# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 4 Results
### Performance with Measurement Biasing
Figure 7:2 Position error (blue) and RTPC uncertainty (1-\(\sigma\) (orange) and 3-\(\sigma\) (red)) for fused filter in Large Radiometric Bias setup. biasing as compared to the fused filter. Even though this result is intuitive, we would prefer the gating network be able to identify the presence of the mis-modeling and help isolate its root cause. Our optimal gating network better exhibits this type of behavior. The weights from our optimal gating network for these radiometric data bias scenarios are shown in Figure 7:3 We clearly see through these results that radiometric measurement biasing is more difficult to detect as compared to optical measurement biasing. The type of radiometric biasing that we introduced is consistent with a clock bias, and because our filter is setup to estimate clock states, the filters themselves are able to deal with the biasing as long as that biasing is not too large of a clock error (relative to our a priori clock uncertainty modeling). When the biasing is detected, the weights are still able to identify that the optical-only filter is the best behaving filter since it does not process the biased data. This result is significant since it can be used in analysis to point to the root cause of the anomaly being radiometric in nature or another similar problem that would primarily disadvantage radiometric-based tracking over optical-based tracking. The effects of smaller radiometric biases are not easily distinguishable, but this goes back to the issue of the clock and other parameters absorbing their effects. By adjusting the filter models, we could make the gating network more sensitive to radiometric biases, but the filter may then be less robust to the biases themselves. As an alternative, we could possibly add new filters to the bank that vary modeling for the parameters that absorb radiometric biases (e.g., clock modeling). This proposed extension to this investigation is left as future work. For each of the scenarios run to this point, we computed their average optimal gating network weight over the full data arc and summarized those results in Figure 7:4 These average values give a general sense of the priority of the filters across the full data arc. These results really underline the conclusions that we have made up to this point:  
Figure 7:5 Optimal gating network weights with radiometric measurement biasing. Small (solid lines), medium (dotted lines), and large (dashed lines) biasing cases are included. Figure 7:6 Magill gating network weights with biased radiometric measurements. Small (solid lines), medium (dotted lines), and large (dashed lines) biasing cases are included. When all filters in the bank are properly modeled, each of the weights stays close to the ideal \(1/N\) value, thus indicating nominal filter bank behavior (i.e., no anomaly is detected). * Optical biasing is detectable via our optimal gating network at all three magnitudes (with detectability increasing with the size of the bias), and the gating network clearly prefers the radiometric-only solution over the the other two (with the fused filter performing the worst of all). This result indicates that the anomaly is likely related to optical data quality and that specifically the optical and radiometric data are inconsistent with one another. * Given our filter setup, the radiometric biasing is not as easily detectable via our gating network. This makes sense because the filters were setup to estimate parameters that mimic the biasing that we injected, so essentially the biasing is not anomalous under our model unless it becomes too large. As it becomes larger, the optical-only solution does become preferred, which indicates the biasing is becoming too large for what our filters are modeling. Through these results, we have found that the Magill gating network is largely insensitive to measurement biasing in radiometric-optical fused deep space navigation. When biasing occurs, the Magill weights consistently prefer the fused filter even when it is corrupted by the biased information.