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 backscattered 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.