MEMS fabrication technology has facilitated the implementation of a broad variety of low-cost miniature sensors, including those for measuring linear and angular rates of objects-of-interest. Earlier work has focused on component-level sensor fabrication issues and proof-of- concept verification of their sensing ability. This prior work has provided the foundation for design and performance assessment of the next generation of multi-axis embedded MEMS sensors at a subsystem level. Subsequently, this will lead to the fabrication of an integrated self-calibrating 6 degree-of-freedom (DOF) strapdown intertial sensor assembled in a low-cost miniature package. This in turn will lead to a variety of applications that are currently unrealistic because of cost-weight-power considerations. This particular effort is directed toward establishing the feasibility of extracting additional information from a MEMS sensor by appropriately exciting a single-axis Coriolis sensor, for example, to generate optimum angular velocity and angular acceleration estimates, whereas prior studies have shown only the ability to generate approximate angular acceleration estimates, whereas prior studies have shown only the ability to generate approximate angular rotational velocity measurements. This work entailed the dynamic modeling of a representative MEMS sensor and several different angular velocity and angular acceleration driving functions in a MATLAB-based simulation. The corresponding raw sensor outputs were then optimally processed to concurrently generate estimates of both angular velocity and angular acceleration. The graphical results form these simulation studies are included to show the benefit of physically co-locating a digital computing element with the MEMS sensor, thereby facilitating the creation of a new generation of digital smart sensors, that will be capable of self-calibration based performance deterioration assessment, fault detection and recovery.
A broad variety of passive ranging algorithms are currently being developed and enhanced at NASA Ames and elsewhere. Some of the factors resulting in algorithm variability include (a) number of sensors (e.g., stereo), (b) type of input sensors (e.g., multispectral), and (c) output display needs (e.g., history vectors superimposed on raw video). This paper describes a cost- effective, real-time general purpose (reprogrammable) computationally scalable digital processing architecture that enables researchers to perform comparative evaluations in a laboratory or field environment. Performance benchmark studies indicate that the passive ranging algorithm developed at NASA-Ames can be executed by a 32 processor based shared memory multiprocessor architecture, implemented on two (9U) VME boards.