Sensor Fusion & Kalman Filter Diagnostic Sprint
SoliosAI provides focused sensor fusion and Kalman filter diagnostic support for teams that need clearer evidence of estimation behaviour, filter health, sensor trust, innovation consistency, covariance confidence, and degraded navigation performance.
Understand Filter Health Before It Becomes System Risk
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 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, weighted, 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
Typical material that can support a sensor fusion and Kalman filter diagnostic sprint may include:
Typical Inputs:
- EKF/UKF logs or diagnostic outputs
- Innovation, residual, NIS, or NEES plots
- Covariance and uncertainty plots
- Sensor-fusion configuration notes
- Navigation error or trajectory comparison outputs
- Failure cases, drift events, or unstable filter behaviour
- Design review questions or system concerns
Typical Outputs:
- Filter-health findings summary
- Innovation and residual observations
- Covariance and consistency notes
- Sensor trust and gating recommendations
- Plot, metric, or visual evidence notes where applicable
- Risk-focused technical recommendations
- Review-ready diagnostic summary
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, design-review notes, or synthetic examples. For early reviews, SoliosAI can work from approved material to identify likely autonomy, navigation, simulation, or sensor-fusion risks before deeper engagement is considered. Sensitive, classified, confidential, or regulated information should not be shared until suitable engagement terms and data-handling arrangements are agreed.
Who This Is For
This service is suitable for engineering teams, autonomy developers, UAV and robotics integrators, mining technology groups, maritime autonomy teams, research organisations, sensor-fusion engineers, navigation system developers, and technical decision-makers who need clearer evidence of filter health, estimation consistency, sensor trust, covariance behaviour, or degraded navigation performance.
Useful for:
- UAV and robotics teams
- Navigation and sensor-fusion engineers
- Autonomy and robotics developers
- Mining technology and industrial autonomy groups
- Maritime autonomy and remote operations teams
- Research teams reviewing EKF/UKF behaviour
- Teams investigating drift, uncertainty, or unstable estimation
Sensor fusion Kalman filter diagnostics can help teams identify estimation issues before they become system-level risk. A diagnostic sprint can support EKF/UKF behaviour review, innovation and residual analysis, covariance consistency assessment, sensor trust review, measurement gating checks, and technical recommendations for autonomous systems operating under degraded, uncertain, or sensor-limited conditions. Explore related SoliosAI capabilities including Kalman filter and sensor fusion diagnostics, GNSS-denied and EW resilience testing, UAV mission trajectory simulation, 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.