Forward processed inertial path describes how an inertial navigation system calculates position using real-time data in one direction. The system collects acceleration and angular velocity from inertial sensors and integrates this data over time. It uses this information to estimate position, velocity, and orientation as the vehicle moves.
During forward processing, the system starts from a known position and calculates motion forward in time. It assumes no prior knowledge of the endpoint. As time passes, small sensor errors and noise accumulate, resulting in drift. This drift grows over time and can reduce position accuracy, especially without GNSS corrections.
In GNSS-aided systems, the forward processed path stays accurate when satellite signals are available. GNSS updates correct the accumulated drift and reset the navigation solution. However, in tunnels or urban canyons, GNSS signals may degrade or disappear. During such outages, the system continues processing forward, but errors grow without correction.
Despite drift, the forward path still records dynamic movements accurately. It captures changes in velocity and orientation with high resolution. The system can detect maneuvers, turns, or lane changes even when absolute position is uncertain. These details remain useful for post-processing or merging with other data.
Forward processing is essential in real-time applications like autonomous navigation or live vehicle tracking. It delivers immediate motion estimates, helping systems respond to environmental changes. But because errors increase over time, the forward path alone is not always sufficient for long missions without GNSS.
To improve accuracy, users often apply backward processing in post-mission analysis. By comparing forward and backward solutions, they reduce overall drift. However, forward processing forms the foundation for real-time navigation performance. It ensures systems remain functional even in GNSS-denied zones. In summary, the forward processed inertial path provides real-time navigation data but requires corrections to minimize accumulated drift.
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