The backward processed inertial path calculates position by processing inertial data in reverse, from the endpoint to the starting point. This method uses the same raw inertial measurements but in reverse chronological order. The system treats these inputs as if they were recorded in forward time. Drift still accumulates, but it develops in the opposite direction compared to forward processing. This reversed drift pattern offers a valuable comparison for error analysis.
Backward processing becomes effective when GNSS data is reacquired after a blackout. It uses the known endpoint as a reference. By propagating inertial data backward, the system can estimate a new trajectory. This method provides a second solution that complements the forward path. Engineers often compare both paths to detect and reduce drift.
The system does not need to recognize that it processes data in reverse. It applies standard inertial algorithms to the inverted dataset. The accuracy of the backward path relies on the quality of the endpoint position. If this point is well-constrained by GNSS or another aid, backward processed inertial path yields valuable corrections.
Backward processing works best in post-processing environments. Real-time systems cannot reverse data during operation. This approach suits GNSS-aided inertial navigation systems where signal loss is temporary. It helps bridge outages more accurately than forward-only solutions.
For example, when a vehicle exits a tunnel, the system can reprocess data backward from the tunnel’s end. The resulting backward path counteracts the accumulated drift from the forward solution. Overlaying both paths reveals systematic errors and enhances trajectory estimation.
This dual-direction approach improves position accuracy and strengthens system reliability. In harsh conditions or GNSS-denied areas, backward processed inertial path helps refine navigation performance. It represents an advanced tool in the post-processing workflow of modern inertial navigation systems.
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