Page:Advanced Automation for Space Missions.djvu/348

 are naturally parallel(Vahey,1979).Technical • Extracts information in a useful form advance sin the following: are needed • Makes decisions. • Automatic techniques to rapidly correlate memory stored mapping and modeling information with visual and radar imagery obtained in orbital pass • Fast image enhancement and threshold techniques • Rapid cross-correlation techniques • Rapid boundary-determination techniques • Rapid Fourier transform techniques • Algorithms for improved automated data associations • High density rapid computers for use in space environment • Parallel processing computer techniques involving large wafers, advanced cooling techniques, advanced interconnection techniques between array elements, more logic functions between elements performed in one clock cycle and advanced direct data output from array to a central controller • Ability to load and unload imaging data in full, parallel manner at all stages of handling raw data • Investigation of possible use of optical processing techniques such as holographic process or integrated optics for satellites processing of imagery via world model • Techniques to rapidly, reliably and automatically update world model in satellite and on ground directly from image data • Advanced data compression and compaction techniques for data transmission and storage.

6.1.7 Smart Sensors Complex sensor configurations are required for both IESIS and Titan missions. A high degree of autonomous sensing capability is needed within the sensors themselves (Haye, 1979; Murphy and Jarman, 1979). These sensors must be smart enough to perform automatic calibrations, compensations, and to reconfigure themselves automatically tasks requiring advanced memory capabilities and operating algorithms (Schappell, 1980). Desirable characteristics of such smart sensors on satellites (Breckenridge and Husson, 1979) are: • Introduces no anomalies into data • Performs analytical and statistical calculations • Performs all operation in simplest form • Adapts (handles) new data acquisition and processing situations • Interactive sensor configurations • Adjusts to different environmental conditions The use of a world model in conjunction with smart sensors would confer an extraordinary degree of intelligence and initiative to the system. In order to mate sensors most efficiently with the world model, the model should itself possess models of the sensor components. Since the sunlight at Titan is weak and the planet cold, efficient, visible, and infrared sensors are also necessary. Technology requirements of smart sensors are: • Advanced efficient solid-state imaging devices and arrays • Sensor operation at ambient spacecraft temperature • Electronically tunable optical and IR filters • Advanced automatic calibration and correction techniques • Distributed processing sensors • Rapid, high responsivity detectors in near IR up to 3/am • Optimum set of sensors arrays for particular planetary mission • Sensor models • Silicon-based sensors with dedicated microprocessors and on-chip processing • Investigation of piezoelectric technology for surface wave acoustic devices • Sensor sequence control which can adapt to conditions encountered • Precision pointing and tracking sensor mounts.

6.1.8 Infi)rmation Extraction Tehniques Information can be extracted from sensory data originating from an object by recognizing discriminating features of the object. Such features are of three kinds: (1) physical (color), (2) structural (texture and geometrical properties), and (3) mathematical (statistical means, variance, slope, and correlation coefficients). Humans generally use physical and structural features in pattern recognition because they can easily be discerned by human eyes and other common means. However, human sensory organs are difficult to imitate with artificial devices so these methods are not always effective for machine recognition of objects. But by using carefully designed algorithms, machines can easily extract mathematical features of patterns which humans may have great difficulty in detecting. The algorithms will often involve a fusion of knowledge across multisensor data. As an example, the radiance observed from an object is a function of its reflectance, incident