Abstract
Knowledge of the propagation of sensor errors in strapdown inertial navigation is crucial for the design of inertial and integrated navigation systems. The propagation of initialization errors and deterministic sensor errors is well covered in the literature. If considered at all, the propagation of inertial sensor noise has typically been assessed for un-correlated (white) Gaussian noise. Real inertial sensor noise, however, is time-correlated (colored) and best described by a combination of different stochastic processes. In this paper, we demonstrate how a navigation system’s response to colored noise input differs from the response to bias-like or white noise inputs. We present a method for assessing the navigation error from various inertial sensor noise processes without the need for time-consuming Monte Carlo simulations and demonstrate its application and validity with real sensor data. The proposed method is used to determine in which scenarios the sensor’s real noise can be approximated by simple white Gaussian noise. The results indicate that neglecting colored sensor noise is justified for many applications, but should be checked individually for each sensor configuration and mission.
Highlights
Selecting suitable inertial sensors for an inertial or integrated navigation system is a crucial step in the system’s design
The different responses of the strapdown error dynamics to excitation by different noise processes have been derived in the previous section
We demonstrate the approximation of the navigation errors from sensor noise of an exemplary FOG inertial measurement unit (IMU)
Summary
Selecting suitable inertial sensors for an inertial or integrated navigation system is a crucial step in the system’s design. The often-utilized white noise model represents only one of the different processes, namely the angular random walk for gyroscopes, respectively, velocity random walk for accelerometers This obvious discrepancy between the typical modeling and real sensor behavior raises two questions that shall be answered within this paper: How do the these sensor noise processes propagate through the strapdown inertial navigation?. Such a numerical simulation can provide highly accurate results, but requires detailed modeling, is time-consuming and provides little insight into the underlying mechanisms compared to the analytical modeling Within this manuscript, we present a more basic and simple-to-use method for evaluating the navigation errors from a sensor’s noise properties. The results are used to determine for which applications and under what conditions the various noise processes may be neglected compared to the white noise components
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