Inertial Navigation Trajectory Correction via Turn Speed Estimation

The system detects correction events (turns, stops, starts) and uses them to generate correction signals that compensate for sensor drift and bias accumulation. The feedback mechanism continuously monitors navigation solution quality and applies corrections at identified correction events, resolving the contradiction by maintaining independent operation while preventing accuracy degradation through periodic correction.

The system pre-identifies correction events based on motion patterns before accuracy degradation becomes severe. By detecting turns, stops, and starts in advance and preparing correction signals at these predetermined moments, the system proactively prevents drift accumulation rather than reacting after accuracy is lost, maintaining both independence and precision.

The system uses the device's own motion sensors and detected correction events to generate correction signals without requiring external navigation sources. By self-servicing the navigation solution through internally-generated correction signals based on detected turns, stops, and starts, the system maintains positioning accuracy while minimizing power consumption and computational demands.

The system replaces expensive, power-intensive external navigation sources (GNSS receivers, detailed map databases, complex processing algorithms) with simple, low-cost correction events detection and basic correction signal generation. This substitution maintains adequate positioning accuracy for the application while dramatically reducing computational resources and power requirements.

The correction process is segmented into distinct correction events (turns, stops, starts) rather than attempting continuous correction. Each correction event is handled independently with simple detection and correction signal generation, avoiding the need for complex continuous algorithms. This segmentation maintains accuracy while reducing processing complexity to manageable levels.

The system changes the operational parameters of the correction process by working only at discrete correction events rather than continuously. By transitioning from continuous complex correction to event-based simple correction, the system achieves comparable accuracy with significantly reduced computational complexity and processing requirements.