We illustrate the importance of the interleaver in holographic data storage (HDS) systems affected by burst errors. We
use the Gilbert-Elliott channel model to generate pages with burst errors and investigate a toroidal interleaving scheme.
Classification and rejection tests were performed on the 10-class MSTAR database. To compare our performance, we first summarize relevant prior work. Shift-invariant magnitude Fourier transform (FT) features were used in the feature space trajectory (FST) classifier to classify the 10-class MSTAR data with variants and to reject confuser images and clutter chips. No prior work has addressed this. Implication of various SAR preprocessing on performance is addressed. In confuser rejection, 2 standard confusers (used in prior work) and 2 new confusers are addressed. We are the first to extend confuser rejection tests to 8 target classification with variants and 2 confuser rejection. Finally, clutter rejection while classifying the 10 targets is addressed.
A study was conducted on the use of morphological processing for detection in Uncooled Infra Red (UCIR) data using the Clutter type 1 and Clutter type 2 databases. The CMO algorithm was used with various modifications. A fixed minimum peak value was used rather than a fraction of the maximum peak per image. This is much more realistic, since many real scenes will not contain targets. One-dimensional directional structuring elements (SEs) were used for the Close Minus Open algorithm. We used the range of gray levels within the output blob peaks (blob analysis) and larger windows in peak sorting to reduce false alarms. A new dilation minus erosion morphological algorithm gave the best result.