Sensor Fusion & Kalman Filter Diagnostic Sprint
SoliosAI supports engineering teams with Sensor Fusion & Kalman Filter Diagnostics, EKF/UKF assessment, innovation analysis, covariance behaviour review, sensor trust assessment, and degraded navigation-estimation evidence.
Sensor Fusion & Kalman Filter Diagnostics for Navigation Systems
Sensor Fusion & Kalman Filter Diagnostics help engineering teams understand how an estimation system behaves when measurements are noisy, uncertain, degraded, delayed, inconsistent, or partially unavailable. Navigation and autonomy systems can appear stable during normal conditions while still hiding issues in filter confidence, covariance behaviour, innovation consistency, measurement gating, sensor trust, or degraded-condition response.
SoliosAI supports sensor fusion diagnostics for autonomous systems, UAVs, robotics platforms, ground vehicles, maritime systems, and industrial navigation workflows. Assessment can include EKF/UKF behaviour, Kalman filter health, innovation and residual review, NIS/NEES-style consistency checks, covariance growth, overconfidence, underconfidence, sensor dropout response, and measurement rejection behaviour.
This type of diagnostic review can help teams identify whether a navigation or estimation system is stable, credible, overconfident, slow to recover, or sensitive to degraded measurements. It is useful when preparing technical review evidence, investigating abnormal filter behaviour, comparing sensor configurations, assessing navigation uncertainty, or deciding what should be tested next.
Depending on the available material, SoliosAI can work from diagnostic logs, filter outputs, simulation results, plots, screenshots, design-review notes, sensor summaries, or approved sample data. The goal is to produce practical findings, technical evidence, and next-step recommendations that help engineering teams understand estimator behaviour before filter issues become system-level risk.
Understand Filter Health Before It Becomes System Risk
The Sensor Fusion & Kalman Filter Diagnostics for Navigation and autonomy systems can appear stable while hiding issues in filter confidence, sensor trust, measurement gating, covariance behaviour, innovation consistency, or drift response. SoliosAI helps teams assess EKF/UKF and sensor-fusion behaviour so unstable, overconfident, inconsistent, or poorly tuned estimation systems can be identified and explained with clear technical evidence.
Useful for:
- EKF/UKF tuning and diagnostics
- Innovation and residual behaviour review
- Covariance, uncertainty, and consistency assessment
- Sensor trust, gating, and failure-mode investigation
What the Diagnostic Sprint Can Cover
Filter Health & Stability
Review of Sensor Fusion & Kalman Filter Diagnostics includes EKF/UKF behaviour, estimator stability, divergence risk, filter confidence, and whether state-estimation outputs remain credible under changing conditions.
Innovation & Residual Analysis
Assess innovation trends, residual behaviour, NIS/NEES-style consistency indicators, outliers, and whether measurements are being accepted or rejected appropriately.
Covariance & Uncertainty Behaviour
Review covariance growth, uncertainty confidence, overconfidence, underconfidence, sensor dropout response, and degraded estimation behaviour.
Sensor Trust & Measurement Gating
Evaluate sensor weighting, measurement gating, trust assumptions, sensor disagreement, and failure-mode behaviour across GNSS, IMU, lidar, radar, vision, odometry, or barometric inputs.
Typical Inputs and Outputs
SoliosAI can support Kalman filter and sensor-fusion diagnostics using material such as:
- EKF/UKF logs or diagnostic outputs
- Innovation, residual, NIS, or NEES plots
- Covariance and uncertainty plots
- Sensor-fusion configuration notes
- GNSS, IMU, lidar, radar, vision, odometry, or barometer data summaries
- Navigation error or trajectory comparison outputs
- Failure cases, drift events, or unstable filter behaviour
- Design-review questions or system concerns
Depending on the project scope, outputs may include:
- Filter-health findings summary
- Innovation and residual observations
- Covariance and consistency notes
- Sensor trust and gating recommendations
- Plots, metrics, or visual evidence where applicable
- Review-ready diagnostic summary
- Risk-focused findings and next-step recommendations
Extended Kalman Filter Diagnostics
Extended Kalman Filter diagnostics help assess whether an EKF is behaving consistently under changing system dynamics, nonlinear motion, degraded measurements, or uncertain sensor inputs. SoliosAI can review EKF behaviour, innovation trends, covariance growth, measurement gating, sensor trust, and filter confidence to identify signs of instability, overconfidence, divergence risk, or poor tuning.
Useful For:
- EKF tuning and stability review
- Innovation and residual behaviour analysis
- Covariance growth and uncertainty confidence
- Measurement gating and sensor rejection behaviour
- GNSS, IMU, lidar, radar, vision, odometry, or barometric sensor fusion
- Degraded navigation and sensor dropout scenarios
Unscented Kalman Filter Diagnostics
Unscented Kalman Filter diagnostics help assess how a UKF handles nonlinear estimation, uncertainty propagation, sensor disagreement, and degraded navigation conditions. SoliosAI can review UKF behaviour across predicted states, measurement updates, covariance consistency, innovation response, and sensor-fusion performance to identify whether the estimator is stable, credible, or becoming overconfident.
Useful For:
- UKF behaviour and nonlinear estimation review
- Sigma-point uncertainty propagation assessment
- Innovation, residual, NIS, or NEES-style consistency checks
- Covariance confidence and uncertainty behaviour
- Sensor trust, measurement weighting, and gating review
- Drift, dropout, or degraded-condition investigation
Data Handling & Engagement Boundaries
SoliosAI can begin with non-sensitive, non-classified, client-approved material such as problem statements, public system descriptions, sanitised logs, exported plots, simulation outputs, screenshots, or design-review questions. SoliosAI can work from anonymised or synthetic data to identify likely autonomy, navigation, simulation, or sensor-fusion risks before deeper engagement is considered.
Please read privacy disclaimer for more information.
Who This Is For
This capability is suitable for autonomy teams, robotics developers, UAV integrators, sensor-fusion engineers, navigation system developers, research organisations, and technical decision-makers who need clearer evidence of how estimation systems behave under real-world uncertainty, degraded sensors, drift, or changing mission conditions.
References
Explore related SoliosAI capabilities including UAV mission trajectory simulation, GNSS-denied and EW resilience testing, and verification and validation support.
Need to Understand Filter Behaviour Before the Next Review?
Discuss your sensor-fusion, Kalman filter, navigation-estimation, or autonomy diagnostic concern with SoliosAI.