Paper
23 December 1980 Application Of An Iterative Feature Matching Algorithm To Terminal Homing
E. Noges, A. M. Savol, A. J. Witsmeer, D. Moerdyke
Author Affiliations +
Abstract
Target recognition in the terminal homing scenario consists of matching a set of sensed features with a set of reference features in the prestored reference feature map. An efficient feature matching algorithm, MACHAL, is described. This iterative algorithm employs clustering of feature metrics rather than exhaustive correlation calculations between reference and sensed features. Clustering, followed by data thinning, quickly reduces both reference and sensed data sets and thereby reduces the computational burden. MACHAL is a general algorithm which is capable of matching feature vectors of arbitrary dimension. The computational requirements increase with the dimension of the feature space and with thE increasing number of feature vectors in the sensed and reference feature sets. In this paper, MACHAL is applied to a low order feature matching in a relatively sparse feature space, characteristic of terminal homing problems. A probability model for the algorithm is developed and its validity tested by Monte Carlo simulation. Upper bounds for the clustering threshold and for the noise variance are developed using the probability model. The performance of the algorithm is evaluated by assessment of match accuracy, and robustness to noise resulting from typical sets of sensed and reference scenes. The application of MACHAL to higher order feature space is demonstrated.
© (1980) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. Noges, A. M. Savol, A. J. Witsmeer, and D. Moerdyke "Application Of An Iterative Feature Matching Algorithm To Terminal Homing", Proc. SPIE 0238, Image Processing for Missile Guidance, (23 December 1980); https://doi.org/10.1117/12.959153
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Cited by 1 scholarly publication.
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KEYWORDS
Monte Carlo methods

Image processing

Missiles

Target recognition

Sensors

Detection and tracking algorithms

Feature extraction

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