Signal abstractions in the machine analysis of radar signals for ice profiling

Describes the design and implementation of an automated system for interpreting impulse radar signals for ice thickness profiling. The authors have adopted an integrated approach which includes numeric computation in the form of deconvolution filtering with rule-based classification of signal features at multiple levels. Noise reduction and deconvolution techniques are used to enhance the radar signals for better resolution of overlapping events. Motivated by human perceptual (visual) knowledge, a hierarchy of data structures is constructed as representations of signal characteristics at various levels of abstraction. Classification rules, based on the protocols collected from an expert, physical constraints on the helicopter motion and the nature of the radar signals are used to produce the current signal interpretation. A prototype system has been implemented on the Symbolics Lisp Machine and tested on real data