Continuous Mutual Correction from Multiple Sensors

I'm stumped on what exactly to search for; so here is a non-scientists description of the topic.

I've got multiple sensors of different types that are measuring the same physical value in different ways. The sensors are all subject to significant error or temporary unavailability, but in greatly different ways. I want to develop code that can use all available sensor data by maintaining slowly changing corrections for each sensor based on the other.

I would love guidance on what exactly to look for to begin a literature search on the topic. Thanks.

Notes based on comments:

• A silly example would be collection sampled data on an instrumented automobile's scalar velocity from:

• wheel rotation frequency sensor

• pitot type anemometer

• tri-cup anemometer

• MEMS accelerometer

• GPS

• The physical value being measured, the signal, is not periodic or in any way predictable. It ranges from 0.0 to Max, where max is greater than zero and noninfinite. It is relatively easy to define reasonable limits for max_value, max_delta_value and so on be observation and calculation from simplified Newtonian models.

• At least one sensor is subject to calibration/bias drift continuously. This should be at least 4 orders of magnitude below reported value, but in nonnegligible.

• At least one sensor is unbiased, calibrated and provides confidence range data along with sample data. Per sample error can be as much as 2 orders of magnitude greater than sample value.

• All sensors use wireless communications of data, so all samples are not reported. No error in measurement is introduced in transmission.

The broad area that studies these kinds of topics is called estimation theory. This is more a matter of statistics than of computer science, so you might want to take a look in some statistics textbooks and check out the Statistics.SE.

For instance, you might be interested in the Kalman filter.

That said, before one can devise a solution to your specific solution, we'd need a lot more information about what you know. Do you know anything about the joint distribution of their sensing? Do you know anything about the accuracy of each sensor (e.g., its variance)? Are all sensors calibrated and unbiased? Do you know anything about how the underlying physical value changes over time or the distribution it comes from? Do some self-study of the literature, and make sure you know the answers to those before asking on Stats.SE.

StackExchange Computer Science Q#47559, answer score: 2