Target classification by autoregressive modeling using range extent profiles

We present a novel algorithm based on autoregressive (AR) modeling for analyzing the separability and classification of ground targets using range extent data. Given a range extent profile of a target, we estimate the appropriate model order for the data using the Akaike information criterion (AIC). This prevents under-filling or over-fitting of the data. Previous researchers have shown that even if the actual process is not an AR process, the AR model serves as a reasonable model for a wide class of practical problems. Error modeling for the range extent data is extremely difficult due to the complex nature of the scattering process, uncertainties in the channel, sensor state, target dynamics, and estimation of the range extent from a range profile. Therefore, our data-driven approach provides a useful algorithm for analyzing target model separability and classification. We apply the algorithm to simulated range extent data and obtain good classification results. We plan to test the algorithm further with real range extent data.