# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 5 Conclusions  
In this study, we investigated the usage of filter bank gating networks for the purposes of anomaly detection in deep space autonomous navigation with fused observable types. We specifically investigated methods from the areas of Multiple Model Estimation (MME) and Mixture of Experts (MoE). Existing methods did not tend to deal with the problem of measurement-fused estimation, so our work focused on developing an MME-based gating network that could be used to detect anomalies in a system, especially those relating to conflicting information from different measurement data types. This new gating network was designed to reward filters that produce measurement residuals that are consistent with the filter's uncertainty metrics while balancing this against a term that incorporates memory of previous filter weightings and ensures that the weightings are properly normalized. We then expanded this algorithm to deal with filters that only process a subset of the available measurement types, which is vital to dealing with the fused-sensor problem. After developing this new gating network, we tested it and a standard gating network against a numerical simulation based on the Insight spacecraft's Mars approach phase navigation with fused, optical-only, and radiometric-only data. In these results, we found that our new optimal gating network was able to detect statistically significant measurement mis-modeling--indicated by the gating network weights deviating from equality. Optical sensor biasing stood out clearly (especially as the size of the bias grew) due to there being no modeling in the filters that accounted for this type of biasing. Radiometric biasing was not as easily detectable, though, because the filters already accounted for clock errors, which were consistent in size with the introduced biasing. Essentially, the radiometric biasing was not statistically significant because our models already accounted for the possible presence of these level of errors. For dynamic mis-modeling, we introduced mismodeling into the attitude control thrusting and added new filters to the filter bank that better modeled the introduced level of dynamic uncertainty. We found that the gating network weights clearly identified the presence of the dynamic mis-modeling. We were also able to characterize the mis-modeling as dynamic in nature due to the network's preference for the large dynamic uncertainty versions of the filters and the fused filters did not indicate any problems with radiometric and optical information consistency. In each of our simulations we compared our results against the existing Magill gating network and found that it provided no means \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Filters** & **Ideal** & **Attitude Thruster Scenario** & **Process Noise Scenario** \\ \hline Radio Filter & 0.1666 & 0.0844 & 0.0961 \\ \hline Optical Filter & 0.1666 & 0.2510 & 0.1992 \\ \hline Fused Filter & 0.1666 & 0.0815 & 0.0742 \\ \hline Radio-Q Filter & 0.1666 & 0.1658 & 0.1945 \\ \hline Optical-Q Filter & 0.1666 & 0.2520 & 0.2431 \\ \hline Fused-Q Filter & 0.1666 & 0.1653 & 0.1929 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average Optimal Gating Network Weights for Mis-Modeled Dynamics Scenarios. to consistently identify mis-modeling and often selected filters that were mis-modeled as the optimal solution. Not being able to consistently detect the presence of mis-modeling, the Magill method also had no means to characterize anomalies like our optimal gating network is able to do. This characterization process is not automated at this point, but our gating network provides a good foundation that is flexible enough to adapt to specific mission setups and different types of anticipated anomalies. These results demonstrated that our new algorithm shows significant promise for its intended use-case, but we did identify areas to expand and improve this algorithm in the future.