Abstract
Infrared imaging is widely applied in the discrimination of spatial targets. Extracting distinguishable features from the infrared signature of spatial targets is an important premise for this task. When a target in outer space experiences micro-motion, it causes periodic fluctuations in the observed infrared radiation intensity signature. Periodic fluctuations can reflect some potential factors of the received data, such as structure, dynamics, etc., and provide possible ways to analyze the signature. The purpose of this paper is to estimate the micro-motion dynamics and geometry parameters from the observed infrared radiation intensity signature. To this end, we have studied the signal model of the infrared radiation intensity signature, conducted the geometry and micro-motion models of the target, and we proposed a joint parameter estimation method based on optimization techniques. After analyzing the estimation results, we testified that the parameters of micro-motion and geometrical shape of the spatial target can be effectively estimated by our estimation method.
Highlights
Spatial target recognition is a significant problem in many scientific and engineering fields, such as precise guidance systems and space surveillance systems
Infrared imaging technology is widely used in spatial target recognition systems
The results show that the projection area sequence of the spatial target has the same fluctuation characteristics as the infrared radiation intensity sequence
Summary
Spatial target recognition is a significant problem in many scientific and engineering fields, such as precise guidance systems and space surveillance systems. Radar signals are susceptible to interference and application scenarios are limited This prompted us to study infrared signals for the analysis and extraction of spatial target micro-motion features. From the accurate mathematical description of the observed infrared signature, it is feasible to obtain the complete signal of the target projection area and estimate the micro-motion parameters (including the spin rate, the precession rate, and the nutation angle, etc.). The problem of sparse representation is suitable to be solved by the “relaxed” convex optimization method, and the regularization parameters are introduced This method is limited to the case targets having sparse features, and it does not have universal adaptability. We propose a joint parameter estimation method with optimization techniques of target micro-motion dynamics and geometrical shape parameters from IR intensity features, which makes it easier to extract useful information when constructing classifiers or other predictors in IR space applications.
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