Adaptive Spectral Density Adjustment for Inertial Navigation Filters

The patent implements a dynamic spectral density model that transitions from static to adaptive based on environmental conditions. The system uses a disturbance detector to monitor environmental parameters and dynamically adjusts the spectral density value accordingly - maintaining low values during steady state for accuracy, and increasing values during disturbed conditions to properly characterize IMU errors. This dynamic adaptation resolves the contradiction by making the spectral density flexible rather than fixed.

The system changes the spectral density parameter based on detected environmental conditions. When disturbances are detected (through temperature sensors, vibration sensors, or other environmental monitors), the spectral density value is increased to account for higher IMU error characteristics. This parameter change allows the filter to maintain accuracy in steady state while becoming robust in disturbed environments, directly resolving the measurement precision versus adaptability contradiction.

The system dynamically adjusts spectral density based on real-time environmental monitoring rather than using a fixed high value. During steady state, the spectral density remains low to maintain accuracy and reduce reliance on noisy external sensors. When disturbances are detected, the spectral density increases to improve response speed and robustness. This dynamic behavior resolves the contradiction between response speed and accuracy by adapting the spectral density to current operating conditions.

The system periodically monitors environmental conditions (temperature, vibration, etc.) and adjusts the spectral density accordingly. This periodic detection and adjustment mechanism ensures that the spectral density is high only when needed (during disturbed conditions) and low during steady state, resolving the contradiction by applying high spectral density intermittently rather than continuously.

The patent implements a dynamic model selection mechanism that switches between simple steady state models and more complex disturbed environment models based on detected conditions. The disturbance detector monitors environmental parameters and triggers model switching when disturbances are detected. This dynamic model adaptation maintains simplicity during steady state while ensuring reliability during disturbed conditions, resolving the contradiction between model complexity and navigation solution accuracy.

The system creates a universal error model framework that can operate in both steady state and disturbed environments by incorporating conditional logic. The model uses environmental sensors to determine operating conditions and automatically adjusts its behavior - using simple steady state assumptions when appropriate and transitioning to disturbed environment characteristics when needed. This multi-functional approach resolves the contradiction by making a single model adaptable to multiple operating conditions.