A Lower Bound on Prediction Errors for Randomly Accelerating Targets

Abstract

A lower bound exists for the accuracy with which the future position of a randomly accelerating target can be predicted. This bound applies even when noiseless data is optimally processed. In this correspondence, the prediction error bound is evaluated and presented as a universal curve for the case of a stationary random acceleration process with an exponential autocorrelation function.