1. Field
The present specification generally relates to sensor calibration.
2. Technical Background
Many systems incorporate vector-valued sensors to measure directional physical quantities. Such sensors include multi-axis magnetometers, multi-axis accelerometers, sonar arrays, radar arrays, etc. These sensors are subject to inaccuracies resulting from both internal and external sources.
Systems often make use of multi-axis or vector-valued sensors to measure physical quantities. Since the position of the sensor relative to the physical quantity being measured determines the sensor output vector, it can be considered a positional vector-valued sensor. Thus, positional vector-valued sensors are a class of sensor which has a relationship between relative sensor orientation and source orientation that produce a vector output. FIG. 1A illustrates a vector-valued sensor. The sensor, in orientation (101), senses physical quantity (102). This yields a sensor reading (103) that is relative to the body of the sensor. Similarly, the sensor, when placed in another orientation (111), senses the same physical quantity (102), and this time yields sensor reading (113) as shown in FIG. 1B. These sensors can exhibit inaccuracies stemming from a variety of sources. Sources of sensor inaccuracy may include internal sensor error sources such as sensor scale and bias effects, non-linear sensor output, asymmetric sensor output, sensor non-orthogonality, and sensor cross-axis effect or external error sources such as distortion or attenuation of the physical quantity being measured. For example, a multi-axis magnetometer may exhibit inaccuracies from imperfections in the sensor itself (scale/bias, sensor non-linearity, cross-axis effect, etc.) as well as from magnetic field distortion due to soft-iron effects of nearby ferrous system components or hard-iron effects of nearby magnetic sources such as motors, speakers, or current carrying conductors.
Traditionally, error in vector-valued sensors is accounted for in two ways. First, one might carefully design and place the sensor within the system to minimize or shield the sensor from external error sources. Second, one might account for internal sensor error sources by treating the system as a whole and using computational transformations such scaling, translating, skewing, and rotating the sensor output vectors to minimize error. These methods, however, can vary in effectiveness because the removal of external disturbances is sometimes not feasible (for example, if a system needs a battery to be near the magnetometer due to space constraints). Alternatively, sensors with non-linear, asymmetric, or jagged response properties cannot be effectively corrected using simple transformations of the data as a whole. In particular, magnetometers can exhibit errors of this form when there are a variety of objects nearby which can cause magnetic disturbances, as is often unavoidable in small electronic circuits. Sensors can also exhibit these effects due to imperfections in the manufacturing and assembling of their components. Because simple transformations affect all the data evenly, they are unable to account for any errors that only show up in a portion of the data. Other systems/method for solving sensor error are provided in U.S. patent application Ser. No. 13/478,757: Sensor Devices Utilizing Look-Up Tables for Error Correction and Methods Thereof, assigned to Yost Engineering, Inc. and hereby incorporated by reference, but even greater accuracy is desired.
Therefore, there is a need in the art to provide a better system/method of correcting error of positional vector-valued sensors, particularly in cases in which the error can not be canceled out and exhibits non-linear, asymmetric, or jagged properties.