Updating blending coefficients in real-time for virtual output of an array of sensors

An inertial measurement unit (IMU) comprising:

• a plurality of micro-electromechanical system sensors (MEMS sensors), each of the plurality of MEMS sensors having an output;
• a storage medium for storing calibration coefficients separately for each of the plurality of MEMS sensors, real-time coefficients for each of the plurality of MEMS sensors, and data blending instructions for blending the outputs of the plurality of MEMS sensors;
• and
• a processor, coupled to the storage medium and the plurality of MEMS sensors, configured to execute program instructions to:
• filter, at a frame rate, samples output by the plurality of MEMS sensors with a bandpass filter over a time scale characteristic of a type of error for the plurality of MEMS sensors to produce filtered samples;
• iteratively update a covariance matrix, at an accumulation rate, based on the filtered samples until a selected number of filtered samples have been processed,
• calculate, based on the covariance matrix, changes to the real-time coefficients to be applied to the output of each MEMS sensor of the plurality of MEMS sensors;
• apply, at the frame rate, the changes to the real-time coefficients;
• and
• calculate a blended output for the plurality of MEMS sensors based on the real-time coefficients.

A program product comprising a non-transitory computer-readable medium on which program instructions configured to be executed by at least one processor are embodied, wherein when executed by the at least one processor, the program instructions cause the at least one processor to perform a method comprising:

• at a frame rate,
• periodically storing samples of outputs of a plurality of sensors to produce stored samples;
• filtering the stored samples separately for each of the plurality of sensors with a bandpass filter over a time scale characteristic of a type of error for the plurality of sensors to produce filtered samples;
• storing the filtered samples;
• at an accumulation rate,
• iteratively updating a covariance matrix based on the filtered samples until a selected number of filtered samples have been processed,
• removing data from the covariance matrix for any of the plurality of sensors that have failed;
• and
• calculating, based on the covariance matrix, changes to real-time coefficients to be applied to the outputs of each sensor of the plurality of sensors;
• and
• at the frame rate,
• applying the changes to the real-time coefficients;
• and
• calculating a blended output for the plurality of sensors based on the real-time coefficients.