en |
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/43340 http://scholar.lib.vt.edu/theses/available/etd-06162009-063346/ |
| Summary: | Parametric modeling has many applications. These applications include data estimation and interpolation, modern spectral estimation, and data encoding. This research applies parametric modeling to radar cross section data in an attempt to encode it as well as preserve its spectrum. Traditionally, radar data has been processed through Fourier spectral estimation techniques. These methods not only require large amounts of data, for good spectral estimates, but assume the unobserved data values are zero which leads to spectral smearing. Modern spectral estimation methods alleviate these problems by basing the spectral estimate on a parametric model fit to the data set. The spectral estimate is then derived from the parameters of the model. For models which give a good fit to the data, a good spectral estimate can be made.
The most common parametric models are the autoregressive moving-average (ARMA), the moving-average (MA) and the autoregressive (AR) model. These models represent filters, which when excited by a white Gaussian noise sequence give some output sequence. If the parameters of the models and the noise sequence are selected properly, a desired output data sequence can be modeled. The variance of the white noise is often small compared to the variance of the data sequence. This means that the model parameters plus the noise can be stored with fewer bits than the original data sequence while maintaining the same amount of accuracy in the data. The model parameters and noise sequence can be used to reproduce the original data sequence. Further, if only the spectrum of the data is of interest, only the noise variance plus the parameters need to be stored. This could lead to an even greater amount of data reduction.
Most high resolution radar data applications require only that the spectrum of the data be preserved which makes modern spectral estimation appealing. This research project applies parametric modeling and modern spectral estimation to high resolution radar data as a means of encoding it. Several different parametric modeling techniques are evaluated to see which would be most useful in radar data encoding. The Burg AR parametric model was chosen due to its computational efficiency and its good spectral estimates. The Burg method applied to two radar range profile data sets gave a reduction in data storage by a factor of four. Further encoding was achieved by fitting the Burg AR parameters to a set of basis functions. This produced an additional data reduction by a factor of 36, for a total compression factor of 144. The latter led to some distortion of the high resolution range profiles, yet targets were still sufficiently characterized. === Master of Science |
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