Offline calibration of an inertial measurement unit (IMU) can determine biases in the motions measured by the IMU while it is not in use. The offline calibration uses an expected motion measurement based on a motionless IMU as a reference from which the biases can be computed for a temperature. The bias and the temperature can be stored in a thermal table that can be updated and expanded over multiple calibration sessions to include the biases for a range of temperatures. A model relating the biases to temperature may be created based on the thermal table. For example, a curve-fit equation relating the bias as a function of temperature may be computed based on the values in the thermal table.

Presented herein are systems and methods using inertial measurement units (IMUs) for providing location and guidance, and more particularly for providing location and guidance in environments where global position systems (GPS) are unavailable or unreliable (GPS denied and/or degraded environments), and for such location and guidance being provided to projectiles, munitions, or rounds that are released, fired, or deployed from vehicles or weapons systems. More particularly, this disclosure relates to the use of a series of low-accuracy or low-resolution IMUs, in combination, to provide high-accuracy or high-resolution location and guidance results. This further relates to an electronics-control system for handing off control of the measurement and guidance of a body in flight between groups or subgroups of IMUs to alternate between high dynamic range/lower resolution and lower dynamic range/higher resolution measurement and guidance as the environment dictates.

An inertial measurement system comprising at least one sensor cluster comprising a plurality of inertial sensors for sampling at least one of acceleration and angular velocity of said at least one sensor cluster with respect to each axis in a plurality of axes of a reference frame, and for producing individual outputs associated with said at least one of acceleration and angular velocity, at least three of said inertial sensors said sampling with respect to each same respective said axis; and a processing engine for receiving said individual outputs, combining said individual outputs to yield respective combined outputs, detecting which of said individual outputs diverges from at least one of their inter-comparison, and its respective combined output, according to a decision rule, said processing engine configured to dynamically self-calibrate a parameter that includes individual scale factor of those said inertial sensors whose said individual outputs were detected to diverge.