MULTISENSOR ARCHITECTURES AND ERROR PROPAGATION The purpose of this chapter is to extend the data acquisition error analysis of the preceding chapters to provide understanding about how errors originating in multisensor architectures combine and propagate in algorithmic computations. This development is focused on the wider applications of sensor integration for improving data characterization rather than the narrower applications of sensor fusion employed for data ambiguity reduction. Three diverse multisensor instrumentation architectures are analyzed to explore error propagation influences | Multisensor Instrumentation 6a Design. By Patrick H. Garrett Copyright 2002 by John Wiley Sons Inc. ISBNs 0-471-20506-0 Print 0-471-22155-4 Electronic 8 MULTISENSOR ARCHITECTURES AND ERROR PROPAGATION 8-0 INTRODUCTION The purpose of this chapter is to extend the data acquisition error analysis of the preceding chapters to provide understanding about how errors originating in multisensor architectures combine and propagate in algorithmic computations. This development is focused on the wider applications of sensor integration for improving data characterization rather than the narrower applications of sensor fusion employed for data ambiguity reduction. Three diverse multisensor instrumentation architectures are analyzed to explore error propagation influences. These include sequential multiple sensor information acquired at different times homogeneous information acquired by multiple sensors related to a common description and heterogeneous multiple sensing of different information that jointly describe specific features. These architectures are illustrated respectively by multisensor examples of airflow measurement through turbine engine blades large electric machine temperature modeling and in situ material measurements in advanced process control. Instructive outcomes include the finding that mean error values aggregate with successive algorithmic propagation whose remedy requires minimal inclusion. 8-1 MULTISENSOR FUSION INTEGRATION AND ERROR The preceding chapters have demonstrated comprehensive end-to-end modeling of instrumentation systems from sensor data acquisition through signal conditioning and data conversion functions and where appropriate output signal reconstruction and actuation. These system models beneficially provide a physical description of instrumentation performance with regard to device and system choices to verify fulfillment of measurement accuracy defined as the complement of error. Total instru- 169 170 MULTISENSOR ARCHITECTURES AND .
