# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
###### Abstract
Control distance metrics and related optimal-control-based filtering techniques [18; 19; 20; 21; 22; 23; 24; 25] can be used to both detect anomalies and reconstruct them as optical control policies; however, they tend to focus on dynamic anomalies alone, while there are many different approaches to the more general anomaly detection and characterization problem, we are looking for an approach that accomplishes the following: (1) it is applicable to deep-space navigation problems, (2) it could feasibly be used in an autonomous navigation system, (3) it can both detect an anomaly and be used to characterize and diagnose the cause of the anomaly, (4) it  
Figure 1: An overview of the anomaly detection and characterization process via filter bank gating network weights with example errors and filter setups. can deal with many different types of anomalies that impact navigation solutions (e.g., mis-modeled dynamics, measurement biasing, issues with sensor fusion, over-confident uncertainty metrics, etc. ), (5) and it can be adapted as necessary to best fit the spacecraft's specific mission criteria. Given these requirements, we pursued a filter bank approach for this research given these methods are naturally adaptable and flexible, so they provide a good foundation from which to address all of our different requirements for this algorithm. Application of the filter bank methods (e.g., MME and MoE) to spacecraft navigation has been covered to some extent in the existing literature. Various studies that use MME-based methods tend to focus on making algorithms that are robust to model errors [26; 27; 28; 29; 30]. Similarly, MoE-based studies tend to focus on developing methods that are robust to anomalies in the system. This includes the work of [PERSON], [PERSON], and [PERSON] [31], who provided a comparison of MoE and MME-based methods and how rapidly each could identify the correct mode of the observed system via gating network weights. Furthermore, the MME and MoE approaches have been explicitly applied to the problems of anomaly detection and characterization [32; 33], while the existing literature covers a wide range of applications, they ignore an important component of our presented problem--dealing with the fused-sensor problem. Specifically, we seek a filter bank-based method that supports the inclusion of filters in the bank that only process a subset of the available data types (e.g., radiometric-only, optical-only, etc.). Beyond this we seek a method that can identify and characterize when anomalies occur, including when different data types conflict with each other. This paper will focus on developing an algorithm that meets this description. Section 2 provides an overview of filter bank methods and their relation to autonomous navigation of deep-space spacecraft. In Section 3, we define and derive a new weighting scheme to use with filter banks, which is designed for anomaly detection and characterization. Section 4 presents the results and analysis from numerical simulations based on the InSight spacecraft Mars approach where different weighting schemes for filter banks are used to identify mis-modelings in the system. Finally, Section 5 provides a summary of this research and some discussion of possible avenues for future work.