Statistical Analysis of Split Spectrum Processing For Multiple
Target Detection
This work provides a statistical analysis of the performance of split spectrum
processing (SSP) for the detection of multiple targets using data consisting of simulated flaw signals
added to experimentally obtained back scattered grain noise. The investigation is performed under two
conditions: known a priori target spectral characteristics (i.e., center frequency and bandwidth) which,
in turn, identifies the optimal spectral range for processing, and adaptively obtaining the processing
frequencies using group delay moving entropy. The group delay moving entropy method was introduced to
select the optimal frequency regions for SSP when detecting multiple targets. The effectiveness of this
technique is statistically demonstrated in this paper. The performance is measured in terms of
normalized signal-to-noise ratio (SNR) and probability of target detection. SSP with known target
information yields a slightly higher probability of detection compared to SSP using group delay moving
entropy, while both cases achieve comparable SNR enhancement. The SSP results were also compared with the
corresponding bandpass filter outputs, which show superior performance for SSP for a wide range of
simulation parameters.
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