Robust Adaptive Spatial-Temporal Algorithms for Clutter Rejection and Scene Stabilization
Small Business Information
ADVANCED SCIENCE & NOVEL TECHNOLOGY
28119 Ridgefern Court, Rancho Palos Verdes, CA, 90275
AbstractEfficient clutter suppression technology is of paramount importance for early detection of missile launches in the presence of highly structured solar-lit-clouds clutter. We propose to develop parametric and nonparametric spatial-temporal filteringalgorithms for use in background suppression for passive space-based sensors with chaotically vibrating line-of-sight. The algorithms will be robust to different forms of clutter and will not require prior information about clutter properties. Systemperformance will be demonstrated by simulation under a wide range of environmental conditions, viewing geometries, and sensor parameters. Realistic simulation models will include background imagery, sensor vibrations, model-based image projection, andsensor/algorithm dynamic response, while taking into account earth surface, atmosphere, and clouds for various meteorological, geographic and lighting conditions. We also propose to develop an adaptive, re-configurable clutter rejection system thatutilizes auto-tuning and auto-selection procedures for optimal configuration, reducing susceptibility to sensor vibrations and to dramatic changes in environmental conditions. These procedures will use an overall system quality metric that is a functionof current sub-system performance indices, including signal-to-noise ratio, clutter severity, false alarm rate, detection/declaration probabilities, and tracking quality. Meteorological information will also be used to predict transmission and propagationloss for target signals and to estimate background properties. In contrast to the best existing spatial image processing methods, the developed spatial-temporal rejection filters allow for suppression of severe clutter to the level of sensor noise in thepresence of vibrations. The filters not only reject backgrounds, but also, compensate for image translations and distortions. Moreover, an important feature of the developed system is the use of a re-configurable and adaptive structure that includes a bankof clutter rejection filters along with the auto-tuning and auto-selection procedures that allow for choosing the best possible configuration under the given environmental conditions. The algorithm uses meteorological information and other information onchanging illumination conditions to predict background properties. In addition, we anticipate that centralized and decentralized multi-spectral configurations will allow us to improve the overall detection performance of the system.The potential range of applications of the developed advanced spatial-temporal image processing algorithms and detection algorithms includes a new generation of tracking systems for missile defense, terrestrial reconnaissance, robotics, machine visionsystems, and certain medical imaging applications. The spatial-temporal image processing algorithms can be particularly effective in monitoring the human brain functioning by non-invasive techniques such as functional and dynamic Magnetic ResonanceImaging.
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