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Proceedings Paper

Helicopter detection and classification using hidden Markov models
Author(s): Walter S. Kuklinski; Sean D. O'Neil; Laurens D. Tromp
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Paper Abstract

Hidden Markov models (HMMs) are probabilistic finite state machines that can be used to represent random discrete time data. HMMs produce data through the use of one or more `observable' random processes. An additional `hidden' Markov process controls, which of the `observable' random processes is used to produce an individual data observation. Helicopter radar signatures can be represented as quasi- periodic 1D discrete time series that can be analyzed using HMMs. In the HMM helicopter detection and classification algorithm developed in this study, the states of the `hidden portion' of the HMM were used to represent time dependence alignments between the radar and helicopter rotor structures. For example, the times when specular reflections occur were used to define a `blade-fish' state. Since blade- flash frequency, and the corresponding non-blade-flash state duration, is an important feature in helicopter detection and classification. HMMs that allowed direct specification of state duration probabilities were used in this study. The HMM approach was evaluated using X-Band radar data from military helicopters recorded at Ft. A.P. Hill. After initial adaptive clutter suppression and blade-flash enhancement preprocessing, a set of approximately 1,000 raw in-phase and quadrature data records were analyzed using the HMM approach. A correct target classification rate that varied between 98% for a PRF of 10 KHz to 91% at a 2.5 KHz PRF was achieved.

Paper Details

Date Published: 27 July 1999
PDF: 10 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999); doi: 10.1117/12.357152
Show Author Affiliations
Walter S. Kuklinski, MITRE Corp. (United States)
Sean D. O'Neil, MITRE Corp. (United States)
Laurens D. Tromp, MITRE Corp. (United States)

Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

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