Spatiotemporal radar target identification using radar cross-section modeling and hidden Markov models

We propose a target identification scheme exploiting the temporal dependency and spatial structure of radar cross-section (RCS) measurements in low-frequency, passive, or long-range surveillance radar systems. We employ target RCS modeling integrated into a hidden Markov model (HMM) with state-duration modeling. Assuming that the radar system ensures a sufficient sampling time, it is possible to use the spatial characteristics of airborne targets, because there is consistency between successively sampled RCS measurements. In addition, to exploit the whole temporal characteristic of the sequence of RCS measurements,which has rarely been considered in the literature, we adopt the HMM and target RCS modeling. To accomplish this task, we accurately develop target RCS models and establish the relationship between the target-sensor orientations, which are the hidden states of the HMM, and the corresponding RCS measurements. The proposed target identification scheme, which only uses the sequence of RCS measurements, is demonstrated with simulation results and an analysis for various signal-to-noise ratios and target-fluctuation models.