Aspect angle estimation using fixed-rate Hidden Markov Models

This paper presents a new method of estimating the orientation (relative to the sensing radar) of a ballistic missile using low resolution Radar Cross Section (RCS) measurements. The estimation is accomplished by combining a multi-aspect feature based Hidden Markov Model (HMM) with a low fidelity RCS model of the missile. The RCS model of the missile links the true RCS to a particular orientation or aspect angle. Utilizing this relationship with the state estimation capability of a HMM, the sequence of RCS measurements made by a radar can be decoded to estimate the orientation of the missile. The research and data presented in this paper show that a trained HMM can estimate the orientation of a ballistic missile under constant pulse repetition frequency (PRF) and signal-to-noise ratio (SNR) conditions. Results are shown for 1 Hz, 5 Hz and 10 Hz PRF for varying SNR.