UC Riverside

Many applications require reliable, high precision state estimation while mitigating measurement outliers. This dissertation presents a novel state estimation approach to the challenge of preventing outlier measurements from affecting the accuracy and reliability of state estimation. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The most common method for outlier accommodation utilizes a Neyman-Pearson (NP) type threshold test in a (extended) Kalman filter (KF) to detect and remove residuals greater than a designer specified threshold. Such threshold based methods may use residuals arbitrarily close to the threshold, even when they are not needed to achieve an application's performance specification. Outlier measurements that pass the residual test (i.e., missed detections) results in incorrect information being incorporated into the state and error covariance estimates. Once the state and covariance are incorrect, subsequent outlier decisions may be incorrect, possibly causing divergence.

The major contribution of this dissertation is changing the focus from outlier detection, to looking for a subset of measurements which have minimum risk while achieving a lower bounded information for state estimation. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This dissertation derives and formulates the RAPS solution for outlier accommodation which applies to both linear and nonlinear applications. The approach is also extended to moving horizon state estimation problem. Global Navigation Satellite Systems (GNSS) and inertial measurements for moving vehicle state estimation are used as an example to show the performance of the proposed approach.