Tracking System State Estimator for Moving Platform Positioning
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Solution Overview
Problem
Inertial navigation systems (INS) and electromagnetic tracking systems (EMT) face inaccuracies due to integration drift and multipath effects, respectively, which degrade tracking performance, especially when using narrow beam antennas to locate moving platforms.
Innovation Solution
Combining data from INS and EMT systems using a tracking system state estimator (TSS) that implements a Kalman filter to merge state data from both sources, correcting INS drift and reducing tracking errors by integrating EMT data to improve position and orientation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If inertial navigation systems are used for tracking moving platforms, then autonomous navigation capability is improved, but integration drift causes position and orientation errors to accumulate over time
Solution Approach 1:
The patent combines inertial navigation system (INS) data with electromagnetic tracking system (EMT) data through a tracking system state estimator using Kalman filtering. This merging of two independent tracking systems allows the INS autonomous capability to be maintained while the EMT provides periodic corrections to prevent drift accumulation, thereby resolving the contradiction between automation and measurement precision.
2Measurement precision
If electromagnetic tracking systems use narrow beam antennas to locate moving platforms, then location precision is improved, but multipath effects cause signal intensity variations and tracking errors
Solution Approach 1:
The patent implements a feedback mechanism where the Kalman filter continuously processes EMT signal data and compares it with INS predictions. When multipath effects cause signal variations, the filter detects these anomalies and weights the data appropriately, using the INS data to compensate for EMT errors and vice versa, thereby maintaining tracking accuracy despite multipath interference.
3Measurement precision
If data from both INS and EMT systems is combined using Kalman filtering, then tracking accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a tracking system state estimator as an intermediary component that implements Kalman filtering to process and fuse data from both INS and EMT systems. This intermediary handles the complex mathematical operations of combining heterogeneous data sources, transforming the complex multi-source fusion problem into a standardized filtering process that manages complexity while maximizing tracking accuracy.
Data Source
AI summary
A method for tracking a moving platform (MP) wherein the MP uses an on-board navigation system (NS). Data provided by the navigation system on board the moving platform (MP) is merged with data obtained using a tracking system that tracks the MP from another location. A typical navigation system on board the moving platforms is an inertial navigation system (INS). State data of one or more MP is sent to a processing facility and state data of one or more electromagnetic tracking (EMT) is collected by one or more processing facility. The collected states data from the sources are processed, using the one or more processing facilities for calculating tracking data are used to direct one or more antennas for MP tracking. The state data from one or more MP's are sent using a communications channel.