Nonlinear least-squares estimation for sensor and navigation biases
Shawn M. Herman, Aubrey B. Poore · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Fusion of data from multiple sensors can be hindered by systematic errors known as biases. Specifically, the presence of biases can lead to data misassociation and redundant tracks. Fortunately, if an estimate of the unknown biases can be obtained, the measurements and transformations for each sensor can be debiased prior to fusion. In this paper, we present an algorithm that uses truth data for offline estimation of time invariant biases. Our approach is unique for two reasons. First, we explicitly avoid the use of fictitious "roll-up" biases and instead attempt to model the true sources of systematic errors. This leads to a highly nonlinear bias model that contains 18 unknown parameters. Second, we use the singular value decomposition (SVD) within our nonlinear least-squares estimator to automatically handle the issue of parameter observability. We also show how the SVD can be used to differentiate between absolute and relative bias estimates. Finally, we demonstrate that our algorithm can improve track accuracy, especially for mobile sensor platforms.