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.

[1]  Gernot A. Fink,et al.  Markov Models for Pattern Recognition , 2014, Advances in Computer Vision and Pattern Recognition.

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  L. Carin,et al.  Adaptive multiaspect target classification and detection with hidden Markov models , 2005, IEEE Sensors Journal.

[4]  Mark A. Richards,et al.  Principles of Modern Radar: Basic Principles , 2013 .

[5]  Lawrence Carin,et al.  Multi-aspect target identification with wave-based matching pursuits and continuous hidden Markov models , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[6]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[7]  Jian Li,et al.  Efficient mixed-spectrum estimation with applications to target feature extraction , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[8]  Zheng Bao,et al.  Radar target recognition based on peak location of HRR profile and HMMs classifiers , 2002, RADAR 2002.

[9]  Feng Zhu,et al.  Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets , 2007, IEEE Transactions on Signal Processing.

[10]  Lawrence Carin,et al.  Markov modeling of transient scattering and its application in multi-aspect target classification , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[11]  Lawrence Carin,et al.  Hidden Markov models for multiaspect target classification , 1999, IEEE Trans. Signal Process..

[12]  Zheng Bao,et al.  Radar HRRP target recognition based on dynamic multi-task hidden Markov model , 2011, 2011 IEEE RadarCon (RADAR).

[13]  L. Carin,et al.  Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits , 2001 .

[14]  Lawrence Carin,et al.  Rate-Distortion Analysis of Discrete-HMM Pose Estimation via Multiaspect Scattering Data , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.